[diss] Sähkötekniikan korkeakoulu / ELEC

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Fabrication and characterization of two-dimensional material based devices for photonics and electronics
(Aalto University, 2024) Uddin, MD Gius; Ahmed, Faisal, Dr., Aalto University, Department of Electronics and Nanoengineering, Finland; Elektroniikan ja nanotekniikan laitos; Department of Electronics and Nanoengineering; Photonics Group; Sähkötekniikan korkeakoulu; School of Electrical Engineering; Sun, Zhipei, Prof., Aalto University, Department of Electronics and Nanoengineering, Finland
This thesis explores the potential of two-dimensional (2D) materials in different practical applications and presents the results divided in three parts. The first part focuses on the miniaturized spectrometers. Unlike conventional tabletop spectrometers, we demonstrate miniaturized (~22×8 μm2) computational spectrometers that rely on the electrically tunable spectral response of 2D materials-based single-junction for spectral reconstruction. We achieve high peak wavelength accuracy (~3 nm) and a broad operation window covering the visible and the near-infrared regions, indicating the great potential of the spectrometers to enable numerous portable applications. The second part of this thesis examines different strategies for tuning the optical and electrical properties of 2D materials. We demonstrate that morphological manipulation of 2D indium selenide (InSe) facilitates enhanced light-matter interaction in InSe. Our 2D InSe/1D nanowire heterostructures, exhibit more than 5 times enhanced optical responses compared to that from bare InSe. Moreover, signiﬁcant optical anisotropy is observed that makes our mixed-dimensional heterostructures a good candidate for diverse polarization-dependent optoelectronic applications such as photodetectors. Further, in this thesis, we explore a strain engineering approach to increase the charge carrier mobility of molybdenum ditelluride (MoTe₂) ﬁeld-eﬀect transistors (FETs). It involves the creation of hole arrays in the substrate, transfer of MoTe2 ﬂakes on the hole arrays, and subsequent deposition of ALD Al2O3 passivation layer on top of the MoTe2 ﬂakes. We achieve ~6 times higher charge carrier mobility in the strained MoTe2 FETs than those MoTe2 FETs without strain. The results offer a bright prospect to realize 2D materials-based high-performance devices for future electronics. In the final part of this thesis, we experimentally demonstrate a novel concept for the miniaturization of broadband light sources. Coherent broadband light is generated (via difference-frequency generation) for the ﬁrst time with gallium selenide and niobium oxide diiodide crystals at the deep-subwavelength thickness (<100 nm). The broadband spectrum spans more than an octave (from ~565 to 1906 nm) without the need for dispersion engineering. Compared with conventional methods, our demonstration is ~5 orders of magnitude thinner and requires ~3 orders of magnitude lower excitation power. The results open a new path to create ultra-compact, on-chip broadband light sources.
Hilbert Space Projection Methods for Numerical Integration and State Estimation
(Aalto University, 2024) Sarmavuori, Juha; Särkkä, Simo, Prof., Aalto University, Department of Electrical Engineering and Automation, Finland; Sähkötekniikan ja automaation laitos; Department of Electrical Engineering and Automation; Sensor Informatics and Medical Technology; Sähkötekniikan korkeakoulu; School of Electrical Engineering; Särkkä, Simo, Prof., Aalto University, Department of Electrical Engineering and Automation, Finland
The aim of this thesis is to develop Hilbert space methods for approximation of integrals appearing in filtering and smoothing of nonlinear state-space models. State-space models have many applications in real-world problems and have been studied extensively for almost a century. In filtering, the state is estimated at a given time instant based on measurements up to the time instant. In smoothing, measurements after the given time instant are used as well. The used state-space models are stochastic and hence need to be estimated in probabilistic terms, which requires solving probability integrals. We consider two kinds of state-space models: discrete-time and continuous-discrete-time ones. In the latter case, the dynamics model is continuous time the measurements are obtained in discrete time instants. In linear state-space models with additive Gaussian noise, closed-form solutions are known for both filtering and smoothing problems. In a nonlinear case, we can use Gaussian approximations, which means that we approximate the probability distributions with Gaussian distributions. We study how to use Fourier–Hermite series for smoothing and filtering with Gaussian approximations. For computing terms of the Fourier–Hermite series, we develop a new method that uses partial differentials of a Weierstrass transform of a nonlinear function. Even with the simplifying Gaussian approximation, in general, we cannot solve the resulting Gaussian integrals in closed form, but we need numerical approximations instead. We develop a new numerical integration method based on an approximation of a multiplication operator with a finite matrix, and it is not only applicable to Gaussian integrals but can be used for more general numerical integration. This new numerical integration method generalises Gaussian quadrature and has many similar properties, which are analysed using the theory of linear operators in Hilbert space. Specifically, we prove convergence for a large class of functions. In the case of independent variables, it is possible to compute multidimensional integrals by product rule of unidimensional numerical integrals. With the new numerical integration method, we can generalise the product rule for non-independent variables. We apply this generalised product rule to filtering with arbitrary order moments.
