Electromagnetically driven dosing pumps are suitable for metering any kind of liquid in motor vehicles in a precise manner. Due to the working principle and the pump design, an undesired noise occurs when the armature reaches the mechanical end stops. The noise can be reduced by an adequate self-learning control of the supply energy using a position estimation and velocity control. Based on preliminary investigations /1/, a method for noise reduction is realised by using a user-friendly, tiny and cost-efficient hardware, which enables a use in series manufacturing. The method requires only a voltage and current measurement as input signals. The core of the hardware is an 8-bit microcontroller with 8 kilobytes flash memory including necessary peripherals. A smart software development enables an implementation of the entire noise reduction method onto the tiny flash memory.

Development of thin films has allowed for important improvements in optical, electronic and electromechanical devices within micrometer length scales. In order to grow thin films, there exist a wide variety of deposition techniques, as each technique offers a unique set of advantages. The main challenge of thin film deposition is to reach smallest possible dimensions, while achieving mechanical stability during operating conditions (including extreme temperatures and external forces, complex film structures and device configurations). Silicon carbide (SiC) is attractive for its resistance to harsh environments, and the potential it offers to improve performance in several microelectronic, micro-electromechanical, and optoelectronic applications. The challenge is to overcome presence of high defect densities within structure of SiC while it is grown as a crystalline thin film. For this reason is important to monitor levels of residual stress, inherited from such grown defects, and which can risk the mechanical stability of SiC- made thin film devices. Stoney's equation is the theoretical foundation of the curvature method for measuring thin film residual stress. It connects residual film stress with substrate curvature through thin plates bending mechanics. Important assumptions and vii simplifications are made about the film-substrate system material properties, dimensions and loading conditions; however, accuracy is reduced upon applying such simplifications. In recent studies of cubic SiC growth, certain Stoney's equation assumptions are violated in order to obtain approximate values of residual stress average. Furthermore, several studies have proposed to expand the scope of Stoney's equation utility; however, such expansions demand of more extensive substrate deflection measurements to be made, before and after film deposition. The goal of this work is to improve the analysis of substrate deflection data, obtained by mechanical profilometry, which is a simple and inexpensive technique. Scatter in deflection data complicates the use of simple processes such as direct differentiation or polynomial fitting. One proposed method is total variation regularization of differentiation process; and results are promising for the adaptation of mechanical profilometry for complete measurement of all components of non-uniform substrate curvature.

