This paper deals with the implementation and performance evaluation of adaptive filtering algorithms for noise cancellation without reference signal. Noise cancellation is a technique of estimating a desired signal from a noise-corrupted observation. If the signal and noise characteristics are unknown or change continuously over time, the need of adaptive filter arises. In contrast to the conventional digital filter design techniques, adaptive filters do not have constant filter parameters, they have the capability to continuously adjust their coefficients to their operating environment. To design an adaptive filter, that produces an optimum estimate of the desired signal from the noisy environment, different adaptive filtering algorithms are implemented and compared to each other. The Least Mean Square LMS, the Normalized Least Mean Square NLMS and the Recursive Least Square RLS algorithm are investigated. Three performance criteria are used in the study of these algorithms: the rate of convergence, the error performance and the signal-to-noise ratio SNR. The implementation results show that the adaptive noise cancellation application benefits more from the use of the NLMS algorithm instead of the LMS or RLS algorithm.

In order to ensure secure communication in digital military radio systems, multiple methods are used to protect the transmission from being intercepted by enemy electronic warfare systems. An intercepted transmission can be used to estimate several parameters of the transmitted signal such as its origin (position or direction) and of course the transmitted message itself. The methods used in traditional electronic warfare direction-finding systems have in general poor performance against wideband low power signals while the considered correlation-based time-difference of arrival (TDOA) methods show promising results. The output from a TDOA-based direction-finding system using two spatially separated receivers is the TDOA for the signal between the receiving sensors which uniquely describes a hyperbolic curve and the emitter is located somewhere along this curve. In order to measure a TDOA between two digital radio receivers both receiver systems must have the same time and frequency references to avoid degradation due to reference imperfections. However, in some cases, the receivers are separated up to 1000 km and can not share a common reference. This is solved by using a reference module at each of the receiver sites and high accuracy is achieved using the NAVSTAR-GPS system but, still, small differences between the outputs of the different reference modules occurs which degrades the performance of the system. In a practical electronic warfare system there is a number of factors that degrade the performance of the system, such as non-ideal antennas, analog receiver filter differences, and the analog to digital converter errors. In this thesis we concentrate on the problems which arises from imperfections in the reference modules, such as time and frequency errors.

Despite shifting towards mm-wave bands, the sub 6-GHz band will continue tobe a fundamental spectral band in 5G. Yet, the severe crowdedness of this bandmakes a well constrained spectrum one of the critical 5G requirements. A wellconstrained spectrum means that the communications regimes should dwell politelywithin their dedicated spectral bands and not interfere with other systems workingon neighboring bands. Consequently, communications community seeks convenientmodulation schemes.Accordingly, high Out Of Band (OOB) emission phenomenon in Orthogonal Fre-quency Division Multiplexing (OFDM) is unfavorable for some regimes operating in5G. Therefore, to legitimize OFDM with all 5G regimes, we need to suppress OFDMOOB emission.Since the discontinuous nature of the OFDM signal is the main reason for the highOOB emission, one solution is to render the discontinuous OFDM signal continuous.Two factors control this discontinuity: the physical shape of the modulated signaland the correlation property of the data symbols that modulate the OFDM signal.While most of the traditional approaches focus on reshaping the OFDM signalto render it continuous, in this work we give our attention to the spectral precod-ing approaches. These approaches manipulate the correlation property of the datasymbols to control the high OOB emission in OFDM.On the other hand, tweaking the correlation property of the modulating datasymbols will violate their orthogonality. This violation will yield in-band interfer-ence within the OFDM signal which would degrade the bit error performance of thereceived data.The thesis explains the spectral precoding techniques from conceptual and math-ematical point of view. We discuss the OOB emission suppression capability of theprecoding techniques and study their drawbacks and limitations. We provide ana-lytical trade o study between precoding approaches and classical OFDM treatmentapproaches at the level of OOB emission suppression and in-band interference. Weshow that the in-band interference in precoding techniques is independent on thecommunications channel behavior contrary to that of classical techniques. More-over, we dene the optimal precoder that minimizes the in-band interference. Con-sequently, we design a novel practical precoder that approaches the performanceof the optimal precoder. Furthermore, we analyze the complexity of the precodingapproaches and study the implementation computational requirements.Finally, we test the real time performance of these precoding techniques usingSoftware Designed Radio (SDR) Universal Software Radio Peripherals (USRPs). Wespotlight the hardware limitations and show that despite these limitations, the spec-tral precoder is able to suppress the OOB emissions by tens of decibels. We check the reliability of spectral precoding in practical over air communications systems bysetting up the rst spectral precoding proof of concept prototype. The prototypeproves that precoded OFDM systems cause less OOB interference on neighboringcommunications systems.