Enhancing Latency Reduction and Reliability for Internet Services with QUIC and WebRTC
(Aalto University, 2024) Li, Xuebing; Cho, Byungjin, Dr., Nokia, Finland; Informaatio- ja tietoliikennetekniikan laitos; Department of Information and Communications Engineering; Mobile Cloud Computing; Sähkötekniikan korkeakoulu; School of Electrical Engineering; Xiao, Yu, Prof., Aalto University, Department of Information and Communications Engineering, Finland
This dissertation introduces innovative systems and algorithms aimed at improving latency reduction and enhancing reliability for Internet services. To structure the research effectively, the dissertation categorizes the lifecycle of a service connection into three stages: service discovery, delivery, and migration. In the context of service discovery and migration, edge computing employs server replication to achieve low latency via user proximity and high reliability through load balancing. The primary challenges lie in developing a mechanism for rapid connection establishment and consistent optimal end-user mapping. A focal point of this study is the examination of QUIC and its potential to enhance service discovery and migration. By integrating QUIC's handshake data flow with conventional service discovery protocols, two systems are proposed to reduce the latency in connection setup and enhance the effectiveness of end-user mapping, with a particular emphasis on leveraging anycast and Domain Name System (DNS) technologies. For anycast, a novel approach in cloud computing is introduced to anycast routing, incorporating enhanced capabilities for name resolution and load awareness. In DNS, a middleware solution in the 5G core network improves performance, notably in query delay, cache hit rates, and consistency, thereby refining DNS-based discovery in edge cloud computing. Furthermore, along with essential server-side modifications, the solution extends QUIC's zero-round trip time (0-RTT) handshake feature to facilitate 0-RTT service migration, significantly boosting migration efficiency. Regarding service delivery, the data generation and transmission behavior is governed by the system and network capabilities. The challenge resides in designing a control algorithm to ensure consistent low-latency and reliable packet delivery, facilitating the application requirements. This dissertation concentrates on optimizing delivery control mechanisms within real-time video streaming, using WebRTC as the testbed. It provides an exhaustive analysis of how control parameters affect streaming performance and application metrics, leading to the development of an algorithm for optimizing the parameter setting during the slow start phase of congestion control. Furthermore, a machine learning based streaming control solution is proposed to jointly control multiple parameters, serving as a more general solution. This work also introduces an open-source framework designed to facilitate future research on applying machine learning to WebRTC control. Aiming to improve latency and reliability, this dissertation investigates the integration of emerging technologies such as QUIC, 5G, and machine learning within the established framework of edge computing. This research emphasizes the importance of cross-layer design in the optimization of Internet services, identifying machine learning as a promising approach.
Spatial audio signal processing for passive sonar applications
(Aalto University, 2024) Bountourakis, Vasileios; Pulkki, Ville, Prof., Aalto University, Department of Information and Communications Engineering, Finland; Informaatio- ja tietoliikennetekniikan laitos; Department of Information and Communications Engineering; Communication Acoustics Group; Sähkötekniikan korkeakoulu; School of Electrical Engineering; Pulkki, Ville, Prof., Aalto University, Department of Information and Communications Engineering, Finland
This thesis consists of five publications that focus on the development and evaluation of techniques designed for passive sonar applications utilising hydrophone arrays. The research specifically explores the topic of underwater soundfield visualisation on the horizontal plane for bearing estimation and tracking of sound-emitting targets. The explored techniques draw their inspiration from established microphone array techniques, widely adopted in the field of spatial audio, presenting novel approaches to traditional localisation problems in the underwater domain. In particular, the first publication proposes a novel spatial post-filter in the circular harmonic domain suitable for application in large circular hydrophone arrays, similar to those found in modern submarines. This proposed post-filter is essentially an extension of the Cross-Pattern Coherence (CroPaC) post-filter to higher orders of circular harmonics. The second and third publications concern the development of a space-domain version of CroPaC, i.e., a version that operates directly on the hydrophone array signals, eliminating the need for conversion into the circular harmonic domain. This aspect holds particular significance for passive sonar, as it enables the application of CroPaC to linear arrays, which are the predominant type of arrays used in underwater acoustics. The fourth publication proposes a novel approach for underwater soundfield visualisation using circular hydrophone arrays. The proposed approach is inspired by the soundfield analysis performed in Higher-Order Directional Audio Coding (HO-DirAC), a parametric spatial audio technique which extracts spatial parameters from directionally constrained regions termed sectors. Finally, the fifth publication proposes the use of an optimal mass transport framework for bearing estimation and tracking of underwater targets, achieved by solving a convex optimisation problem. The evaluation of the techniques was conducted using hydrophone array data obtained from highly detailed numerical simulations as well as real-world hydrophone array recordings. The examined array types include linear arrays with both baffled and open designs, as well as circular arrays mounted on cylindrical baffles. In the majority of cases, the array designs were chosen to align with specifications of arrays commonly used in passive sonar operations with submarines. The performance of the proposed techniques demonstrated significant improvements over conventional passive sonar techniques in many cases. These improvements pertain to the accuracy of bearing estimation, the side-lobe suppression capability, the separation of closely spaced targets, the computational complexity, and the robustness to noise, interference, and model mismatch. Lastly, it is noted that, while the evaluation primarily focused on specific hydrophone arrays of special interest deployed in shallow-water environments, the results have broader applicability and may therefore be generalised to other use cases.
Balancing privacy and utility of smart devices utilizing explicit and implicit context
(Aalto University, 2024) Zuo, Si; Informaatio- ja tietoliikennetekniikan laitos; Department of Information and Communications Engineering; Aalto Ambient Intelligence group; Sähkötekniikan korkeakoulu; School of Electrical Engineering; Sigg, Stephan, Prof., Aalto University, Department of Information and Communications Engineering, Finland
The swift evolution of communication technologies, coupled with advancements in sensors and machine learning, has significantly accelerated the pervasive integration of smart Internet of Things (IoT) devices into various aspects of our daily lives. Examples range from automating homes to optimizing industrial processes and improving healthcare. While these applications enhance quality of life and operational efficiency, they also raise concerns about user privacy due to the collection and processing of personal data. Ensuring the seamless and secure integration of these technologies is crucial. Balancing the benefits of smart applications with protecting user privacy is the key challenge. To address this issue, we present a general method as well as customized approaches for specific scenarios. The general method involves data synthesis, which safeguards privacy by substituting real data with synthetic data. We propose an unsupervised statistical feature-guided diffusion model (SF-DM) for sensor data synthesis. SF-DM generates diverse and representative synthetic sensor data without the need for labeled data. Specifically, statistical features such as mean, standard deviation, Z-score, and skewness are introduced to guide the sensor data generation. Regarding customized approaches for specific scenarios, we address both active (explicit context) and passive (implicit context) situations. Explicit context typically includes information willingly shared while implicit context may encompass data collected passively, with users potentially unaware of the full extent of information usage. Segregating explicit and implicit context aims for a balance between personalization and privacy, empowering users with enhanced control over their information and ensuring adherence to privacy regulations. In active scenarios, we focus on privacy protection in pervasive surveillance. We propose Point-Former, the example-guided modification of motion in point cloud to translate from default motion and gesture interaction alphabets to personal ones, to safeguard privacy during gesture interactions in pervasive space. In the passive scenario involving implicit context, we consider on-body devices and environmental devices. For on-body devices, we introduce \textbf{CardioID}, an interaction-free device pairing method that generates body-implicit secure keys by exploiting the randomness in the heart's operation (electrocardiogram (ECG) or ballistocardiogram (BCG) signals). For environmental smart devices, we propose GIHNET, a low complexity and secure GAN-based information hiding method for IoT communication via an insecure channel. It hides the original information using meaningless representations, by obscuring it beyond recognition. Building on GIHNET, we extend the use of data encryption and propose SIGN, which converts signatures into a Hanko pattern and uses it as an encryption method to generate digital signatures in pervasive spaces.
