Noise pollution from road traffic is one of the greatest environmental issues in modern day, and the social cost for road traffic noise was estimated to over 16 billion SEK per year in Sweden in2014. Passive or active control methods can be used to reduce the noise. Active control methods or active noise control is more suitable for attenuating noise in lower frequencies. Active noise control reduces noise by eliminating the noise with a secondary source. There are different control strategies to construct an active noise control system, where the update of the secondary sourceis controlled by an algorithm. There are several different algorithms that are possible to use, and one option is to use a Feedforward Filtered-X Least-Mean-Square (FXLMS) algorithm. It uses control positions where the noise is meant to be reduced and reference signals that measure the noise upstream prior the secondary source. FXLMS also uses a model of the secondary source path to the control position in order to ensure convergence of the algorithm. Although the use of multiple reference signals increases the accuracy of the algorithm, it also increases the convergence time and the practical cost of such an installation. Unfortunately, it can require many reference signals to obtain a sufficient noise reduction when the unwanted noise source is complex and has multiple propagation paths.This study investigates the possibility of producing a new, reduced set of reference signals with a linear combination of the original reference signals that still contain the majority of information needed for suficient noise reduction. This new set of reference signals are sometimes called virtual reference signals. Three different methods of virtual reference signals are analysed; first a constant method using singular-value decomposition on the covariance of the reference signals, second another constant method using singular-value decomposition on the covariance of response estimate from each corresponding reference signal, third an adaptive algorithm updating the linear combination to adapt for incoming data. The different strategies are tested on road test measurements at three different constant speeds, 40km=h; 80km=h and 120km=h, and on data generated from a numerical vehicle model in COMSOL.The results from the analysis indicates that the virtual reference signals could sufficiently reproduce information from the original reference signals to obtain a similar noise reduction with fewer reference signals. However, the virtual reference signals with the adaptive algorithm could not manage to track a transient system where the signal amplitudes are varying over time. Further work is needed to analyse the limits and requirements to obtain virtual reference signals that can represent and track a system even for transient events.

<p dir="ltr">In Global Navigation Satellite Systems Reflectometry (GNSS-R) a critical theme is in un-<br>derstanding and delving into determining specular points, and how to optimize its solutions.<br>The implications are significant for soil moisture, Snow Water Equivalent (SWE), water stor-<br>age, and climate dynamics. For instance, the Signals of Opportunity in P-Band Investigation<br>(SNOOPI) will utilize observations in reference to the specular point to evaluate measurements<br>that could be used to determine water content, soil moisture, and SWE. The focus of this<br>study is presenting and evaluating two prominent methods for determining specular points:<br>the Minimum Path Delay (MPL) and the Unit Difference (UD). Specular point determin-<br>nation presents various challenges which include: surface roughness, temporal and spatial<br>variability, and multipath effects. All of these earth’s surface characteristics pose a challenge<br>for scientists and engineers who wish to collect terrestrial parameters. The analysis in this<br>study offers a comparative approach focusing on data from the simulator for the CubeSat<br>SNOOPI mission is to evaluate specular point determination accuracy as well as offer a real-<br>world application to determine the efficacy of the two methods. Through this evaluation,<br>the researcher aims to improve specular point determination techniques used in the GNSS-R<br>community and offer insights into future techniques and how they can support each other<br>for more precise results.<br></p>

Chiplets have become an increasingly popular technology for extending Moore's Law and improving the reliability of integrated circuits. They do this by placing several small, interacting chips on an interposer rather than the traditional, single chip used for a device. Like any other type of integrated circuit, chiplets are in need of a physical layer of security to defend against hardware Trojans, counterfeiting, probing, and other methods of tampering and physical attacks. Power distribution networks are ubiquitous across chiplet and monolithic ICs, and are essential to the function of the device. Thus, we propose a method of fingerprinting transient signals within the PDN to identify individual chiplet systems and physical-layer threats against these devices. In this work, we describe a Python-wrapped HSPICE model we have built to automate testing of our proposed PDN fingerprinting methods. We also document the methods of analysis used- wavelet transforms and time-domain measurements- to identify unique characteristics in the voltage response signals to transient stimuli. We provide the true positive and false positive rates of these methods for a simulated lineup of chips across varying operating conditions to determine uniqueness and reliability of our techniques. Our simulations show that, if characterized at varying supply voltage and temperature conditions in the factory, and the sensors used for identification meet the sample rates and voltage resolutions used in our tests, our protocol provides sufficient uniqueness and reliability to be enrolled. We recommend that experimentation be done to evaluate our methods in hardware and implement sensing techniques to meet the requirements shown in this work.

