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  • 1.
    Abd-Elrady, Emad
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Harmonic signal modeling based on the Wiener model structure2002Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    The estimation of frequencies and corresponding harmonic overtones is a problem of great importance in many situations. Applications can, for example, be found in supervision of electrical power transmission lines, in seismology and in acoustics. Generally, a periodic function with an unknown fundamental frequency in cascade with a parameterized and unknown nonlinear function can be used as a signal model for an arbitrary periodic signal. The main objective of the proposed modeling technique is to estimate the fundamental frequency of the periodic function in addition to the parameters of the nonlinear function.

    The thesis is divided into four parts. In the first part, a general introduction to the harmonic signal modeling problem and different approaches to solve the problem are given. Also, an outline of the thesis and future research topics are introduced.

    In the second part, a previously suggested recursive prediction error method (RPEM) for harmonic signal modeling is studied by numerical examples to explore the ability of the algorithm to converge to the true parameter vector. Also, the algorithm is modified to increase its ability to track the fundamental frequency variations.

    A modified algorithm is introduced in the third part to give the algorithm of the second part a more stable performance. The modifications in the RPEM are obtained by introducing an interval in the nonlinear block with fixed static gain. The modifications that result in the convergence analysis are, however, substantial and allows a complete treatment of the local convergence properties of the algorithm. Moreover, the Cramér–Rao bound (CRB) is derived for the modified algorithm and numerical simulations indicate that the method gives good results especially for moderate signal to noise ratios (SNR).

    In the fourth part, the idea is to give the algorithm of the third part the ability to estimate the driving frequency and the parameters of the nonlinear output function parameterized also in a number of adaptively estimated grid points. Allowing the algorithm to automatically adapt the grid points as well as the parameters of the nonlinear block, reduces the modeling errors and gives the algorithm more freedom to choose the suitable grid points. Numerical simulations indicate that the algorithm converges to the true parameter vector and gives better performance than the fixed grid point technique. Also, the CRB is derived for the adaptive grid point technique.

  • 2.
    Abd-Elrady, Emad
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Nonlinear Approaches to Periodic Signal Modeling2005Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Periodic signal modeling plays an important role in different fields. The unifying theme of this thesis is using nonlinear techniques to model periodic signals. The suggested techniques utilize the user pre-knowledge about the signal waveform. This gives these techniques an advantage as compared to others that do not consider such priors.

    The technique of Part I relies on the fact that a sine wave that is passed through a static nonlinear function produces a harmonic spectrum of overtones. Consequently, the estimated signal model can be parameterized as a known periodic function (with unknown frequency) in cascade with an unknown static nonlinearity. The unknown frequency and the parameters of the static nonlinearity are estimated simultaneously using the recursive prediction error method (RPEM). A treatment of the local convergence properties of the RPEM is provided. Also, an adaptive grid point algorithm is introduced to estimate the unknown frequency and the parameters of the static nonlinearity in a number of adaptively estimated grid points. This gives the RPEM more freedom to select the grid points and hence reduces modeling errors.

    Limit cycle oscillations problem are encountered in many applications. Therefore, mathematical modeling of limit cycles becomes an essential topic that helps to better understand and/or to avoid limit cycle oscillations in different fields. In Part II, a second-order nonlinear ODE is used to model the periodic signal as a limit cycle oscillation. The right hand side of the ODE model is parameterized using a polynomial function in the states, and then discretized to allow for the implementation of different identification algorithms. Hence, it is possible to obtain highly accurate models by only estimating a few parameters.

    In Part III, different user aspects for the two nonlinear approaches of the thesis are discussed. Finally, topics for future research are presented.

  • 3.
    Abd-Elrady, Emad
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Söderström, Torsten
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Wigren, Torbjörn
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Periodic signal analysis using orbits of nonlinear ODEs based on the Markov estimate2004Conference paper (Refereed)
  • 4.
    Abd-Elrady, Emad
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Söderström, Torsten
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Wigren, Torbjörn
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Periodic signal modeling based on Liénard's equation2003Report (Other academic)
  • 5.
    Abd-Elrady, Emad
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Söderström, Torsten
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Wigren, Torbjörn
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Periodic signal modeling based on Liénard's equation2004In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 49, no 10, p. 1773-1778Article in journal (Refereed)
  • 6.
    Abrahamsson, Anna
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Variance Adaptive Quantization and Adaptive Offset Selection in High Efficiency Video Coding2016Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Video compression uses encoding to reduce the number of bits that are used forrepresenting a video file in order to store and transmit it at a smaller size. Adecoder reconstructs the received data into a representation of the original video.Video coding standards determines how the video compression should beconducted and one of the latest standards is High Efficiency Video Coding (HEVC).One technique that can be used in the encoder is variance adaptive quantizationwhich improves the subjective quality in videos. The technique assigns lowerquantization parameter values to parts of the frame with low variance to increasequality, and vice versa. Another part of the encoder is the sample adaptive offsetfilter, which reduces pixel errors caused by the compression. In this project, thevariance adaptive quantization technique is implemented in the Ericsson researchHEVC encoder c65. Its functionality is verified by subjective evaluation. It isinvestigated if the sample adaptive offset can exploit the adjusted quantizationparameters values when reducing pixel errors to improve compression efficiency. Amodel for this purpose is developed and implemented in c65. Data indicates thatthe model can increase the error reduction in the sample adaptive offset. However,the difference in performance of the model compared to a reference encoder is notsignificant.