Generalized Accelerated Optimization Framework for Big Data Processing
(Aalto University, 2024) Dosti, Endrit; Charalambous, Themistoklis, Prof., University of Cyprus, Cyprus; Informaatio- ja tietoliikennetekniikan laitos; Department of Information and Communications Engineering; Sähkötekniikan korkeakoulu; School of Electrical Engineering; Vorobyov, Sergiy A., Prof., Aalto University, Department of Information and Communications Engineering, Finland
Large-scale optimization problems arise in different fields of engineering and science. Due to the large number of parameters and different structures that these problems can have black-box first-order methods are widely used in solving them. Among the existing firstorder methods, the ones that are most widely used are different variants of Fast Gradient Methods (FGM). Such methods are devised in the context of the estimating sequences framework and exhibit desirable properties such as fast convergence rate and low per iteration complexity. In this Thesis, we devise new estimating sequences and show that they can be used to construct accelerated first-order methods. We start by considering the simplest case, i.e., minimizing smooth and convex objective functions. For this class of problems, we present a class of generalized estimating sequences, constructed by exploiting the history of the estimating functions that are obtained during the minimization process. Using these generalized estimating sequences, we devise a new accelerated gradient method and prove that it converges to a tolerated neighborhood of the optimal solution faster than FGM and other first-order methods. We then consider a more general class of optimization problems, namely composite objectives. For this class of problems, we introduce the class of composite estimating sequences, which are obtained by making use of the gradient mapping framework and a tight lower bound on the function that should be minimized. Using these composite estimating sequences, we devise a composite objective accelerated multi-step estimating sequence technique and prove its accelerated convergence rate. Last, embedding the memory term coming from the previous iterates into the composite estimating sequences, we obtain the generalized composite estimating sequences. Using these estimating sequences, we construct another accelerated gradient method and prove its accelerated convergence rate. The methods devised for solving composite objective functions that we introduce in this thesis are also equipped with efficient backtracking line-search strategies, which enable more accurate estimates of the step-size. Our results are validated by a large number of computational experiments on different types of loss functions, wherein both simulated and publicly available real-world datasets are considered. Our numerical experiments also highlight the robustness of our newly introduced methods to the usage of inexact values for of the Lipschitz constant and the strong convexity parameter.
Auditory-model-based assessment of the effect of head-worn devices on sound localisation
(Aalto University, 2024) Lladó, Pedro; Hyvärinen, Petteri, DSc., Aalto University, Department of Information and Communications Engineering, Finland; Informaatio- ja tietoliikennetekniikan laitos; Department of Information and Communications Engineering; Communication Acoustics; Sähkötekniikan korkeakoulu; School of Electrical Engineering; Pulkki, Ville, Prof., Aalto University, Department of Information and Communications Engineering, Finland
Head-worn devices (HWDs), e.g. headphones, head-mounted displays or helmets, inherently introduce acoustic distortions to the sound reaching the ear canals, potentially degrading the localisation abilities of the listener. These distortions pose potential risks to safety, hinder spatial awareness, and may affect immersion in augmented reality applications. Traditional methods for assessing the degradation in localisation due to HWDs rely on listening experiments, which are time-consuming and require specific facilities. Consequently, alternative approaches are sought, particularly in the prototyping and development phases of HWDs. This thesis investigates the feasibility of utilising acoustic measurements and auditory models to estimate the degradation in localisation caused by HWDs. We examine the efficacy of existing static localisation models in predicting experimental data when HWDs are worn. These models demonstrate robustness in their predictions, despite their initial validation under open-ear conditions only. Furthermore, we propose two auditory models tailored to estimating degradation in localisation with HWDs. The first model combines a peripheral processing front-end with a shallow neural network that estimates perceived localisation from frequency-dependent interaural cues. The second model extends an existing static localisation model based on Bayesian inference to accommodate voluntary head rotations in an auditory-aided visual search task. We validate these models with experimental data and provide publicly available implementations. Our contributions aim to enhance automatic assessment tools for HWD quality by leveraging advancements in sound localisation research. The performance of these models is robust for unseen listening conditions, highlighting the importance of integrating evidence from hearing research into assessment methodologies. This motivates the need for ongoing fundamental research in sound localisation and the development of auditory models that incorporate such findings, with the overarching goal of enhancing the quality of spatial audio applications.
Computationally efficient statistical inference in Markovian models
(Aalto University, 2024) Corenflos, Adrien; Särkkä, Simo, Prof., Aalto University, Department of Electrical Engineering and Automation, Finland; Sähkötekniikan ja automaation laitos; Department of Electrical Engineering and Automation; Sensor Informatics and Medical Technology; Sähkötekniikan korkeakoulu; School of Electrical Engineering; Särkkä, Simo, Prof., Aalto University, Department of Electrical Engineering and Automation, Finland
Markovian systems are ubiquitous in nature, science, and engineering, to model the evolution of a system for which the future state of the system only depends on the past through the present state. These often appear as time series or stochastic processes, and when they are partially observed, they are known under the umbrella term of state-space models. Inferring the current state of the system from these partial, and often noisy, observations is a fundamental question in statistics and machine learning, and it is often solved using Bayesian inference methods that correct a prior belief on the state of the system through the likelihood of the observations. This perspective gives rise to typically recursive algorithms, which sequentially process the observations to slowly refine the estimate of the current state of the system. The most common of these algorithms are the Kalman filter and its extensions via linearisation procedures, and particle filtering methods, based on Monte Carlo. Another question, which often arises is that of the past state or past trajectory of the system, given all the observations. Furthermore, it may also be of interest to identify the model itself, whereby the most likely (or any other metric) model within a family is picked given the observations. In this thesis, we examine the three problems of Bayesian filtering, smoothing, and identification in the context of Markovian models, and we propose computationally efficient algorithms to solve them. In particular, we develop the parallelisation of the recursive structure of the filteringsmoothing algorithms, which, while optimal in a sequential setting, can be significantly sped up by using modern parallel computing architectures. This endeavour is tackled in both the context of particle approximations and Kalman-related methods. Another important aspect of the thesis is the use of gradient-based methods to perform inference in state-space models, taking several forms. One of these is the generalisation of the Metropolis-adjusted Langevin algorithm (MALA) and related algorithms to the context of particle and Kalman filters, and their implication for high-dimensional state inference. Another one is making particle filters differentiable by approximating the usual algorithm and then using the approximation to perform inference in statespace models using gradient-based methods. Finally, we also discuss the use of gradient-flows to perform automatic locally optimal filtering in state-space models. Some of these algorithms are de facto sequential and hardly parallelisable, but some instances can benefit from parallelisation, and we discuss the implications of this in terms of computational efficiency.