The Vagus nerve has shown to play an important role regarding inflammatory diseases, regulating the production of proteins that mediate inflammation. Two important such proteins are the pro-inflammatory cytokines, TNF and IL-1β. This thesis makes use of Vagus nerve recordings, where TNF and IL-1β are subsequently injected in mice, with the aim to see if cytokine-specific information can be extracted. To this end, a type of semi-supervised learning approach is applied, where the observed waveform-data are modeled using a conditional probability distribution. The conditioning is done based on an estimate of how often each observed waveform occurs and local maxima of the conditional distribution are interpreted as candidate-waveforms to encode cytokine information. The methodology yields varying, but promising results. The occurrence of several candidate waveforms are found to increase substantially after exposure to cytokine. Difficulties obtaining coherent results are discussed, as well as different approaches for future work. / Vagusnerven har visat sig spela en viktig roll beträffande inflammatoriska sjukdomar. Denna nerv reglerar produktionen av inflammatoriska protein, som de inflammationsfrämjande cytokinerna TNF och IL-1β. Detta arbete använder sig av elektroniska mätningar av Vagusnerven i möss som under tiden blir injicerade med de två cytokinerna TNF och IL-1β. Syftet med arbetet är att undersöka om det är möjligt att extrahera information om de specifika cytokinerna från Vagusnervmätningarna. För att uppnå detta designar vi en semi-vägledd lärandemetod som modellerar dem observerade vågformerna med en betingad sannolikhetsfunktion. Betingandet baseras på en uppskattning av hur ofta varje enskild vågform förekommer och lokala maximum av den betingade sannolikhetsfunktionen tolkas som möjliga kandidat-vågformer att innehålla cytokin-information. Metodiken ger varierande, men lovande resultat. Förekomsten av flertalet kandidat-vågformer har en tydlig ökning efter tidpunkten för cytokin-injektion. Vidare så diskuteras svårigheter i att uppnå konsekventa resultat för alla mätningar, samt olika möjligheter för framtida arbete inom området.

User-centered computer based learning is an emerging field of interdisciplinary research. Research in diverse areas such as psychology, computer science, neuroscience and signal processing is making contributions the promise to take this field to the next level. Learning systems built using contributions from these fields could be used in actual training and education instead of just laboratory proof-of-concept. One of the important advances in this research is the detection and assessment of the cognitive and emotional state of the learner using such systems. This capability moves development beyond the use of traditional user performance metrics to include system intelligence measures that are based on current neuroscience theories. These advances are of paramount importance in the success and wide spread use of learning systems that are automated and intelligent. Emotion is considered an important aspect of how learning occurs, and yet estimating it and making adaptive adjustments are not part of most learning systems. In this research we focus on one specific aspect of constructing an adaptive and intelligent learning system, that is, estimation of the emotion of the learner as he/she is using the automated training system. The challenge starts with the definition of the emotion and the utility of it in human life. The next challenge is to measure the co-varying factors of the emotions in a non-invasive way, and find consistent features from these measures that are valid across wide population. In this research we use four physiological sensors that are non-invasive, and establish a methodology of utilizing the data from these sensors using different signal processing tools. A validated set of visual stimuli used worldwide in the research of emotion and attention, called International Affective Picture System (IAPS), is used. A dataset is collected from the sensors in an experiment designed to elicit emotions from these validated visual stimuli. We describe a novel wavelet method to calculate hemispheric asymmetry metric using electroencephalography data. This method is tested against typically used power spectral density method. We show overall improvement in accuracy in classifying specific emotions using the novel method. We also show distinctions between different discrete emotions from the autonomic nervous system activity using electrocardiography, electrodermal activity and pupil diameter changes. Findings from different features from these sensors are used to give guidelines to use each of the individual sensors in the adaptive learning environment.

Root-zone soil moisture (RZSM) is an important variable when forecasting plant growth, determining water availability during drought, and understanding evapotranspiration as a flux. However, current methods indirectly estimate RZSM using data assimilation, which requires time-series data to make model-based predictions. This is because direct measurement requires a lower frequency signal, typically P-band and below (<500MHz), to reach root zone depths and, in turn, necessitates a larger antenna to be deployed in space, which is often unfeasible. A new remote sensing technique known as Signals of Opportunity (SoOp) reutilizes transmitted communication signals to perform microwave remote sensing. This means that SoOp platforms need not include a transmitter, but rather rely on passive radar technology to make measurements. This thesis details the development of a UAS-based P-band SoOp receiver instrument. This platform will be used to progress the state-of-art in techniques for direct measurement of RZSM.