The number of Nano satellites launched into space is increasing rapidly each year. All satellites need a ground station with a baseband to communicate with. Current commercial basebands for TT&C (telemetry, tracking and command) are using proven designs and are too expensive to be used for Nano satellites. This thesis investigates the feasibility to use a SDB (Software-Defined Baseband) for TT&C with Nano satellites in low earth orbit. It describes the making of a transceiver which is validated with simulated data,recorded data and live satellite passes. It also describes the implementation of a GPS (Global PositioningSystem), TCR (Time Code Receiver) and TCP/IP (Transmission Control Protocol/Internet Protocol)interface as well as the process of testing for EMC (Electromagnetic Compatability) requirements. A half-duplex transmit/receive system is also proposed. The SDB functioned acceptable for telemetry with Nano satellites with up to 90% of the data packets being received depending on which satellite that wasbeing tracked. The SDB needs further development, validation and certification to compete with today’scommercial basebands.

This thesis deals with the estimation of synchronization parameters in {Orthogonal Frequency Division Multiplexing} (OFDM) communication systems and in active ultrasonic measuring systems. Estimation methods for the timing and frequency offset and for the attenuation taps of the frequency selective channel are presented and investigated.In OFDM communication systems the estimation of the timing offset of the transmitted data frame is one important parameter. This offset provides the receiver with a means of synchronizing its sampling clock to that of the transmitter. A second important parameter is the offset in the carrier frequency used by the receiver to demodulate the received signal.For OFDM systems using a cyclic prefix, the joint {Maximum Likelihood} (ML) estimation of the timing and carrier frequency offset is introduced. The redundancy introduced by the prefix is exploited optimally. This novel method is derived for a non-dispersive channel. Its performance, however, is also evaluated for a frequency-selective Rayleigh-fading radio channel. Time dispersion causes an irreducible error floor in this estimator's performance. This error floor is the limiting factor for the applicability of the timing estimator. Depending on the requirements, it may be used in either an acquisition or a tracking mode. For the frequency estimator the error floor is low enough to allow for stable frequency tracking.A low-complex variant of the timing offset estimator is presented allowing a simple implementation. This is the ML estimator, given a 2-bit representation of the received signal as the sufficient statistics. Its performance is evaluated for a frequency-selective Rayleigh-fading radio channel and for a twisted-pair copper channel. Simulations show this estimator to have a similar error floor as the full resolution ML estimator.The problem of estimating the propagation time of a signal is also of interest in active pulse echo systems, such as are used in, {\it e.g.}, radar, medical imaging, and geophysics. The {Minimum Mean Squared Error} (MMSE) estimator of arrival time is derived and investigated for an active airborne ultrasound measurement system. Besides performing better than the conventional {\it Maximum a Posteriori} (MAP) estimator, this method can be used to develop different estimators in situations where the system Signal to Noise Ratio (SNR) is unknown.Coherent multi-amplitude OFDM receivers generally need to compensate for a frequency selective channel in order to detect transmitted data symbols reliably. For this purpose, a channel equalizer needs to be fed estimates of the subchannel attenuations.The linear MMSE estimator of these attenuations is presented. Of all linear estimators, this estimator optimally makes use of the frequency correlation between the subchannel attenuations. Low-complex modified estimators are proposed and investigated. The proposed modifications cause an irreducible error floor for this estimator's performance, but simulations show that for SNR values up to 20~dB, the improvement of a modified estimator compared to the Least Squares (LS) estimator is at least 3~dB. / <p>Godkänd; 1996; 20080328 (ysko)</p>