  • 7.
    Abrahamsson, Richard
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Estimation Problems in Array Signal Processing, System Identification, and Radar Imagery2006Doctoral thesis, monograph (Other academic)
    Abstract [en]

    This thesis is concerned with parameter estimation, signal processing, and applications.

    In the first part, imaging using radar is considered. More specifically, two methods are presented for estimation and removal of ground-surface reflections in ground penetrating radar which otherwise hinder reliable detection of shallowly buried landmines. Further, a study of two autofocus methods for synthetic aperture radar is presented. In particular, we study their behavior in scenarios where the phase errors leading to cross-range defocusing are of a spatially variant kind.

    In the subsequent part, array signal processing and optimal beamforming is regarded. In particular, the phenomenon of signal cancellation in adaptive beamformers due to array perturbations, signal correlated interferences and limited data for covariance matrix estimation is considered. For the general signal cancellation problem, a class of improved adaptive beamformers is suggested based on ridge-regression. Another set of methods is suggested to mitigate signal cancellation due to correlated signal and interferences based on a novel way of finding a characterization of the interference subspace from observed array data. Further, a new minimum variance beamformer is presented for high resolution non-parametric spatial spectrum estimation in cases where the impinging signals are correlated. Lastly, a multitude of enhanced covariance matrix estimators from the statistical literature are studied as an alternative to other robust adaptive beamforming methods. The methods are also applied to space-time adaptive processing where limited data for covariance matrix estimation is a common problem.

    In the third and final part the estimation of the parameters of a general bilinear problem is considered. The bilinear model is motivated by the application of identifying submarines from their electromagnetic signature and by the identification of a Hamerstein-Wiener model of a non-linear dynamic system. An efficient approximate maximum-likelihood method with closed form solution is suggested for estimating the bilinear model parameters.

  • 8. Abu-Rmileh, Amjad
    et al.
    Garcia-Gabin, Winston
    Zambrano, Darine
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    A robust sliding mode controller with internal model for closed-loop artificial pancreas2010In: Medical and Biological Engineering and Computing, ISSN 0140-0118, E-ISSN 1741-0444, Vol. 48, no 12, p. 1191-1201Article in journal (Refereed)
  • 9. Abu-Rmileh, Amjad
    et al.
    Garcia-Gabin, Winston
    Zambrano, Darine
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Internal model sliding mode control approach for glucose regulation in type 1 diabetes2010In: Biomedical Signal Processing and Control, ISSN 1746-8094, Vol. 5, no 2, p. 94-102Article in journal (Refereed)
  • 10. Agüero, Juan C.
    et al.
    Godoy, Boris I.
    Goodwin, Graham C.
    Wigren, Torbjörn
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Scenario-based EM identification for FIR systems having quantized output data2009In: Proc. 15th IFAC Symposium on System Identification, International Federation of Automatic Control , 2009, p. 66-71Conference paper (Refereed)
  • 11. Agüero, Juan C.
    et al.
    Goodwin, Graham C.
    Lau, Katrina
    Wang, Meng
    Silva, Eduardo I.
    Wigren, Torbjörn
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Three-degree of freedom adaptive power control for CDMA cellular systems2009In: Proc. 28th Global Telecommunications Conference, IEEE Communications Society, 2009, p. 2793-2798Conference paper (Refereed)
  • 12.
    Ahmed-Ali, Tarek
    et al.
    Université de Caen Normandie, ENSICAEN, 14032 Caen, France.
    Tiels, Koen
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Schoukens, Maarten
    Control Systems, Eindhoven University of Technology, Eindhoven, The Netherlands.
    Giri, Fouad
    Université de Caen Normandie, ENSICAEN, 14032 Caen, France.
    Sampled-Data Based State and Parameter Estimation for State-Affine Systems with Uncertain Output Equation2018Conference paper (Refereed)
    Abstract [en]

    The problem of sampled-data observer design is addressed for a class of state- and parameter-affine nonlinear systems. The main novelty in this class lies in the fact that the unknown parameters enter the output equation and the associated regressor is nonlinear in the output. Wiener systems belong to this class. The difficulty with this class of systems comes from the fact that output measurements are only available at sampling times causing the loss of the parameter-affine nature of the model (except at the sampling instants). This makes existing adaptive observers inapplicable to this class of systems. In this paper, a new sampled-data adaptive observer is designed for these systems and shown to be exponentially convergent under specific persistent excitation (PE) conditions that ensure system observability and identifiability. The new observer involves an inter-sample output predictor that is different from those in existing observers and features continuous trajectories of the state and parameter estimates.

  • 13.
    Alemayehu, Brook
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Johnsons, Fredrik
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Maskininlärning inom kommersiella fastigheter: Prediktion av framtida hyresvakanser2018Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The purpose of this thesis is to investigate the possibilities of predicting vacancies in the real estate market by using machine learning models in terms of classification. These models were mainly based on data from contracts between a Swedish real estate company and their tenants. Attributes such as annual renting cost and rental area for each contract were supplemented with additional data regarding financial and geographical information about the tenants. The data was stored in three different formats with the first having binary classes which aim is to predict if the tenant is moving out within a year or more. The format of the second and third version were both multi classification problems that aims to classify if the tenants might terminate their contract within a specific interval with the length of three and six months.