Infrastructureless unmanned aerial vehicle localization
(Aalto University, 2024) Kinnari, Jouko; Verdoja, Francesco, Academy Research Fellow, Aalto University, Department of Electrical Engineering and Automation, Finland; Sähkötekniikan ja automaation laitos; Department of Electrical Engineering and Automation; Intelligent Robotics; Sähkötekniikan korkeakoulu; School of Electrical Engineering; Kyrki, Ville, Prof., Aalto University, Department of Electrical Engineering and Automation, Finland
The ability to localize, i.e., determine the position and orientation of a Unmanned Aerial Vehicle (UAV) with respect to a known frame of reference, is a basic requirement for autonomous flight. Common solutions for providing a UAV with localization ability have relied on the availability of an infrastructure built for this purpose, usually based on an arrangement of radio emitters, predominantly Global Navigation Satellite Systems (GNSSs). However, disruptions in the radio signal path, as well as actions taken by an adversary, such as spoofing and jamming, may hinder localization accuracy. This thesis focuses on UAV localization, in environments lacking infrastructure for that purpose, specifically using a low-size, weight and power (SWaP) sensor system consisting of a camera, an Inertial Measurement Unit (IMU), and a magnetometer. The challenges limiting this approach are associated with the difficulty of relating UAV environment measurements to a map, due to not only differences between the appearance of the map representation and the environment as observed using onboard sensors, but also natural ambiguities such as perceptual aliasing. This thesis addresses three specific problem areas and demonstrates a full localization solution running in real time on a small UAV. First, the thesis addresses the problem of how to perform localization with respect to an orthophoto map using a camera whose orientation is not strictly vertical. A method is presented for allowing variation in camera view direction by orthoprojecting camera images to a top-down view based on a planar assumption of the ground under the UAV. This would be an adequate assumption when flying over relatively flat terrain, as demonstrated through experimentation on real data. Second, this thesis addresses the problem of seasonal appearance change, where we learn a function for assessing the correspondence between an image acquired by an UAV and an orthophoto map by proposing a method that is tolerant to seasonal appearance change in the operating environment. The proposed method exceeds the state-of-the-art in the literature both in terms of the time to convergence and localization error. Third, this work addresses the wake-up robot problem. For this purpose, an approach is presented for learning a model to extract a compact descriptor vector representation from both a UAV image and from a map, thus enabling very fast confirmation or rejection of pose hypotheses, which allows localization to occur over large areas without knowledge of the initial pose. The presented method alleviates the computational challenges inherent in the problem of localization over a large area with an unknown prior starting position and orientation. The method also enables operation of a small UAV on a map covering an area of 100 square kilometers without requiring knowledge of the initial pose while tolerating seasonal appearance change and resolving ambiguities due to perceptual aliasing. Finally, the operation of the algorithm developed for the wake-up robot problem running on a small UAV is demonstrated in real time using real data. The thesis concludes by characterizing a number of open issues related to the problem domain.
Reinforcement Learning Methods for Setpoint Optimization and Control Method Design in Process Industry with Case Studies in Steel Strip Rolling and District Heating
(Aalto University, 2024) Deng, Jifei; Sierla, Seppo, Dr., Aalto University, Department of Electrical Engineering and Automation, Finland; Sun, Jie, Prof., Northeastern University, China; Sähkötekniikan ja automaation laitos; Department of Electrical Engineering and Automation; Information Technologies in Industrial Automation; Sähkötekniikan korkeakoulu; School of Electrical Engineering; Vyatkin, Valeriy, Prof., Aalto University, Department of Electrical Engineering and Automation, Finland
Process industry necessitates precise control and monitoring for operational efficiency, safety, and productivity. Traditional approaches, such as first-principles models, empirical models, and trial-and-error methods, have been utilized, often involving simplification and linearization to address the intricate and dynamic nature of industrial processes. However, to enhance product quality and energy efficiency, there is a growing demand for intelligent and adaptive methodologies to compute optimal solutions for industrial processes. One significant challenge lies in the realm of setpoint optimization, where precise computation of equipment parameters to align with quality specifications is paramount. In the domain of process control, achieving high-quality products relies on the implementation of feedback control methods. However, devising adaptive control methodologies capable of dynamically responding to evolving conditions poses a substantial challenge. Recognizing the potential of reinforcement learning (RL) to learn from interactions, RL techniques have been adopted to learn policies for setpoint optimization and process control. In the context of setpoint optimization in strip rolling and fuel cost reduction in district heating, RL methodologies have been investigated to calculate and optimize setpoints for the systems. Leveraging environment models of the processes, RL agents generate optimal solutions based on machine capacity to meet customer demands. Furthermore, RL-based adaptive control methodologies have been developed for the steel strip rolling process, enabling dynamic responses to evolving conditions. To make the RL-based control policy more accurate and practical for industrial processes, an offline RL method that learns control policies directly from the data has been proposed to address biases originating from approximated environment models that impact the accuracy. Steel strip rolling and district heating have been selected to evaluate the efficacy of RL-based methods in addressing setpoint optimization and process control challenges. The results indicate that the proposed methods outperform the traditional approaches, marking substantial advancements in automation, optimization, and control methodologies within the process industry.