This report is the result of a Master’s Degree Thesis done at the Royal Institute of Technology, KTH, during October 2014 to April 2015. The project was commissioned by Lidl in collaboration with Stockholms Stad and Integrated Transport Research Labs at KTH, and is a part of the Off-peak project. The Aim of the project was to help Lidl perform quieter street unloadings during off-peak hours. The unloading, today, is done with electrical pedestrian trucks and roll containers. Information was gathered about the current equipment and the current method of unloading was studied through observations. There were three problematic areas identified: the uneven ground, the hardware and the user. These were studied further on a pedestrian truck which was borrowed from Lidl for the thesis work. A market study was performed to see what solutions existed on the market. Information was also gathered on Off-peak projects in other cities and what the benefits of such projects could be. Study visits were made to Toyota-BT in Mjölby and Karnag in Täby to gather further knowledge. A meeting was held with K.Hartwall at Integrated Transport Research Lab to discuss their current solutions. The three problematic areas were discussed during a midterm meeting. Two of them were selected for future work: an add-on module to keep the fork carriage from rattling and an aid for the drivers of the trucks that shows the noise level. Many iterations of brainstorming, testing and evaluating led to the final results. The testing showed that the module that was supposed to keep the fork carriage from rattling didn’t have the desired results. The choice was then made to develop a spring solution to keep the forks from bouncing of each other. This led to the development of a leaf spring solution that is mounted on the underside of the fork carriage. The development of an aid for the drivers led to the Noise Advisor which is a sound level meter that sits on the side of the trucks mast and gives visual feedback to the driver. / Denna rapport är resultatet av ett examensarbete utfört på Kungliga Tekniska Högskolan, KTH, från oktober 2014 till april 2015. Projektets uppdragsgivare var Lidl och det utfördes i samarbete med Stockholms Stad och Integrated Transport Research Labs på KTH, som en del av Off-peak projektet. Målet med projektet var att hjälpa Lidl utföra tystare gatulossningar under off-peak tider. Lossningen idag sker med hjälp av elektriska ledtruckar samt rullburar. En bakgrundsstudie genomfördes där den nuvarande utrustningen och metoden för gatulossning studerades genom observationer på plats. Då identifierades tre problemområden: den ojämna marken, utrusningen och användaren. Dessa studerades vidare på en ledtruck och några rullburar som lånades in från Lidl. En marknadsundersökning utfördes för att se vilka lösningar som redan fanns på marknaden. Andra off-peak projekt i andra städer studerades och vilka fördelar de medförde. Studiebesök gjordes på Toyota-BT i Mjölby och Karnag i Täby för att samla ytterligare kunskap. Ett möte hölls med K.Hartwall på Integrated Transport Research Lab för att diskutera deras nuvarande lösningar. De tre problemområdena diskuterades under ett avstämmningsmöte som hölls i mitten av projektet. Då valdes två av dessa områden: en tilläggsmodul som ska motverka att gaffeln skramlar och en dosa som ger feedback till förare om ljudnivån när hen kör den. Många iterationer av brainstorming, tester och utvärderingar ledde till det slutliga resultatet. Testen visade att påbyggnadsmodulen, som var tänkt att förhindra gaffeln från att skramla, inte gav något bra resultatet. Då gjordes valet att utveckla en lösning som med hjälp av en fjäder förhindrar att gafflarna slår mot varandra. Detta ledde till utvecklingen av en lösning med en bladfjäder som monteras på undersidan av gaffeln. Utvecklingen av ett hjälpmedel till förarna ledde till Noise advisor som är en ljudnivåmätare som sitter på sidan av ledtrucken och ger en visuell feedback till föraren.

In recent years, reconfigurable devices are developing fast because of its flexibility and less development cost. But intrinsic shortcomings of reconfigurable devices, for example, high power, low speed, etc. induce difficulties in complex designs realizations. So people began to consider combination of ASIC (Application-Specific Integrated Circuit) and reconfigurable device on a single chip, which is SOPC (System on Programmable Chip). SOPC can not only decrease development risk and timing to market, but also be used in different applications, especially of products that keep varying, for example, communication and network products. Dynamically reconfiguration means reconfigurable device of the chip can be reconfigured repeatable, and performs different functions at different times. Compared with static reconfiguration, dynamic reconfiguration can use the reconfigurable device more thoroughly. It‟s a hot spot of research in the world, especially in reconfigurable computing. This paper mainly concludes my research work in reconfigurable SOPC in 3 major parts: hardware, software and application. The following works and innovations are completed: 1. SOPC hardware system architecture design and discussion. Helps to define the system architecture and design goals. The design of EBI controller which is used in the SOPC. The integration of the blocks in the system. 2. The building-up of the SOPC system-level verification and block-level verification environment. The set-up of the hardware-software co-simulation environment. The post-layout simulation and formal verification tasks. We propose an innovative automated regression system. The system helps to achieve the same simulation coverage (95%) and the total simulation time is reduced by approximately 30%. 3. SOPC software design, including the OS kernel porting, drivers design and application design. The design of the PowerPC initialization program and UART (Universal Asynchronous Receiver/Transmitter), reconfiguring communication driver programs. Writing the test-cases which are specialized for the system verification and hardware testing. 4. Being the co-designer of the novel bus macro based on the FDP reconfigurable logic core. And we realize the whole reconfigurable system based on this bus macro. 5. The reconfigurable application research based on Reconfigurable Logic Core. The reconfigurable image filter designed implemented on FDP300K Reconfigurable Logic Core device. Using self-design Reconfigurable Logic Core internal bus macro to implement the partial reconfigurable system. The test results showed that the reconfigurable filter has the feature of fast configuration speed and good output image quality.