In paper-making there are numerous of parameters that control the final outcome of the paper. This thesis examines the connections between paper properties and influential factors in the manufacturing process, by looking at the entire fiber line from the incoming wood chips to the actual finished paper. The analysis is done by studying how important process variables connect to the properties of the paper, and also by investigating the possibilities of modeling how these affect the final product.There are numerous factors that affect the final outcome of a manufactured paper. Five of these; wood type, cooking time, refining energy input, amount of starch and roll pressure in the paper machine, have been investigated in a series of laboratory experiments. A factorial designed experiment was set up to investigate the mentioned factors impact on paper properties. Focus in the study was laid on two aspects. One was to investigate interaction effects among the process variables and the significance of theseinteraction effects as well as the main effects. For the second part it was possible to utilize these interaction effects and deduce which combinations of factor levels that could result in equal output levels of certain paper parameters.Being able to predict the paper quality as accurately as possible is another importantaspect in paper-making. In the second study in the thesis the relation between the paper properties and process variations are charted. Through different multivariate methods prediction models were created based on the data gathered in the designed experiments. The underlying correlation structures in the data could be used in conjunction with the design factors to derive models that connected process parameters to paper properties. With the help of these models it is possible to predict what paper property levels toexpect when altering process variables.

Sintering occurs in many types of material such as iron, ceramics and snow, typically during thermal treatment, and aects the material properties, particularly the strength, by the bonding of particles into a coherent structure. In order to improve the mechanical strength in magnetite iron ore pellets it is important to be able to characterize and quantitatively measure the degree of sintering and features that impact the process of sintering.The aim for this licentiate thesis has been to create tools for sintering characterization through automated image analysis of optical microscopy images. Such tools are of interest since they provide a comparable quantication of pellet properties that can be related to other parameters, giving a historical record that is digital, objective and not dependent on the eyes of a trained expert. In this work, two dierent studies of the microstructure in indurated (heat hardened) pellets have been performed. The methods presented in these studies have been shown suitable for characterizing sintering properties in iron ore pellets, and possibly also other materials that experience sintering phenomena.The first study presents research to automate image capture and analysis of entire crosssections of indurated iron ore pellets to characterize proportions of magnetite, hematite, and other components. Spatial distributions of the mentioned phases are produced for each pellet, graphing proportions in relation to the distance to the pellet surface. The results are not directly comparable to a chemical analysis but comparisons with manual segmentation of images validates the method. Dierent types of pellets have been tested and the system has produced robust results for varying cases.The second study focuses on the analysis of the particle joins and structure. The joins between particles have been identied with a method based mainly on morphological image processing and features have been calculated based on the geometric properties and curvature of these joins. The features have been analyzed and been determined to hold discriminative power by displaying properties consistent with sintering theory and results from traditional physical dilation measurements on the heated samples.A note of caution for quantitative studies of iron ore pellet has been identied in this thesis. Especially for green pellets, the microscopy sample preparation prohibit any statistical inference studies due to particle rip-out during polishing. Researchers performing qualitative microscopy studies are generally aware of the phenomenon of rip-outs, but the extent of how even seemingly good samples are aected has not been unveiled until attempting extensive quantitative analysis of features such as green pellet porosity during the course of this work.