Microwave quantum communications: new approaches to sensing and mitigation of the bosonic pure-loss channel
(Aalto University, 2024) Khalifa, Hany; Paraoanu, Sorin, Dr., Aalto University, Department of Applied Physics, Finland; Informaatio- ja tietoliikennetekniikan laitos; Department of Information and Communications Engineering; Sähkötekniikan korkeakoulu; School of Electrical Engineering; Jäntti, Riku, Prof., Aalto University, Department of Information and Communications Engineering, Finland
With the current availability of microwave quantum technologies, it is imperative to investigate the different methods and techniques that would enhance the performance of currently existing microwave communication systems. There are two particular areas of interest that are considered in this thesis: (1) quantum microwave sensing in the presence of extreme additive white Gaussian noise, and (2) the imperfect propagation and storage of bosonic modes inside lossy transmission media. Due to the small signal powers in the microwave domain, the task of finding the most efficient detection method for the completion of the aforementioned tasks while maintaining the quantum advantage is complicated. In this thesis, novel methods and techniques are proposed that ease the experimental requirements for microwave quantum technologies. The thesis comprizes four main publications that summarize the research investigation. Publications I and II consider the problem of physically realizing microwave quantum illumination without the need for ideal single-photon counters. Firstly, publication I studies the effect of the excess noise and losses induced by the environment on the utilized signal-idler pair. Then, publication II provides a novel solution, a CNOT (controlled not) gate quantum illumination receiver that achieves an optimal performance set for a quantum illumination receiver without the need for single-photon counters. In publications III and IV, the focus is on devising new strategies to mitigate the losses experienced by microwave bosonic modes during propagation or storage. The objective here is to adapt the concept of noiseless linear amplification, earlier demonstrated in the optical domain, to the microwave region. Despite the persistent problem of microwave detection, the novel one-way noiseless linear amplifier based on quantum non-demolition detectors managed to outperform a conventional one based on microwave photon counters. Furthermore, it also offered an uninterrupted performance due to its fault tolerance which could not be replicated by a conventional noiseless linear amplifier. Finally, publication IV considers several future applications of one-way noiseless linear amplifiers in sensing, remote entanglement sharing and secret key generation, where the device demonstrated in this thesis is able to outperform any other conventional noiseless linear amplifier.
Plasma-enhanced chemical vapor deposition of carbon nanofibers: correlations between process parameters and physicochemical properties
(Aalto University, 2024) Pande, Ishan; Laurila, Tomi, Prof., Aalto University, Department of Electrical Engineering and Automation, Finland; Sähkötekniikan ja automaation laitos; Department of Electrical Engineering and Automation; Microsystems Technology; Sähkötekniikan korkeakoulu; School of Electrical Engineering; Laurila, Tomi, Prof., Aalto University, Department of Electrical Engineering and Automation, Finland
Carbon nanofibers (CNFs) possess versatile physicochemical properties, making them pivotal in advancing technology, particularly in electrochemical sensing due to their high conductivity, large surface area, and broad potential window. Plasma-enhanced chemical vapor deposition (PECVD) is commonly employed for CNF synthesis, facilitating the growth of vertically aligned fibers at low temperatures. This intricate process involves adjusting parameters such as temperature, gas ratio, and plasma power to tailor fiber morphology and surface chemistry. Catalyst and adhesive layer selection further impacts these properties, while growth time serves as an additional tunable parameter. Despite extensive documentation of CNF growth via PECVD, systematic investigations into key aspects are lacking. For instance, the influence of the adhesive layer on CNF morphology, surface chemistry, and electrochemical performance remains unexplored. Similarly, the dual role of NH3 as both etchant and dopant is often overlooked. Moreover, discussions on CNF applications rarely justify process parameter selection or explore potential enhancements through parameter adjustments. The aim of this work is to systematically assess (i) the effects of material choices and selected process parameters on the micro- and macroscale morphology, surface chemistry, and doping of CNFs, and (ii) the implications of these effects on their electrochemical characteristics. Two research hypotheses guide the work done in this thesis, namely, (I) the choice of adhesive layer significantly influences the morphology, surface chemistry, and electrochemical performance of CNFs grown via PECVD, and (II) alternating between H2 and NH3 as etchant gases during CNF growth alters both micro- and macroscale morphology, impacting electroanalytical properties. Our key findings confirm our hypotheses: (i) CNF morphology, surface chemistry, and electrochemical properties depend on the adhesive layer, (ii) CNF macroscale geometry affects pseudocapacitance without significantly impacting electron transfer kinetics, (iii) precise control of CNF morphology enhances selectivity and sensitivity towards our probe molecule dopamine, and (iv) altering etchant gases between H2 and NH3 significantly alters CNF micro- and macroscale morphology, resulting in notable changes in electrochemical properties, and (v) the ratio of the etchant and feedstock gases influences the doping level, morphology and electrochemical characteristics of CNFs. Overall, our results demonstrate the importance of carefully selecting the process parameters in the CNF growth process, as the choice has a marked effect on the doping, morphology, surface chemistry and electrochemical performance of the CNFs. By demonstrating that the electroanalytical performance of CNF electrodes can be tailored by this approach, this work provides a robust foundation for designing CNF electrodes for a wide variety of applications.
Meaning in a Wider Sense - From Conversational Interaction Technologies to Patient Engagement and Experience Design for Digital Health
(Aalto University, 2024) Boda, Péter Pál; Informaatio- ja tietoliikennetekniikan laitos; Department of Information and Communications Engineering; Sähkötekniikan korkeakoulu; School of Electrical Engineering; Alku, Paavo, Prof., Aalto University, Department of Information and Communications Engineering, Finland
Healthcare has gone through explosive changes in the past few decades. While digitalisation has introduced innovative solutions, such as electronic health records and health data interoperability, healthcare systems are increasingly strained by the continuous growth of patients with chronic diseases, the aging population, the rising costs, and the shortage of staff. To alleviate, at least partially, these pain points, new care models have emerged with the patient in the center of the care and emphasis on the delivered value and outcomes. These approaches heavily rely on data that must look wider and deeper, beyond the patient's medical condition only. With the help of these multimodal data points, healthcare can view individuals more as whole-persons than as patients only, thus helping shared decision making, providing better care, and ultimately, obtaining better outcomes at lower cost. Due to the ubiquitous availability of advanced digital health solutions, including digital medicine and therapeutics, today's care teams are able to access and collect patient-specific markers that correlate with patients' health and well-being, such as the socioeconomic status, lived social environment, health behaviour, lifestyle choices, and physical activity. The data can be acquired from various sources, including patients' own reports, remote monitoring, wearables, or other health applications. A central motivation of this thesis is to dive into the underlying enablers of digital health solutions and to examine how advanced interaction with seamless patient experience can be provided. The above topic is studied in the first part of the thesis from the point of view of basic research by focusing on artificial intelligence (AI) and machine learning (ML) based interaction technologies, as well as efficient modelling of spoken dialogue and multimodal interfaces. The second, applied research part of the thesis examines digital health from the point of view of experience design, patient experience, and meaningful engagement. The thesis exhibits several examples for interaction solutions with improved multimodal integration and evaluation methods. Furthermore, the work on user research and design-driven discovery of parental engagement is presented, as well as a multimodal journaling solution built for parents of premature babies based on the results of the design research phase. Finally, the thesis synthesises all the results through the relations of patient engagement, experience and empowerment, and presents a framework for computational care continuum powered by digital health solutions as enablers.