With MEG, the tiny magnetic fields produced by neuronal currents within the brain can be measured completely non-invasively. But the signals are very small (~100 fT) and often obscured by spontaneous brain activity and external noise. So, a recurrent issue in MEG data analysis is the identification and elimination of this unwanted interference within the recordings. Various strategies exist to meet this purpose. In this thesis, two of these strategies are scrutinized in detail. The first is the commonly used procedure of averaging over trials which is a successfully applied data reduction method in many neurocognitive studies. However, the brain does not always respond identically to repeated stimuli, so averaging can eliminate valuable information. Alternative approaches aiming at single trial analysis are difficult to realize and many of them focus on temporal patterns. Here, a compromise involving random subaveraging of trials and repeated source localization is presented. A simulation study with numerous examples demonstrates the applicability of the new method. As a result, inferences about the generators of single trials can be drawn which allows deeper insight into neuronal processes of the human brain. The second technique examined in this thesis is a preprocessing tool termed Signal Space Separation (SSS). It is widely used for preprocessing of MEG data, including noise reduction by suppression of external interference, as well as movement correction. Here, the mathematical principles of the SSS series expansion and the rules for its application are investigated. The most important mathematical precondition is a source-free sensor space. Using three data sets, the influence of a violation of this convergence criterion on source localization accuracy is demonstrated. The analysis reveals that the SSS method works reliably, even when the convergence criterion is not fully obeyed. This leads to utilizing the SSS method for the transformation of MEG data to virtual sensors on the scalp surface. Having MEG data directly on the individual scalp surface would alleviate sensor space analysis across subjects and comparability with EEG. A comparison study of the transformation results obtained with SSS and those produced by inverse and subsequent forward computation is performed. It shows strong dependence on the relative position of sources and sensors. In addition, the latter approach yields superior results for the intended purpose of data transformation.

The question “who infected whom” is a perennial one in the study of infectious disease dynamics. To understand characteristics of infectious diseases such as how many people will one case produce over the course of infection (the reproductive number), how much time between the infection of two connected cases (the generation interval), and what factors are associated with transmission, one must ascertain who infected whom. The current best practices for linking cases are contact investigations and pathogen whole genome sequencing (WGS). However, these data sources cannot perfectly link cases, are expensive to obtain, and are often not available for all cases in a study. This lack of discriminatory data limits the use of established methods in many existing infectious disease datasets. We developed a method to estimate the relative probability of direct transmission between any two infectious disease cases. We used a subset of cases that have pathogen WGS or contact investigation data to train a model and then used demographic, spatial, clinical, and temporal data to predict the relative transmission probabilities for all case-pairs using a simple machine learning algorithm called naive Bayes. We adapted existing methods to estimate the reproductive number and generation interval to use these probabilities. Finally, we explored the associations between various covariates and transmission and how they related to the associations between covariates and pathogen genetic relatedness. We applied these methods to a tuberculosis outbreak in Hamburg, Germany and to surveillance data in Massachusetts, USA. Through simulations we found that our estimated transmission probabilities accurately classified pairs as links and nonlinks and were able to accurately estimate the reproductive number and the generation interval. We also found that the association between covariates and genetic relatedness captures the direction but not absolute magnitude of the association between covariates and transmission, but the bias was improved by using effect estimates from the naive Bayes algorithm. The methods developed in this dissertation can be used to explore transmission dynamics and estimate infectious disease parameters in established datasets where this was not previously feasible because of a lack of highly discriminatory information, and therefore expand our understanding of many infectious diseases.