    Based on the results from Microsoft Azure Machine Learning Studio, it is discovered that the multi classification problems perform rather poorly due to the classes being unbalanced. Regarding the  performance of the binary model, a more satisfying result was obtained but not to the extend to say that the model can be used to determine a vacancy with high accuracy. It should rather be used as a risk analysis tool to detect if a tenant is showing tendencies that could result in a future vacancy. A major pitfall of this thesis was the lack of data and the financial information not being specific enough. The performance of the models will likely increase with a larger dataset and more accurate financial information. 

  • 14. Almeida, Juliana
    et al.
    Martins da Silva, Margarida
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Mendonça, Teresa
    Rocha, Paula
    A compartmental model-based control strategy for NeuroMuscular Blockade level2011In: Proc. 18th IFAC World Congress, International Federation of Automatic Control , 2011, p. 599-604Conference paper (Refereed)
  • 15. Almeida, Juliana
    et al.
    Martins da Silva, Margarida
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Wigren, Torbjörn
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Mendonça, Teresa
    Contributions to the initialization of online identification algorithms for anæsthesia: the NeuroMuscular Blockade case study2010In: Proc. 18th Mediterranean Conference on Control and Automation, Piscataway, NJ: IEEE , 2010, p. 1341-1346Conference paper (Refereed)
  • 16. Alonso, Hugo
    et al.
    Mendonça, Teresa
    Lemos, João M.
    Wigren, Torbjörn
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    A simple model for the identification of drug effects2009In: Proc. 6th International Symposium on Intelligent Signal Processing, Piscataway, NJ: IEEE , 2009, p. 269-273Conference paper (Refereed)
  • 17.
    Alverbäck, Adam
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    LQG-control of a Vertical Axis Wind Turbine with Focus on Torsional Vibrations2012Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In this thesis it has been investigated if LQG control could be used to mitigate torsional oscillations in a variable speed, fixed pitch wind turbine. The wind turbine is a vertical axis wind turbine with a 40 m tall axis that is connected to a generator. The power extracted by the turbine is delivered to the grid via a passive rectifier and an inverter. By controlling the grid side inverter the current is controlled and hence the rotational speed can be controlled. A state space model was developed for the LQG controller. The model includes both the dynamics of the electrical system as swell as the two mass system, consisting of the turbine and the generator connected with a flexible shaft. The controller was designed to minimize a quadratic criterion that punishes both torsional oscillations, command following and input signal magnitude. Integral action was added to the controller to handle the nonlinear aerodynamic torque.

    The controller was compared to the existing control system that uses a PI controller to control the speed, and tested usingMATLAB Simulink. Simulations show that the LQG controller is just as good as the PI controller in controlling the speed of the turbine, and has the advantage that it can be tuned such that the occurrence of torsional oscillations is mitigated. The study also concluded that some external method of dampening torsional oscillations should be implemented to mitigate torsional oscillations in case of a grid fault or loss of PWM signal.

  • 18.
    Andersson, Carl
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Wahlström, Niklas
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Schön, Thomas B.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Data-Driven Impulse Response Regularization via Deep Learning2018Conference paper (Refereed)
    Abstract [en]

    We consider the problem of impulse response estimation of stable linear single-input single-output systems. It is a well-studied problem where flexible non-parametric models recently offered a leap in performance compared to the classical finite-dimensional model structures. Inspired by this development and the success of deep learning we propose a new flexible data-driven model. Our experiments indicate that the new model is capable of exploiting even more of the hidden patterns that are present in the input-output data as compared to the non-parametric models.

  • 19.
    Andersson, Helena
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Individualized mathematical modeling of neural activation in electric field2017Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Deep Brain Stimulation (DBS) is a treatment of movement disorders such as Parkinson's disease and essential tremor. Today it has been used in more than 80.000 patients. Electrical stimulation is administered by an implanted pulse generator through an electrode surgically placed in a target brain area specific to the treated disease. Opposed to alternative purely surgical treatment procedures, DBS is reversible and can be turned off.

    In this project, the aim is to individualise an already existing computational model of DBS, but also to look at optimisation of the treatment by developing a neuron model. It has been executed the following way. To localise the target area for the electrode, Magnetic Resonance Imaging (MRI) is used. An MRI image consists of volume elements called voxels. By analysing these voxels, it is possible to set up a coordinate system for the position of different parts of the brain. To build up an individualised model of the DBS, an MRI image is segmented into tissues of different conductivity thus resulting in a more accurate description of the electrical field around the electrode. To visualize the stimuli coverage for the medical staff, the MRI image of the target area, the electrode, and the electrical field produced by the stimuli are depicted in the same figure. From the results, we can draw the conclusion that this method works well for individualising the computational model of DBS, but it has only been used on one MRI scan so far so it needs further testing to obtain more data to compare with.

    The neuron model is a temporospatial mathematical model of a single neuron for the prediction of activation by a given electrically applied field generated by a DBS lead. The activation model is intended to be part of a patient-specific model of an already existing computational model of DBS. The model originate from a neuron model developed by Hodgkin and Huxley (HH). The original HH model only takes into account one compartment and, to make the neuron model more accurate, it is combined with a cable model. The simulation results obtained with the model have been validated against an established and widely accepted neuron model. The results correlated highly to each other with only minor differences. To see how position and orientation impact on activation, the developed HH model was tested for different pulse widths, distances from the lead, and rotations of the neuron relative to the lead. A larger pulse width makes activation more likely and so does a larger amplitude. Thicker neurons are more likely to get activated, neurons closer to the lead and also neurons perpendicular to the lead. From the results we can draw the conclusion that this method is a good way to stimulate neural activation of a single neuron. In future research, it might be possible to compare results from the neuron model with patient's response to treatment.