Exploiting distributed energy resources with a virtual power plant : Intelligent market participation based on forecasts
(Aalto University, 2024) Subramanya, Rakshith; Sierla, Seppo, Dr., Aalto University, Department of Electrical Engineering and Automation, Finland; Sähkötekniikan ja automaation laitos; Department of Electrical Engineering and Automation; Research group of Information Technologies in Industrial Automation; Sähkötekniikan korkeakoulu; School of Electrical Engineering; Vyatkin, Valeriy, Prof., Aalto University, Department of Electrical Engineering and Automation, Finland
Virtual power plants (VPPs) are a promising solution for integrating renewable energy sources, battery energy storage, and smart loads into the modern power grid. They offer an alternative to traditional centralized power generation, which is often based on fossil fuel or nuclear power, and a key characteristic of a VPP is the profitable exploitation of the distributed energy resources that it manages. This is done by trading the capacity provided by these renewable energy resources on various electricity markets. To ensure the stability of the power grid, Frequency reserve markets are used, and VPPs, especially in Northern Europe, aggregate and trade DERs on such frequency reserve markets. The industrial informatics aspects of VPPs involve coordinating a pool of intelligent Distributed Energy Resources (DERs), predicting market prices using Artificial Intelligence (AI), and developing industrial informatics architectures for VPPs in the AI era. AI is utilized to analyze extensive datasets of historical data like electricity markets or DER capacity to discern patterns and trends. This information is then leveraged to forecast future demand and supply, aiding VPPs in optimizing their operations. Similarly, with the frequency reserve market forecasts, a VPP can make better decisions about allocating resources and participating in energy markets. This dissertation explores the integration of VPPs with DERs using various industry standards. For the optimal operation and profitability of the VPPs, DER capacity and reserve market forecasting are performed and integrated into VPPs. Also, reinforcement Learning is employed for the reserve market bidding. All the proposed architectural components, such as VPP, forecasting, and DER integration, are implemented on the cloud for seamless operation. Also, a multi-tenant architecture is proposed to implement the scalability of DER integration and various Software as a Service (SaaS) integrations like forecasting to a VPP. Building continuous software engineering practices is one of the main challenges in machine learning (ML) applications. For this purpose, this work also introduces Machine Learning and Operation (MLOps) and Cloud Design Patterns (CDPs) in the context of VPP. This research contributes to realizing a more efficient, resilient, and environmentally friendly energy system by addressing the challenges of DER integration with VPP, market participation, forecasting, and cloudification of a VPP with all the sub-systems. The dissertation begins by presenting the related work in the field, establishing the context for the proposed system. Four use cases define and explain the functional and non-functional system requirements and their implementation in detail. At last, the results are presented with conclusions.
Electrochemistry and Surface Properties of Nanostructured Carbon Electrodes and Interfaces
(Aalto University, 2024) Kousar, Ayesha; Laurila, Tomi, Prof., Aalto University, Department of Electrical Engineering and Automation, Finland; Sähkötekniikan ja automaation laitos; Department of Electrical Engineering and Automation; Microsystems Technology; Sähkötekniikan korkeakoulu; School of Electrical Engineering; Laurila, Tomi, Prof., Aalto University, Department of Electrical Engineering and Automation, Finland
Over the past two decades, carbon nanomaterials have received significant attention in health technologies, due to their impressive electroanalytical properties, especially in neurotransmitter detection. The death of dopaminergic neurons and resulting lower levels of dopamine (DA) severely disrup the body's motor functions, making life extremely difficult for Parkinson's patients. Electrochemical carbon sensors offer the potential to detect DA levels effectively in the brain. However, biofouling of electrode surfaces, causing surface passivation, along with a lack of sensitivity and selectivity in DA sensors, continues to hinder growth in the field of DA electrochemical sensors. Despite the extensive literature available on various approaches to tackle these challenges, such as employing multimaterial coatings on electrode surfaces and applying hemical treatments, there remains a lack of thorough understanding regarding their effectiveness. This necessitates the importance of developing methods to regulate the performance of electrochemical sensors by modulating the geometric properties of materials during fabrication and comprehensively studying the cause-and-effect relationships. This dissertation demonstrates the significance of modifying material assembly and structure to control associated electroanalytical properties. The role of surface nanostructures and interfaces towards carbon nanofiber (CNF) electrochemistry is studied, with DA as a case study due to its relevance in neurodegenerative disease treatment. To attain selectivity of neurotransmitter detection, adsorption of the analyte is crucial. However, reducing electrode fouling is also essential for optimal DA sensor performance. This dissertation demonstrates that achieving such balance is possible by modifying the nanoscale structure of carbon nanomaterials to promote favorable adsorption of target molecules, while optimizing the macroscopic geometry of the electrode to mitigate excessive fouling. CNF electrodes with increased fiber lengths are demonstrated to enhance electrochemical sensitivity and selectivity against common interferents by increasing adsorption sites for target molecules. Moreover, breaking the planar geometry of carbon electrodes and introducing macroscopic geometries is shown to reduce biofouling and electrochemical fouling susceptibility. It is demonstrated that commonly used adhesion layers in the fabrication of CNF, such as Cr and Ti, exhibit different carbon segregation dynamics upon annealing, affecting electrochemical activity. Subsequently, the presence of Ni seed layer alters these dynamics, favoring ordered graphitic carbon segregation and improving electrochemical properties. Nevertheless, based on the results presented in the dissertation, it is argued that i) modifying nanoscale and macroscopic geometries and (ii) systematic evaluation of the electroactivity of often overlooked electrode components are crucial for designing biosensors with optimized performance.
