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This bachelor thesis investigates the possibility of implementing a system to detect defects with extractable data from ABB’s servo guns. By looking at the deflection data i.e., the electrode position at every stroke, multiple methods are applied to investigate the behavior of the data. The distribution of the data that was first investigated led the thesis straight to one of the traditional methods in Statistical Process Control (SPC) called Control Chart. After investigating the data’s frequency domain, pattern and applying unsupervised clustering, several experiments were performed. The first experiment was to see how the deflection changed due to a change in force applied between the electrode tips. Second, artificial defects was created by switching electrode tips of different lengths and change the set force between the electrode tips. The idea behind the experiments was to evaluate the performance of a control chart whereas the lower and upper control levels was based on a regression curve that was implemented by a set force for a specific servo gun. The method proved to work for the tests that were performed, but as the results suggest it needs more research and development to be suitable for implementation in ABB’s software. / Detta examensarbete undersöker möjligheten att implementera ett system som detekterar defekter m.h.a. böjningsdata från ABB’s svetstänger. Genom att titta på datan, dvs., elektrodernas position vid varje slag/svets, används flertal metoder för att undersöka processens beteende. Datans distribution visade en god passform till en normalfördelning, vilket ledde projektet till en traditionell metod inom Statistical Process Control (SPC) sk. Control Chart. Efter att man undersökt datans frekvensdomän, om det fanns ett mönster att avläsa och tillämpat Unsupervised clustering, genomfördes flera olika experiment. Det första experimentet undersökte hur böjningen i tångarmarna förändrades genom att applicera olika krafter mellan elektroderna. Dessutom genererades data för att försöka skapa konstgjorda sprickor genom att byta ut elektroder av olika längd. I tillägg skapades drivande data (förändringar i böjning med tiden) genom att applicera små förändringar i kraft mellan elektroderna i ett visst intervall. Idén bakom experimenten var att utvärdera prestationen av en Control Chart, där övre och undre kontrollparametrar bestämdes utifrån en regressionsfunktion som var implementerad m.h.a. parametrar från resultatet utav förhållandet mellan kraft och böjning. Experimenten utfördes på en utav ABB’s svettänger s.k. GWT X9. Metoden visade sig fungera för testen som blev utförda, men resultatet föreslår att arbetet kräver mer foskning och arbete för att bli implementerad i ABB’s mjukvara.

this thesis the problem of maximum likelihood (ML) detection for the linear multiple-input multiple-output (MIMO) channel is considered. The thesis investigates two algorithms previously proposed in the literature for implementing the ML detector, namely semide nite relaxation and sphere decoding. The first algorithm, semide nite relaxation, is a suboptimal implementation of the ML detector meaning that it is not guaranteed to solve the maximum likelihood detection problem. Still, numerical evidence suggests that the performance of the semide nite relaxation detector is close to that of the true ML detector. A contribution made in this thesis is to derive conditions under which the semide nite relaxation estimate can be guaranteed to coincide with the ML estimate. The second algorithm, the sphere decoder, can be used to solve the ML detection problem exactly. Numerical evidence has previously shown that the complexity of the sphere decoder is remarkably low for problems of moderate size. This has led to the widespread belief that the sphere decoder is of polynomial expected complexity. This is however unfortunately not true. Instead, in most scenarios encountered in digital communications, the expected complexity of the algorithm is exponential in the number of symbols jointly detected. However, for high signal to noise ratio the rate of exponential increase is small. In this thesis it is proved that for a large class of detection problems the expected complexity is lower bounded by an exponential function. Also, for the special case of an i.i.d. Rayleigh fading channel, an asymptotic analysis is presented which enables the computation of the expected complexity up to the linear term in the exponent.

Two estimation problems are treated in this thesis. Estimators are suggested and the asymptotical properties of the estimates are investigated analytically. Numerical simulations are used to assess small-sample performance. In addition, performance bounds are calculated. The first problem treated is parameter estimation for the reduced rank linear regression. A new method based on instrumental variable principles is proposed and its asymptotical performance analyzed. In addition, the Cram\'r-Rao bound for the problem is derived for a general Gaussian noise model. The new method is asymptotically efficient (it has the smallest possible covariance) if the noise is temporally white, and outperforms previously suggested algorithms when the noise is temporally correlated. The approximation of a matrix with one of lower rank under a weighted norm is needed as part of the estimation algorithm. Two new, computationally efficient, methods are suggested. While the general matrix approximation problem has no known closed form solution, the proposed methods are asymptotically optimal as part of the estimation procedure in question. A new algorithm is also suggested for the related rank detection problem. The second part of this thesis treats direction of arrival estimation for narrowband signals using an array of sensors. Most algorithms require the noise covariance matrix to be known (up to a scaling factor) or to possess a known structure. In many cases the noise covariance is in fact estimated from a separate batch of signal-free samples. This work addresses the combined effects of finite sample sizes both in the estimated noise covariance matrix and in the data with signals present. No assumption is made on the structure of the noise covariance. The asymptotical covariance of weighted subspace fitting (WSF) is derived for the case in which the data are whitened using the noise covariance estimate. The obtained expression suggests an optimal weighting that improves performance compared to the standard choice. In addition, a new method based on covariance matching is proposed. Both methods are asymptotically statistically efficient. The Cramér-Rao bound for the problem is derived, and the expression becomes surprisingly simple. / <p>QC 20110103</p>