  • 20.
    Andersson, Helena
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Cubo, Rubén
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    The Impact of Deep Brain Stimulation on a Simulated Neuron: Inhibition, Excitation, and Partial Recovery2018In: 2018 European Control Conference, Limassol, Cyprus, 2018Conference paper (Refereed)
    Abstract [en]

    Deep Brain Stimulation (DBS) is an establishedtherapy to alleviate the symptoms of neurological disorderssuch as Parkinson’s Disease and Essential Tremor. Depending on the disease, a certain area of the brain is subjected to electrical stimuli through a surgically implanted lead. Despite the clinically proven effectiveness of DBS, the underlying biological mechanism is poorly understood. Two dominating theories seek to explain how the DBS therapy exerts effect on neurons, one through inhibition and another through excitation. This simulation study aims at demonstrating that both scenarios are feasible within the brain domain exposed to pulsatile electrical stimulation and conditional on the temporal relationship between the neural input and the DBS pulse sequence. Since some neurons in the targeted population are assisted to fire in response to the cumulative dynamical action of a stimulation pulse and neural input from a neighbouring neuron, a partial function recovery of the neural network is expected. Simulations with a spatially distributed deterministic neuron model support the presented hypothesis and provide insights into the role of DBS frequency and pulse width (duty cycle) in restoring the neural processing ability. The obtained results highlight the role of the phase difference between the neural input and the DBS pulse sequence in the neuron’s response to stimulation.

  • 21.
    Arabaci, Okan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Blockchain consensus mechanisms: the case of natural disasters2018Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Blockchain is described as a trustworthy distributed service for parties that do not fully trust each other. It enables business transactions to be handled without a third party or central governance. For this distributed and concurrent communication to work, a consensus mechanism needs to be implemented into the blockchain protocol. This mechanism will dictate how and when new blocks can be added and in some cases, by whom.

    The medical industry suffers from many informational inefficiencies. Data is scattered across many different databases and the lack of coordination often results in mishandling of the data. This is especially clear when a natural disaster hits and time is of the essence.

    The purpose of this thesis is to assess how much a blockchain solution and its consensus mechanism can resist unusual behavior before they behave erratically. This involves analyzing design parameters and translating parameters from a disaster into a simulation to run tests. Overall, this thesis will explore if blockchain is a compatible solution to the difficulties in natural disaster response. This was obtained by conducting a qualitative study and developing a prototype and simulating disaster parameters in the prototype blockchain network. A set of test cases was created.

    The results show that the resilience differs significantly depending on consensus mechanism. Key parameters include consensus finality, scalability, byzantine tolerance, performance and blockchain type. Blockchain is well suited to handle typical challenges in natural disaster response: it results in faster allocation of medical care and more accurate information collection, as well as in a system which allows seamlessly for the integration of external organizations in the blockchain network. 

  • 22.
    Aronsson, Erik
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Crondahl, Olle
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Maskininlärning applicerat på data över biståndsinsatser: En studie i hur prediktiva modeller kan tillämpas för analys på Sida2017Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The purpose of this master's thesis was to study if machine learning can be used asdecision support at the Swedish International Development Agency (Sida) in their work to provide financial aid. The aim was to examine the recurringphenomenon of increased number of aid disbursements towards the end of the year. A study and presentation of the data has been done to show the disbursementdistribution of Sida's operating departments. Moreover, qualitative interviews with different roles at Sida have been done to highlight the complexity of the agency and toexplain why and how different disbursement patterns occur. The approach has been to use classification models as well as regression models applied to data ofaid contributions from Sida's database. The classification models used were Decision Tree, k-Nearest Neighbour and Gradient Boosted Tree and thepurpose with the models was to illustrate which features of a contribution that are likely to be of importance for whether a disbursement occurs in December or earlier.The regression models used were linear models with the aim to predict if disbursements are likely to be delayed relative to the prognosis. The classificationmodel succeeded to point out three attributes that had influence on the classification result. The general conclusions of the report are that data ofcontributions generated in different IT-systems and various work routines at Sida's departments affect the quality of the data and the models’ accuracies negatively.Furthermore, insufficient amounts of data due to changes in Sida's information management has created difficulties when using data driven models to predict latedisbursements.

  • 23.
    Aronsson, Oscar
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Development and Implementation of an Advanced Storage Model2017Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The European process industry is in need of modernization if it shall retain its competitiveness on the growing global market and at the same time reduce the environmental impact that the industrial activities have. The concept behind industrial automation has been very successful in increasing the efficiency for the material handling process, but some industries still have a lack in the field of automation. One of these industries is the mining industry.

    ABB is currently working within the EU-funded project DISIRE in order to increase the amount of traceability and therefore also the potential of automation in the mining industry by introducing a flow simulation over the mine infrastructure. But one of the largest inherited problems that this industry has over other process industries is that the flow partly consists of a batch structure where the continuous flows of the product only takes place between bunkers and buffer zones.

    ABB has developed a Matlab simulation where these bunkers are modelled by a simple queue algorithm which does not take the blending or time delays of the ore into account. The main task of this master thesis was to investigate which different modelling approaches that could increase the accuracy of the simulation. The Cellular Automata (CA) were found to be most suitable modelling approach due to its simplicity and a Matlab toolbox were developed and implemented based on the theories behind CA. The results were partly evaluated with the results of an ongoing experiment at Luleå university and by comparison to theories of granular media movement. The CPU-time for the silo flow with 10.000 particles in a flat silo using a MacBook Pro 2.26GHz was about 8 seconds.