Image analysis methods for processing 3D profile data have been investigated and developed. These methods include; Image reconstruction by prioritized incremental normalized convolution, morphology-based crack detection for steel slabs, and adaptive morphology based on the local structure tensor. The methods have been applied to a number of industrial applications.An issue with 3D profile data captured by laser triangulation is occlusion, which occurs when the line-of-sight between the projected laser light and the camera sensor is obstructed. To overcome this problem, interpolation of missing surface in rock piles has been investigated and a novel interpolation method for filling in missing pixel values iteratively from the edges of the reliable data, using normalized convolution, has been developed.3D profile data of the steel surface has been used to detect longitudinal cracks in casted steel slabs. Segmentation of the data is done using mathematical morphology, and the resulting connected regions are assigned a crack probability estimate based on a statistic logistic regression model. More specifically, the morphological filtering locates trenches in the data, excludes scale regions for further analysis, and finally links crack segments together in order to obtain a segmented region which receives a crack probability based on its depth and length.Also suggested is a novel method for adaptive mathematical morphology intended to improve crack segment linking, i.e. for bridging gaps in the crack signature in order to increase the length of potential crack segments. Standard morphology operations rely on a predefined structuring element which is repeatedly used for each pixel in the image. The outline of a crack, however, can range from a straight line to a zig-zag pattern. A more adaptive method for linking regions with a large enough estimated crack depth would therefore be beneficial. More advanced morphological approaches, such as morphological amoebas and path openings, adapt better to curvature in the image. For our purpose, however, we investigate how the local structure tensor can be used to adaptively assign to each pixel an elliptical structuring element based on the local orientation within the image. The information from the local structure tensor directly defines the shape of the elliptical structuring element, and the resulting morphological filtering successfully enhances crack signatures in the data.

Rolling element bearings are used in rotating machinery in various industry branches. Their health status must be monitored continuously in order to establish proper operational conditions in a production process. Numerous approaches, which can be investigated under the subject of ``Condition Based Maintenance", have been studied within mechanical engineering and signal processing to be able to detect and classify possible faults on rolling bearings.Periodic impulsive signals can emerge from defected bearings within rotating machinery. As the signal is distorted by an unknown transfer function, noise and severe interference, the challenge becomes to reduce these effects as much as possible to extract valuable and reliable information about the rolling bearings' health status. Without any observation of the source signal, a scale-invariant higher order moment, skewness, can be used as a tool to characterize statistical properties to enhance the desired signal. It is the impulsiveness, thus asymmetry of the signal that will be promoted. To assess the performance of skewness, a signal model that consists of harmonically related sinusoids representing an impulsive source is built. Depending on such a model, surface characteristics of skewness are investigated. In relation to harmonic content, the ability of skewness in discovering such harmonic relation is studied. It has been observed that the optimization process converges to a setting where all harmonics are preserved, while any component that does not possess such a harmonic relation is suppressed. In the case of multiple mutually inharmonic source signals with harmonic support, it is shown that skewness maximization results in a setting where only the harmonic set with highest skewness remains. Finally, experimental examples are provided to support theoretical findings.

Additive manufacturing (AM) enables the manufacturing of complex and tailored products for an unlimited number of applications such as aerospace, healthcare, etc. The technology has received a lot of attention in lightweight applications where it is associated with new design possibilities but also reduced material costs, material waste, and energy consumption. The use of ultrasound has the potential to become the material characterization method used for AM since it is quick, safe, and scales well with component size. Ultrasound data, coupled with supervised learning techniques, serves as a powerful tool for the non-destructive evaluation of different materials, such as metals. This research focuses on understanding the additive manufacturing process, the resulting material properties, and the variation captured using ultrasound due to the manufacturing parameters. The case study included in this thesis is the examination of 316L steel cubes manufactured using laser powder bed fusion. This study includes the estimation and prediction of manufacturing parameters using supervised learning, the assessment of the influence of the manufacturing parameters on the variability within samples, and the quantitative quality assessment of the samples based on the material properties that are a result of the changes in manufacturing parameters. The research is vital for analyzing the homogeneity of microstructures, advancement in online process control, and ensuring the quality of additively manufactured products. This study contributes to valuable insights into the relationship between manufacturing parameters, material properties, and ultrasound signatures. There is a significant variation captured using ultrasound within the samples and between samples that shows the backscattered signal is sensitive to the microstructure that is a result of the manufacturing parameters. Since the material properties change with the change in manufacturing parameters, the quality of a sample can be described by the relation between the material properties and backscattered ultrasound signals. The thesis is divided into two parts. The first part focuses on the introduction of the study, a summary of the contributions, and future work. The second part contains a collection of papers describing the research in detail.