Integrated Optoelectronics with van der Waals Materials
(Aalto University, 2024) Cui, Xiaoqi; Elektroniikan ja nanotekniikan laitos; Department of Electronics and Nanoengineering; Photonics Group; Sähkötekniikan korkeakoulu; School of Electrical Engineering; Sun, Zhipei, Prof., Aalto University, Department of Electronics and Nanoengineering, Finland
This thesis studies the novel optoelectronic operation principles and functions with vdW material integrated devices. The main study outputs include optically configurable AAT/Bi-AAT photoresponses, a reconstructive spectroscopic material identification based on a semiconductor/metal heterostructure, and a nanostructured InSe waveguide photodetector. The first part of this thesis provides an overview of the vdW material integrated optoelectronics. The second part introduces the basis of vdW materials, and the principles of photodetection studied in this thesis. The advanced vdW material integrated optoelectronic devices are reviewed, especially the devices on the optical waveguide platform. The third part reviews the microfabrication techniques utilised in this work, and the characterisation methods such as electrical and optoelectronic measurement setups. The fourth part includes an optically configurable AAT/bi-AAT photoresponse. The AAT behaviour results from charge trapping and detrapping processes, assisted by manually introduced trap state. A symmetric device configuration is employed to achieve the AAT photoresponse. Two sources of carrier detrapping lead to wavelength-dependent AAT/bi-AAT photoresponses. The demonstration introduces optical wavelength as a trigger for advanced photoresponses. The fifth part presents a computational reconstructive wavelength meter, leveraging a configurable semiconductor/metal heterostructure that has both VDS and VGS tunable wavelength-dependent photoresponses. The feature of high peak-wavelength accuracy is further conducted for complex spectrum inputs for material identification. The sixth part investigates two optoelectronic devices with the optical waveguide platform, including the reuse of the reconstructive wavelength meter, and a nanostructured InSe waveguide photodetector that enables light direct insertion and out-of-plane dipole-assisted light absorption. Finally, the studies in this thesis are summarised and concluded. The possible optimisations and outlook are presented.
Transfer-Plausible Acoustics for Augmented Reality
(Aalto University, 2024) Meyer-Kahlen, Nils; Schlecht, Sebastian J., Prof., Friedrich-Alexander Universität Erlangen-Nürnberg, Germany; Robinson, Philip, Dr., Reality Labs Research, USA; Informaatio- ja tietoliikennetekniikan laitos; Department of Information and Communications Engineering; Virtual Acoustics Group; Sähkötekniikan korkeakoulu; School of Electrical Engineering; Lokki, Tapio, Prof., Aalto University, Department of Information and Communications Engineering, Finland
Augmented reality (AR) telepresence systems aim to present visual and auditory "holograms" of conversation partners via head-mounted displays and transparent headphones. These systems require binaural audio that adapts not only to the user's orientation and position but also to their acoustic environment. Many fundamental technologies for such real-time, binaural auralization systems have been developed over the years. These virtual acoustic systems were often tested in direct comparison to a high-quality reference rendering, so the implied objective for the system's development was often indistinguishability from a reference. However, differences were usually audible in such tests, at least for non-ideal, practically relevant systems. When developing future AR systems, two questions arise: "Why exactly do such discrepancies occur?" and "What are meaningful objectives and evaluation paradigms other than indistinguishability from a reference?" First, finding reasons for discrepancies involves a detailed understanding of specific rendering methods, underlying models, and their violations. Two fundamental properties of a parametric spatial room impulse response processing technique are studied as examples. Second, as an objective that leads to meaningful AR evaluation paradigms, one option is to assess if auditory illusions are evoked, i.e., whether a listener believes a virtual sound source to be real. This work introduces the transfer-plausibility paradigm, which evaluates if a virtual source creates an auditory illusion, even in the presence of other, real sound sources. In summary, Publication I and Publication II discuss fundamental properties of spatial room impulse response processing techniques: Publication I shows how direction-of-arrival estimation based on the pseudo intensity vector depends on anisotropy in the late reverberation. Publication II investigates how perceptual roughness can occur in spatial room impulse response rendering based on broadband directional assignment. Publication III and Publication IV deal with problems more closely related to AR. Publication III proposes an approach for blind spatial room impulse response estimation using a pseudo-reference signal. Publication IV demonstrates auditory modeling-based quantification of impairments caused by so-called transparent headphones used for AR. Publication V and Publication VI introduce the notion of transfer-plausibility and compare it against other paradigms. The results suggest that even non-ideal virtual acoustic renderings are comparable in transfer-plausibility tests. Publication VII presents an experiment about the inability for self-localization using position-dependent room acoustic differences. The thesis concludes by presenting opportunities for future transfer-plausibility tests and a proposed model for describing differences in experimental paradigms by their sensitivity to auditory similarity, context, and artifacts.