  • 24. Arvidsson, Åke
    et al.
    Rydén, Tobias
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Load transients in pooled cellular core network nodes2015In: Performance evaluation (Print), ISSN 0166-5316, E-ISSN 1872-745X, Vol. 90, p. 18-35Article in journal (Refereed)
  • 25.
    Aubry, Augusto
    et al.
    CNR, IREA.
    De Maio, Antonio
    Universit`a degli Studi di Napoli “Federico II”.
    Piezzo, Marco
    Universit`a degli Studi di Napoli “Federico II”.
    Naghsh, Mohammad Mahdi
    Isfahan University of Technology.
    Soltanalian, Mojtaba
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Stoica, Petre
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Cognitive Radar Waveform Design for Spectral Coexistence in Signal-Dependent Interference2014Conference paper (Refereed)
  • 26.
    Ayotte, John
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Dynamic positioning of a semi-submersible, multi-turbine wind power platform2015Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    As a growing market for offshore wind power has created a niche for deep-water installations, offshore floating wind solutions have become more and more viable as a renewable energy source. This technology is currently in development and as with many new technologies, many traditional design methods are found lacking. In the multi-turbine platform design investigated, turbine units are placed closely together to conserve material use and reduce cost, however with such tightly spaced turbines; wake interaction poses a threat to the productivity and the lifespan of the installation. In order to fully capitalize on the substantial increase in available wind energy far at sea, it is important that these floating parks operate in an optimal way. The platform investigated in this report sports 3, 6MW turbines which must be positioned such that wake interference is minimized; the platform must always bear a windward heading. 

    Maneuvering ocean going vessels has been practiced using automated dynamic positioning systems in the gas and oil industry for over 50 years, often employing submerged thrusters as a source of propulsion. These systems are mostly diesel powered and require extra operational maintenance, which would otherwise increase the cost and complexity of a floating wind farm. In this paper, it is suggested that the wind turbines themselves may be used to provide the thrust needed to correct the platform heading, thus eliminating the practical need for submerged thrusters. By controlling the blade pitch of the wind turbines, a turning moment (torque) can be exerted on the platform to correct heading (yaw) relative wind direction. Using the Hexicon H3-18MW platform as a starting point; hydrodynamic, aerodynamic and electromechanical properties of the system are explored, modeled and attempts at model predictive control are made. Preliminary results show that it is possible to control the H3’s position (in yaw) relative the wind using this novel method.

  • 27.
    Babu, Prabhu
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Spectral Analysis of Nonuniformly Sampled Data and Applications2012Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Signal acquisition, signal reconstruction and analysis of spectrum of the signal are the three most important steps in signal processing and they are found in almost all of the modern day hardware. In most of the signal processing hardware, the signal of interest is sampled at uniform intervals satisfying some conditions like Nyquist rate. However, in some cases the privilege of having uniformly sampled data is lost due to some constraints on the hardware resources. In this thesis an important problem of signal reconstruction and spectral analysis from nonuniformly sampled data is addressed and a variety of methods are presented. The proposed methods are tested via numerical experiments on both artificial and real-life data sets.

    The thesis starts with a brief review of methods available in the literature for signal reconstruction and spectral analysis from non uniformly sampled data. The methods discussed in the thesis are classified into two broad categories - dense and sparse methods, the classification is based on the kind of spectra for which they are applicable. Under dense spectral methods the main contribution of the thesis is a non-parametric approach named LIMES, which recovers the smooth spectrum from non uniformly sampled data. Apart from recovering the spectrum, LIMES also gives an estimate of the covariance matrix. Under sparse methods the two main contributions are methods named SPICE and LIKES - both of them are user parameter free sparse estimation methods applicable for line spectral estimation. The other important contributions are extensions of SPICE and LIKES to multivariate time series and array processing models, and a solution to the grid selection problem in sparse estimation of spectral-line parameters.

    The third and final part of the thesis contains applications of the methods discussed in the thesis to the problem of radial velocity data analysis for exoplanet detection. Apart from the exoplanet application, an application based on Sudoku, which is related to sparse parameter estimation, is also discussed.

  • 28.
    Babu, Prabhu
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Gudmundson, Erik
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Automatic cepstrum-based smoothing of the periodogram via cross-validation2008In: Proc. 16th European Signal Processing Conference, European Association for Signal Processing , 2008Conference paper (Refereed)
  • 29.
    Babu, Prabhu
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Gudmundson, Erik
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Optimal Preconditioning for Interpolation of Missing Data in a Band-Limited Sequence2008In: Proc. 42nd Asilomar Conference on Signals, Systems and Computers, Piscataway, NJ: IEEE , 2008, p. 561-565Conference paper (Refereed)
  • 30.
    Babu, Prabhu
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Li, Jian
    Linear Systems, Sparse Solutions, and Sudoku2010In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 17, no 1, p. 40-42Article in journal (Refereed)
  • 31.
    Babu, Prabhu
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    A combined linear programming-maximum likelihood approach to radial velocity data analysis for extrasolar planet detection2011In: ICASSP2011, the 36th International Conference on Acoustics, Speech and Signal Processing, Prague, Czech Republic, 2011, p. 4352-4355Conference paper (Refereed)
    Abstract [en]