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[pt] Aplicações de Realidade Virtual (VR) com rastreamento de movimentos da cabeça precisam de efeitos de espacialização de alta qualidade. A abordagem tradicional para RV/jogos (interpolação dos canais L+R para construção do estereo) se mostrou insuficiente por ser incapaz de simular a acústica do mundo real. Por isso a pesquisa na área tem migrado para espacialização 3D do áudio. O receptor tem a sensação de que o som veio de um local no espaço 3D. Em outras palavras, ele pode localizar o emissor apenas pelo áudio por consequência permite a construção ambientes mais imersivos e coerentes quando usados em conjunto de técnicas visuáis. Nesse novo contexto, motores de jogos devem prover aos designers de áudio uma gama de ferramentas especializadas para a espacialização de àudio 3D além as de uso geral, que encluem: reverberações e reflexões usadas na construção de ambientes como igrejas e cavernas (locais com ecos); modulação, para criar variações de frequência e aliviar na repetitividade de sons recorrentes (como os de passos e tiros); mix e fade de volumes, utilizado na criação de momentos dramáticos na história e reprodução musical. Nesse trabalho, nós propomos um motor de áudio de tempo real para espacialização de fontes sonoras pontuais em ambientes virtuais. Vai possuir uma arquitetura documentada e de código aberto que provê um conjunto de efeitos e a habilidade de os compor. Nós implementamos a espacialização de áudio em 3D sobre bancos de dados de respostas impulsionais da cabeça (HRIRs) e efeitos sonoros com técnicas de processamento digital de sinais (DSP). Apesar da existência de sistemas comerciais poderosos de áudio para VR estejam disponíveis (e.g. Oculus), nosso protótipo pode ser uma alternativa se a simplicidade, testabilidade e ajustes forem levados em conta. / [en] Virtual Reality (VR) applications with low-latency head tracking require high-quality spatial audio effects. However, classic VR/game sound approaches cannot properly simulate the acoustic of the real world. Current audio research is moving towards 3D spatial audio to have a more realistic simulation. In 3D spatial audio, the listener has the sensation that sound comes from a particular direction in 3D space. In other words, the listener can localize a source based on audio and have a more coherent and immersive experience when paired with visual simulation. In this new context, game engines should provide sound designers with a set of 3D spatial audio tools. The following common effects are desirable in this type of toolbox: reverberations and reflections, which can be employed in the creation of caverns or churches (places with lots of echoes); modulation, which can increase the perceived variety of a recorded sound, by slightly varying its pitch (as in the sounds of footsteps); mixing and fading volumes, which can create dramatic moments in storytelling and music reproduction. In this work, we propose a realtime audio engine to spatialize sound point sources in virtual environments. This engine is an open-source architecture that provides a basic set of audio effects and an efficient way to mix and match them. We implement 3D audio spatialization by leveraging recorded head-related impulse responses (HRIRs) and we produce special sound effects with digital signal processing (DSP) techniques. Although some powerful commercial audio SDKs for Virtual Reality are currently available (e.g. Oculus), our audio engine prototype may be a flexible option when adaptation, simplification, testing, and parameter tuning are necessary.

Lathes are frequently used in industrial production. It is an important parameter to calculate the force exerted by the cutter head on the raw material. Knowing the force acting on the cutting tool will aid in predicting the displacement of the cutting edge as well as predict displacement of the workpiece. The main purpose of this paper is to find a way to calculate the force exerted on the cutting tool tip. The magnitude of the tooltip force is estimated without the use of a force sensor. Instead, strain sensors are used to collect strain signals, and acceleration sensors are used to collect acceleration signals. Combine these two signals to calculate the magnitude of force. The force-strain frequency response function is calculated. The force-strain FRF acts as a bridge connecting force and strain signals. Calculate the input force signal on the cutting tool tip using the strain signal. In this way, is available to obtain the force time-image. Changes in force can be predicted by looking for force-time laws. In this thesis using MATLAB software for simulation and actual experimental measurements. Verify the reliability of the calculation method. The method and MATLAB code for calculating force-strain FRF are researched and written. Simulate the cutting tool input-output model in MATLAB. Combined with the actual experimental measurement results, the accuracy and limitations of this calculation method are analyzed. Discuss directions for future work.