Data-driven room-acoustic modelling
(Aalto University, 2024) Götz, Georg; Schlecht, Sebastian J., Prof., Department of Information and Communications Engineering, Aalto University, Finland; Informaatio- ja tietoliikennetekniikan laitos; Department of Information and Communications Engineering; Communication Acoustics; Sähkötekniikan korkeakoulu; School of Electrical Engineering; Pulkki, Ville, Prof., Aalto University, Department of Information and Communications Engineering, Finland
The study of room acoustics has traditionally been of interest in architectural planning and design. With the spread of virtual- and augmented-reality technology, room-acoustic modelling has also become increasingly relevant for audio engines. The dynamic and fast-paced nature of such applications requires rendering systems to operate in real-time. However, accurate state-of-theart room-acoustic-simulation technology is often computationally expensive, limiting its use for audio engines. Data-driven methods offer the potential to bypass expensive simulations, while ensuring convincing perceptual experiences. This dissertation works towards data-driven audio engines by exploring the interaction between room-acoustic modelling and data-driven methods. It comprises five peer-reviewed publications that investigate automatic data acquisition, robust room-acoustic analysis in complex environments, and data-driven room-acoustics rendering. As sound propagates through a room, it interacts with various surfaces, leading to a gradual energy decay over time. The properties of this energy decay significantly influence the acoustic impression evoked by a room, making it a widely studied topic in room-acoustic research. The first part of this thesis provides an overview of sound-energy decay, its analysis, and challenges associated with complex geometries featuring multiple rooms and non-uniform absorption-material distributions. To this end, it introduces a neural network for multi-exponential sound-energy-decay analysis. Moreover, spatial and directional variations of sound-energy decay are investigated, and a compact representation to model them is proposed. The second part of this thesis is centred around data-driven methods and explores how they can be applied to room-acoustics research. After elaborating on the properties of room-acoustic data, techniques for its large-scale acquisition are investigated. Two of the contained publications describe autonomous robot systems for conducting room-acoustic measurements. While the first one describes the general idea and the design constraints of a practical system, the second one extends the measurement strategy to complex geometries featuring multiple connected rooms. An overview of commonly used machine-learning concepts is provided, focusing on the ones relevant for the included publications. Finally, several applications of data-driven methods in roomacoustics research are described, including a summary of a late-reverberation rendering system proposed in one of the appended publications.
Photovoltaic Hosting Capacity of Distribution Networks
(Aalto University, 2024) Püvi, Verner; Lehtonen, Matti, Prof., Aalto University, Department of Electrical Engineering and Automation, Finland; Sähkötekniikan ja automaation laitos; Department of Electrical Engineering and Automation; Power Systems and High Voltage Engineering; Sähkötekniikan korkeakoulu; School of Electrical Engineering; Lehtonen, Matti, Prof., Aalto University, Department of Electrical Engineering and Automation, Finland
Due to an increasing share of renewable energy sources and widespread adoption of distributed photovoltaics (PV), the ability of the distribution networks to reliably interconnect new PV installations without hindering the power system's operation gained a lot of interest in the recent decade. Political support and falling prices of the photovoltaics make the panels available for as small as single household installations and some of the network operators have already reported the power quality issues caused by the PVs. To address this issue, this thesis investigates the PV hosting capacity (HC) of low-voltage distribution networks. The thesis is split into two main contributions centered around the power quality limitations of the HC and network structure influence on the HC. The thesis starts with a review of the HC definitions, its most common limiting factors, and reports on the results of a measurement campaign of low-voltage substations. In the first part, a Monte Carlo-based HC evaluation methodology is presented, which is used for the PV-only and energy storage-augmented scenarios. Alongside the single-phase PV installations, a voltage unbalance (VU) mitigation methodology is presented. Despite the VU being a very strict limit, it can be mitigated by relatively low power injections. Moreover, a comparison of PV curtailment and network reinforcement is presented to find the break-even points of the costs of the two. The second part of the thesis presents the distribution network's structure impact on the hosting capacity. A fixed set of customers is simulated with multiple feeding substations and a varying number of PV plants. The slime mold algorithm was proposed to be employed for generating numerous network topologies and its advantages over other algorithms were shown. The results revealed that around one-third of the customers can have PV installations until the HC is depleted. Voltage control can increase the HC, however remains the risk of possible need to change residential PV policies to sustain the current pace of PV installations. Finally, the thesis explores the practical side of the HC and analyzes the accuracy of distribution network state estimation. A PV safety margin is proposed, that represents an equivalent PV power that has to be curtailed in order to keep the estimated values below the actual values of the states.
Investigations and Applications of Angle-Resolved Measurements of Spectral Reflectance and Transmittance
(Aalto University, 2024) Aschan, Robin; Manoocheri, Farshid, PhD, Aalto University, Department of Information and Communications Engineering, Finland; Sähkötekniikan ja automaation laitos; Department of Electrical Engineering and Automation; Metrology Research Institute; Sähkötekniikan korkeakoulu; School of Electrical Engineering; Ikonen, Erkki, Prof., Aalto University, Department of Electrical Engineering and Automation, Finland
This doctoral thesis explores the quantification of a material's appearance through angle-resolved measurements of spectral reflectance and transmittance. The appearance of a material often determines the quality of a product across various sectors of industry, entertainment, and research. Therefore, there is a need for accurately characterized reference standards, traceable to the international system of units (SI). Accordingly, this thesis focuses on bidirectional reflectance distribution function (BRDF) and bidirectional transmittance distribution function (BTDF) measurements of diffuse material in the visible and near-infrared wavelength range. Moreover, the thesis presents the development of an SI-traceable facility for the accurate determination of angle-resolved scattering quantities. One application of BRDF includes the verification of proper operation of ray tracing models (RTM) using calibrated reference standards. The first part of the thesis introduces an SI-traceable 3D gonioreflectometer for BRDF measurements of an artificial sample. To find a suitable sample for the verification process, the 3D instrument systematically measured several material candidates, evaluating their BRDF characteristics. Subsequently, the instrument's uncertainty in BRDF measurements is characterized, and the reliability of RTM is checked by comparing simulated and measured BRDF data of an artificial sample based on the best material candidate. The following sections of the thesis are dedicated to exploring the practical applications of BTDF. The sections present the development of a facility for SI-traceable BTDF measurements, featuring an absolute gonioreflectometer extended to BTDF measurements in the visible and near-infrared wavelength range. The instrument specializes in measurements of matte surfaces that uniformly transmit light in all directions. Validation of the absolute instrument's capabilities in BTDF measurements was conducted through a detailed uncertainty evaluation and also a comparison measurement with a commercial spectrophotometer. Furthermore, the thesis discusses the adaptation of the 3D instrument for out-of-plane measurements of BTDF, recognizing the utility of materials that have anisotropic scattering distributions. The fourth part of the thesis offers considerations on the definition of BTDF in the context of thick sample measurement. The thesis presents a method for correcting measured BTDF as a function of viewing zenith angle based on the geometry of the facility. Furthermore, the method was validated by using two instruments exhibiting varying degrees of sensitivity to sample thickness. This work provides tools for unambiguous treatment of the BTDF of thick samples.

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