    In this paper we introduce a new technique for estimating the parameters of the Keplerian model commonly used in radial velocity data analysis for extrasolar planet detection. The unknown parameters in the Keplerian model, namely eccentricity e, orbital frequency f, periastron passage time T, longitude of periastron., and radial velocity amplitude K are estimated by a new approach named SPICE (a semi-parametric iterative covariance-based estimation technique). SPICE enjoys global convergence, does not require selection of any hyperparameters, and is computationally efficient (indeed computing the SPICE estimates boils down to solving a numerically efficient linear program (LP)). The parameter estimates obtained from SPICE are then refined by means of a relaxation-based maximum likelihood algorithm (RELAX) and the significance of the resultant estimates is determined by a generalized likelihood ratio test (GLRT). A real-life radial velocity data set of the star HD 9446 is analyzed and the results obtained are compared with those reported in the literature.

  • 32.
    Babu, Prabhu
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Comments on "Iterative Estimation of Sinusoidal Signal Parameters"2010In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 17, no 12, p. 1022-1023Article in journal (Refereed)
  • 33.
    Babu, Prabhu
    et al.
    Department of Electronic and Computer Engineering, HKUST, Hong Kong.
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Connection between SPICE and Square-Root LASSO for sparse parameter estimation2014In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 95, p. 10-14Article in journal (Refereed)
    Abstract [en]

    In this note we show that the sparse estimation technique named Square-Root LASSO (SR-LASSO) is connected to a previously introduced method named SPICE. More concretely we prove that the SR-LASSO with a unit weighting factor is identical to SPICE. Furthermore we show via numerical simulations that the performance of the SR-LASSO changes insignificantly when the weighting factor is varied. SPICE stands for sparse iterative covariance-based estimation and LASSO for least absolute shrinkage and selection operator.

  • 34.
    Babu, Prabhu
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Sparse spectral-line estimation for nonuniformly sampled multivariate time series: SPICE, LIKES and MSBL2012In: 2012 Proceedings Of The 20th European Signal Processing Conference (EUSIPCO), 2012, p. 445-449Conference paper (Refereed)
    Abstract [en]

    In this paper we deal with the problem of spectral-line analysis ofnonuniformly sampled multivariate time series for which we introduce two methods: the first method named SPICE (sparse iterativecovariance based estimation) is based on a covariance fitting framework whereas the second method named LIKES (likelihood-basedestimation of sparse parameters) is a maximum likelihood technique. Both methods yield sparse spectral estimates and they donot require the choice of any hyperparameters. We numericallycompare the performance of SPICE and LIKES with that of the recently introduced method of multivariate sparse Bayesian learning(MSBL).

  • 35.
    Babu, Prabhu
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Spectral analysis of nonuniformly sampled data — a review2010In: Digital signal processing (Print), ISSN 1051-2004, E-ISSN 1095-4333, Vol. 20, no 2, p. 359-378Article in journal (Refereed)
  • 36.
    Babu, Prabhu
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Li, Jian
    University of Florida.
    Modeling radial velocity signals for exoplanet search applications2010In: The 7th International Conference on Informatics in Control, Automation and Robotics, Madeira, Portugal, 2010Conference paper (Refereed)
  • 37.
    Babu, Prabhu
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Li, Jian
    Chen, Zhaofu
    Ge, Jian
    Analysis of radial velocity data by a novel adaptive approach2010In: Astronomical Journal, ISSN 0004-6256, E-ISSN 1538-3881, Vol. 139, no 2, p. 783-793Article in journal (Refereed)
  • 38.
    Babu, Prabhu
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Marzetta, Thomas L.
    An IQML type algorithm for AR parameter estimation from noisy covariance sequences2009In: Proc. 17th European Signal Processing Conference, European Association for Signal Processing , 2009, p. 1022-1026Conference paper (Refereed)
  • 39. Barral, Joëlle K.
    et al.
    Gudmundson, Erik
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Stikov, Nikola
    Etezadi-Amoli, Maryam
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Nishimura, Dwight G.
    A Robust Methodology for In Vivo T1 Mapping2010In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 64, no 4, p. 1057-1067Article in journal (Refereed)
  • 40. Barral, Joëlle K.
    et al.
    Stikov, Nikola
    Gudmundson, Erik
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Nishimura, Dwight G.
    Skin T1 Mapping at 1.5T, 3T, and 7T2009In: Proceedings of the ISMRM 2009, Honolulu, Hawaii, USA, 2009Conference paper (Refereed)
  • 41.
    Bartelmess, Johan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Compression efficiency of different picture coding structures in High Efficiency Video Coding (HEVC)2016Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Video content is expected to account for 80 percent of all Internet traffic in 2019. There is therefore an increasing need for better video compression methods, to decrease the use of internet bandwidth. One way of achieving high video compression is to predict pixel values for a video frame based on prior and succeeding pictures in the video. The H.265 video compression standard supports this method, and in particular makes it possible to specify in which order pictures are coded, and which pictures are predicted from which. The coding order is specified for Groups Of Pictures (GOP), where a number of pictures are grouped together and predicted from each other in a specified order. This thesis evaluates how the GOPs should be structured, for instance in terms of sizing, to maximize the compression efficiency relative to the video quality. It also investigates the effect of multiple reference pictures, a functionality that enables the picture that renders the best prediction to be selected. The results show that the largest tested GOP size of 32 pictures is preferable for all tested video characteristics, and that support for multiple reference pictures renders a similar increase in compression efficiency for all GOP sizes.