Supersonic jet exhaust plumes produce noise in jet engines, which has been a problem in the aerospace field. Researchers are working on ways to reduce this turbulent mixing noise, with little modification to the engine and nozzle. Prior work has shown that total temperature non-uniformity is a noise reduction technique which introduces a stream of cold flow into the heated jet. This method has been shown to cause changes in the exhaust plume and result in a 2±0.5 dB reduction of peak sound pressure levels. The goal of this work is to reveal underlying changes in the spatial-temporal structure of plume instability and turbulence caused by non-uniform total temperature distributions. Studies have demonstrated several methods of jet noise reduction by modifying the turbulent mixing in the exhaust plume. Large-scale turbulent structures have been shown to be the dominant source of noise in heated supersonic jets, especially over long, streamwise distances. Therefore, a large field-of-view measurement is desirable for studying these structures. Time-Resolved Doppler Global Velocimetry (TR-DGV) with a sampling frequency of 50 kHz is used to collect flow velocity data that is resolved in both time and space. The experiments for data collection were performed on a heated supersonic jet at the Virginia Tech Advanced Propulsion and Power Laboratory. A converging-diverging nozzle with a diameter Reynolds number of 850,000 was used to generate a perfectly expanded, heated flow of Mach 1.5 and a nozzle pressure ratio (NPR) of 3.67. The unheated plume was introduced at the center of the nozzle, with a total temperature ratio (TTR) of 2. Comparison of the mean velocity fields shows that the introduction of the cooler temperature flow in the thermally non-uniform case results in a velocity deficit of about 10% compared to the thermally uniform case. The method of spectral proper orthogonal decomposition (SPOD) was used to reveal the large-scale, coherent noise producing mechanisms. SPOD results indicate that the thermally non-uniform case showed a decrease in turbulent kinetic energy compared to the uniform case at all frequencies. Coherent fluctuations start developing further upstream in the thermally non-uniform case. The addition of the unheated plume results in a disruption in the propagation of the Mach waves from the shear layer into the ambient. The results indicate that the total temperature non-uniformity results in a modified exhaust plume and mean flow distribution at the nozzle exit, compared to that of a thermally uniform flow, which past studies have indicated is a method to reduce jet noise. / Master of Science / Supersonic jet exhaust plumes produce noise in jet engines, which has been a problem in the aerospace field. Researchers are working on ways to reduce this turbulent mixing noise, with little modification to the engine and nozzle. Traditionally, nozzles produce a single stream of uniform temperature flow. This work identifies a method of reducing jet noise, known as thermal non-uniformity. A stream of cold flow is introduced at the center of the nozzle. Applying this method to jet engines can result in quieter aircraft. Large-scale turbulent structures are the dominant noise producing source in supersonic free jets. To further understand the relationship between coherent structures and acoustic jet noise, spectral analysis is used to educe these structures from the flow. This study uses velocity data collected using Time-Resolved Doppler Global Velocimetry (TR-DGV). The study compares the results of a thermally uniform and a thermally non-uniform heated supersonic jet of Mach 1.5. The goal of this study is to determine the effects of thermal non-uniformity on large-scale coherent structures using a modal decomposition analysis known as spectral proper orthogonal decomposition (SPOD). The results from this study show that the thermally non-uniform cases contained less turbulent kinetic energy compared to the thermally uniform cases. Coherent fluctuations start developing further upstream in the thermally non-uniform case. The addition of the unheated plume results in a disruption in the propagation of the Mach waves from the shear layer into the ambient. The results indicate that the total temperature non-uniformity results in a modified exhaust plume and mean flow distribution at the nozzle exit, compared to that of a thermally uniform flow, which past studies have indicated is a method to reduce jet noise.

Electrocardiogram (ECG) signal processing has been the subject of intense research in the past years, due to its strategic place in the detection of several cardiac pathologies. However, ECG signal is frequently corrupted with different types of noises such as 60Hz power line interference, baseline drift, electrode movement and motion artifact, etc. In this thesis, a hybrid two-stage model based on the combination of wavelet decomposition and artificial neural network is proposed for ECG noise reduction based on excellent localization features: wavelet transform and the adaptive learning ability of neural network. Results from the simulations validate the effectiveness of this proposed method. Simulation results on actual ECG signals from MIT-BIH arrhythmia database [30] show this approach yields improvement over the un-filtered signal in terms of signal-to-noise ratio (SNR).

We propose a method of adaptive image denoising using a support vector machine (SVM) classifier to select between multiple well-performing contemporary denoising algorithms for each pixel of a noisy image. We begin by proposing a simple method for realistically generating noisy images, and also describe a number of novel and pre-existing features based on seam energy, local colour, and saliency which are used as classifier inputs. Our SVM strategic image denoising (SVMSID) results demonstrate better image quality than either candidate denoising algorithm for images of moderate noise level, as measured using the perceptually-based quaternion structural similarity image metric (QSSIM). We also demonstrate a modified training point selection method to improve robustness across many noise levels, and propose various extensions to SVMSID for further exploration. / Thesis / Master of Applied Science (MASc)

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