Multi-robot systems have important applications, such as space explorations, underwater missions, and surveillance operations. In most of these cases robots need to exchange data through communication. Limitations in the communication system however impose constraints on the design of coordination strategies. In this thesis we present three papers on cooperative control problems in which different communication constraints are considered. The first paper describes a rendezvous problem for a team of robots that exchanges position information through communication. A local control law for each robot should steer the team to a common meeting point when communicated data are quantized. The robots are not equipped with any sensors so the positions of other teammates are not measured. Two different types of quantized communication are considered: uniform and logarithmic. Logarithmic quantization is often preferable since it requires that fewer bits are communicated compared to when uniform quantization is used. For a class of feasible communication topologies, control laws that solve the rendezvous problem are derived. A hierarchical control structure is proposed in the second paper, for modelling autonomous underwater vehicles employed in finding a minimum of a scalar field. The controller is composed of two layers. The upper layer is the team controller, which is modeled as discrete-event system. It generates waypoints based on the simplex search optimization algorithm. The waypoints are used as target points by the lower control layer, which continuously steers each vehicle from the current to the next waypoint. It is shown that the communication of measurements is needed at each step for the team controller to generate unique waypoints. A protocol is proposed to reduce the amount of data to be exchanged, motivated by that underwater communication is costly in terms of energy. In the third paper, a probabilistic pursuit{evasion game is considered as an example to study constrained communication in multi-robot systems. This system can be used to model search-and-rescue operations and multi-robot exploration. Communication protocols based on time-triggered and event-triggered synchronization schemes are considered. It is shown that by limiting the communication to events when the probabilistic map updated by the individual pursuer contains new information, as measured by a map entropy, the utilization of the communication link can be considerably improved compared to conventional time-triggered communication.

This thesis begins with a survey of current state-of-the art in-car navigation systems. The pros and cons of the four commonly used information sources — GNSS/RF-based positioning, vehicle motion sensors, vehicle models and map information — are described. Common filters to combine the information from the various sources are discussed. Next, a GNSS-aided inertial navigation platform is presented, into which further sensors such as a camera and wheel-speed encoder can be incorporated. The construction of the hardware platform, together with an extended Kalman filter for a closed-loop integration between the GNSS receiver and the inertial navigation system (INS), is described. Results from a field test are presented. Thereafter, an approach is studied for calibrating a low-cost inertial measurement unit (IMU), requiring no mechanical platform for the accelerometer calibration and only a simple rotating table for the gyro calibration. The performance of the calibration algorithm is compared with the Cramér-Rao bound for cases where a mechanical platform is used to rotate the IMU into different precisely controlled orientations. Finally, the effects of time synchronization errors in a GNSS-aided INS are studied in terms of the increased error covariance of the state vector. Expressions for evaluating the error covariance of the navigation state vector are derived. Two different cases are studied in some detail. The first considers a navigation system in which the timing error is not taken into account by the integration filter. This leads to a system with an increased error covariance and a bias in the estimated forward acceleration. In the second case, a parameterization of the timing error is included as part of the estimation problem in the data integration. The estimated timing error is fed back to control an adjustable fractional delay filter, synchronizing the IMU and GNSS-receiver data. / QC 20101117

Physiological signals when subjected to digital signal processing algorithms often reveal information about their origin and how they are regulated. Recently, it has been shown that when power spectrum of the heart rate variability signal is computed, physiological mechanisms about how the autonomic nervous system modulates the sinus node of the heart can be unraveled. During the past several years, computation of the power spectrum of heart rate variability has progressed from Blackman-Tukey algorithm and autoregressive modelling to Wigner-Ville distribution. In this thesis, we describe the development of appropriate algorithms for QRS detection from an ECG signal to obtain a heart rate signal, interpolation of heart rate variability signal and the computation of power spectrum. We also describe mathematical details underlying time-frequency analysis, specifically for the Wigner-Ville distribution. We present a software package in C++, for computing the Wigner-Ville distribution of the heart rate variability signal. As applications of these methods in physiology and clinical medicine, we found that the power spectrum of the heart rate variability of premature infants can help us understand the ontogeny of the autonomic nervous system. Similarly, physiological effects of atropine, methacholine and allergen challenges can be elucidated using the power spectrum of heart rate variability in small animals, such as a rat model. Furthermore, a progressive tilt model in human subjects is used to compare power spectrum obtained from the Blackman-Tukey method, autoregressive modelling and the Wigner-Ville distribution. Finally, an application of the Wigner-Ville distribution technique to study the changes that take place in the autonomic regulation of the heart during different stages of sleep is presented. / Thesis / Master of Engineering (ME)