  • 42. Beck, Amir
    et al.
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Li, Jian
    Exact and approximate solutions of source localization problems2008In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 56, no 5, p. 1770-1778Article in journal (Refereed)
    Abstract [en]

    We consider least squares (LS) approaches for locating a radiating source from range measurements (which we call R-LS) or from range-difference measurements (RD-LS) collected using an array of passive sensors. We also consider LS approaches based on squared range observations (SR-LS) and based on squared range-difference measurements (SRD-LS). Despite the fact that the resulting optimization problems are nonconvex, we provide exact solution procedures for efficiently computing the SR-LS and SRD-LS estimates. Numerical simulations suggest that the exact SR-LS and SRD-LS estimates outperform existing approximations of the SR-LS and SRD-LS solutions as well as approximations of the R-LS and RD-LS solutions which are based on a semidefinite relaxation.

  • 43.
    Berg, Niklas
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Ice navigation with ice compressionin the Gulf of Finland2010Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Safe winter navigation is a hot topic. Not only is the traffic density increasing but theenvironmental considerations are also getting bigger. An oil leakage from a big oiltanker can be of catastrophic proportions in the wrong area and more trafficincreases the risk of an accident. A project that aims for safer winter navigation isSafeWIN. The aim of this project is to develop a forecasting system for compressiveice and thus make winter navigation safer.This thesis is part of above mentioned project and aims to investigate what influenceice compression and ice class has on winter navigation. Vessels are exclusivelyAFRAMAX size tankers sailing on Primorsk in the Gulf of Finland during 2006. Transitdata comes from AIS tracks recorded by the Swedish Maritime Administration. Adatabase with tanker transits has been created and this information is the source forthe studies in this thesis. Included in the database are wind data, ice particulars andtransit information such as speed, and time at different activities during the transit.Average values for a transit has been investigated for comparison and to get a pictureof an average transit.Velocity, waiting time and time with assisting icebreaker are parameters that arebelieved to show how a tanker performs in winter navigation. These parameters arecompared with ice compression and ice class separately to see if there is acorrelation. Ice compression has also been investigated for correlation towards windforce to see if stronger wind generates stronger compression.Using the velocity in different ice compressions an estimate of ice resistance that stemfrom ice compression has been extracted by means of Lindqvist’s formula.

  • 44.
    Bhikkaji, Bharath
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Model Reduction and Parameter Estimation for Diffusion Systems2004Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Diffusion is a phenomenon in which particles move from regions of higher density to regions of lower density. Many physical systems, in fields as diverse as plant biology and finance, are known to involve diffusion phenomena. Typically, diffusion systems are modeled by partial differential equations (PDEs), which include certain parameters. These parameters characterize a given diffusion system. Therefore, for both modeling and simulation of a diffusion system, one has to either know or determine these parameters. Moreover, as PDEs are infinite order dynamic systems, for computational purposes one has to approximate them by a finite order model. In this thesis, we investigate these two issues of model reduction and parameter estimation by considering certain specific cases of heat diffusion systems.

    We first address model reduction by considering two specific cases of heat diffusion systems. The first case is a one-dimensional heat diffusion across a homogeneous wall, and the second case is a two-dimensional heat diffusion across a homogeneous rectangular plate. In the one-dimensional case we construct finite order approximations by using some well known PDE solvers and evaluate their effectiveness in approximating the true system. We also construct certain other alternative approximations for the one-dimensional diffusion system by exploiting the different modal structures inherently present in it. For the two-dimensional heat diffusion system, we construct finite order approximations first using the standard finite difference approximation (FD) scheme, and then refine the FD approximation by using its asymptotic limit.

    As for parameter estimation, we consider the same one-dimensional heat diffusion system, as in model reduction. We estimate the parameters involved, first using the standard batch estimation technique. The convergence of the estimates are investigated both numerically and theoretically. We also estimate the parameters of the one-dimensional heat diffusion system recursively, initially by adopting the standard recursive prediction error method (RPEM), and later by using two different recursive algorithms devised in the frequency domain. The convergence of the frequency domain recursive estimates is also investigated.

    List of papers
    1. Reduced order models for diffusion systems
    Open this publication in new window or tab >>Reduced order models for diffusion systems
    2001 In: International Journal of Control, Vol. 75, no 15, p. 1543-1557Article in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-91720 (URN)
    Available from: 2004-05-06 Created: 2004-05-06Bibliographically approved
    2. Reduced order models for diffusion systems using singular perturbations
    Open this publication in new window or tab >>Reduced order models for diffusion systems using singular perturbations
    2001 In: Energy and Buildings, Vol. 33, no 8, p. 769-781Article in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-91721 (URN)
    Available from: 2004-05-06 Created: 2004-05-06Bibliographically approved
    3. Reduced order model for a two-dimensional diffusion system
    Open this publication in new window or tab >>Reduced order model for a two-dimensional diffusion system
    In: International Journal of ControlArticle in journal (Refereed) Submitted
    Identifiers
    urn:nbn:se:uu:diva-91722 (URN)
    Available from: 2004-05-06 Created: 2004-05-06Bibliographically approved
    4. Reduced order models for diffusion systems via Collocation methods
    Open this publication in new window or tab >>Reduced order models for diffusion systems via Collocation methods
    2000 In: Proc of 12th IFAC Symposium on System Identification, Santa Barbara, CA, USA, JuneArticle in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-91723 (URN)
    Available from: 2004-05-06 Created: 2004-05-06Bibliographically approved
    5. Bias and variance of parameter estimates of a one-dimensional heat diffusion system
    Open this publication in new window or tab >>Bias and variance of parameter estimates of a one-dimensional heat diffusion system
    2002 In: Proc of 15th IFAC Congress, Barcelona, Spain, JulyArticle in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-91724 (URN)
    Available from: 2004-05-06 Created: 2004-05-06Bibliographically approved
    6. Recursive algorithm for estimating parameters in a one-dimensional heat diffusion system
    Open this publication in new window or tab >>Recursive algorithm for estimating parameters in a one-dimensional heat diffusion system
    2002 In: Proc of Reglermöte, Linköping, SwedenArticle in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-91725 (URN)
    Available from: 2004-05-06 Created: 2004-05-06Bibliographically approved
    7. Recursive algorithms for estimating parameters in a one-dimensional diffusion system: derivation and implementation
    Open this publication in new window or tab >>Recursive algorithms for estimating parameters in a one-dimensional diffusion system: derivation and implementation
    In: International Journal of Adaptive Control and Signal ProcessingArticle in journal (Refereed) Submitted
    Identifiers
    urn:nbn:se:uu:diva-91726 (URN)
    Available from: 2004-05-06 Created: 2004-05-06Bibliographically approved
    8. Recursive algorithms for estimating parameters in a one-dimensional diffusion system: analysis
    Open this publication in new window or tab >>Recursive algorithms for estimating parameters in a one-dimensional diffusion system: analysis
    In: International Journal of ControlArticle in journal (Refereed) Submitted
    Identifiers
    urn:nbn:se:uu:diva-91727 (URN)
    Available from: 2004-05-06 Created: 2004-05-06Bibliographically approved
  • 45.
    Bhikkaji, Bharath
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Model reduction for diffusion systems2000Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Diffusion phenomena has been studied with a lot of interest, for a long time, due to its historical and practical significance. In the recent days it has thrown a lot of interest among control engineers, as more and more practical systems, varying from stock markets to environmental pollution, have been observed to involve diffusion.

    Diffusion systems are normally modeled by linear partial differential equations (LPDEs) of the form

    (1)   ∂T(x,t)/∂t = £T(x,t),

    where £ is a second order linear spatial differential operator and T(x,t) is the physical quantity, whose variations in the spatial domain cause diffusion. To characterise diffusion phenomena, one has to obtain the solution of (1) either analytically or numerically. Note that, since (1) involves a second order spatial operator and a first order time derivative, one needs at least two boundary conditions in the spatial domain, x, and an initial condition at time t = 0, for determining T(x,t).

    LPDEs of the type (1) can be interpreted as infinite order linear time invariant (LTI) systems with inputs as boundary conditions. To compute the solution of (1) numerically, one has to approximate, explicitly or implicitly, the underlying infinite order system by a finite order system. Any numerical scheme, which computes the solution of (1), essentially approximates the underlying infinite order LTI system by a finite order LTI system. The efficiency of the approximation, for a given problem, varies for the different numerical schemes.

    In this thesis, we make an attempt to explore more about diffusion systems in general. As a starting point, we consider a simple case of one-dimensional heat diffusion across a homogeneous region. The resulting LPDE is first shown explicitly to be an infinite order dynamical system. An approximate solution is computed from a finite order approximation of the true infinite order dynamical system. In this thesis, we first construct the finite order approximations using certain standard PDE solvers based on Chebyshev polynomials. From these finite order approximations we choose the best one, from a model reduction perspective, and use it as a benchmark model. We later construct two more approximate models, by exploiting the given structure of the problem and we show by simulations that these models perform better than the chosen benchmark.

  • 46.
    Bijl, Hildo
    et al.
    Delft University of Technology.
    Schön, Thomas B.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Optimal controller/observer gains of discounted-cost LQG systems2018In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836Article in journal (Refereed)
    Abstract [en]

    The linear-quadratic-Gaussian (LQG) control paradigm is well-known in literature. The strategy of minimizing the cost function is available, both for the case where the state is fully known and where it is estimated through an observer. The situation is different when the cost function has an exponential discount factor, also known as a prescribed degree of stability. In this case, the optimal control strategy is only available when the state is fully known. This paper builds onward from that result, deriving an optimal control strategy when working with an estimated state. Expressions for the resulting optimal expected cost are also given. The result is illustrated via an experimental validation.

  • 47. Bijl, Hildo
    et al.
    van Wingerden, Jan-Willem
    Schön, Thomas B.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Verhaegen, Michel
    Mean and variance of the LQG cost function2016In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 67, p. 216-223Article in journal (Refereed)
    Abstract [en]

    Linear Quadratic Gaussian (LQG) systems are well-understood and methods to minimize the expected cost are readily available. Less is known about the statistical properties of the resulting cost function. The contribution of this paper is a set of analytic expressions for the mean and variance of the LQG cost function. These expressions are derived using two different methods, one using solutions to Lyapunov equations and the other using only matrix exponentials. Both the discounted and the non-discounted cost function are considered, as well as the finite-time and the infinite-time cost function. The derived expressions are successfully applied to an example system to reduce the probability of the cost exceeding a given threshold.

  • 48.
    Binggeli, Christian