Publications from the Department of Electrical Engineering
Automatic ControlCommunication Systems
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Information Coding
Integrated Circuits and Systems
Vehicular Systems
Latest PhD theses
The protection of confidential data is a fundamental need in the society in which we live. This task becomes more relevant when observing that every day, data traffic increases exponentially, as well as the number of attacks on the telecommunication infra-structure. From the natural sciences, it has been strongly argued that quantum communication has great potential to solve this problem, to such an extent that various governmental and industrial entities believe the protection provided by quantum communications will be an important layer in the field of information security in the next decades. However, integrating quantum technologies both in current optical networks and in industrial systems is not a trivial task, taking into account that a large part of current quantum optical systems are based on bulk optical devices, which could become an important limitation. Throughout this thesis we present an all-in-fiber optical platform that allows a wide range of tasks that aim to take a step forward in terms of generation and detection of photonic states. Among the main features, the generation and detection of photonic quantum states carrying orbital angular momentum stand out.
The platform can also be configured for the generation of random numbers from quantum mechanical measurements, a central aspect in future information tasks.
Our scheme is based on the use of new space-division-multiplexing (SDM) technologies such as few-mode-fibers and photonic lanterns. Furthermore, our platform can also be scaled to high dimensions, it operates in 1550 nm (telecommunications band) and all the components used for its implementation are commercially available. The results presented in this thesis can be a solid alternative to guarantee the compatibility of new SDM technologies in emerging experiments on optical networks and open up new possibilities for quantum communication.
@phdthesis{diva2:1797425,
author = {Alarcón, Alvaro},
title = {{All-Fiber System for Photonic States Carrying Orbital Angular Momentum:
A Platform for Classical and Quantum Information Processing}},
school = {Linköping University},
type = {{Linköping Studies in Science and Technology. Dissertations No. 2340}},
year = {2023},
address = {Sweden},
}
In Model Predictive Control (MPC), optimization problems are solved recurrently to produce control actions. When MPC is used in real time to control safety-critical systems, it is important to solve these optimization problems with guarantees on the worst-case execution time. In this thesis, we take aim at such worst-case guarantees through two complementary approaches:
(i) By developing methods that determine exact worst-case bounds on the computational complexity and execution time for deployed optimization solvers.
(ii) By developing efficient optimization solvers that are tailored for the given application and hardware at hand.
We focus on linear MPC, which means that the optimization problems in question are quadratic programs (QPs) that depend on parameters such as system states and reference signals. For solving such QPs, we consider active-set methods: a popular class of optimization algorithms used in real-time applications.
The first part of the thesis concerns complexity certification of well-established active-set methods. First, we propose a certification framework that determines the sequence of subproblems that a class of active-set algorithms needs to solve, for every possible QP instance that might arise from a given linear MPC problem (i.e., for every possible state and reference signal). By knowing these sequences, one can exactly bound the number of iterations and/or floating-point operations that are required to compute a solution. In a second contribution, we use this framework to determine the exact worst-case execution time (WCET) for linear MPC. This requires factors such as hardware and software implementation/compilation to be accounted for in the analysis. The framework is further extended in a third contribution by accounting for internal numerical errors in the solver that is certified. In a similar vein, a fourth contribution extends the framework to handle proximal-point iterations, which can be used to improve the numerical stability of QP solvers, furthering their reliability.
The second part of the thesis concerns efficient solvers for real-time MPC. We propose an efficient active-set solver that is contained in the above-mentioned complexity-certification framework. In addition to being real-time certifiable, we show that the solver is efficient, simple to implement, can easily be warm-started, and is numerically stable, all of which are important properties for a solver that is used in real-time MPC applications. As a final contribution, we use this solver to exemplify how the proposed complexity-certification framework developed in the first part can be used to tailor active-set solvers for a given linear MPC application. Specifically, we do this by constructing and certifying parameter-varying initializations of the solver.
@phdthesis{diva2:1755033,
author = {Arnström, Daniel},
title = {{Real-Time Certified MPC:
Reliable Active-Set QP Solvers}},
school = {Linköping University},
type = {{Linköping Studies in Science and Technology. Dissertations No. 2324}},
year = {2023},
address = {Sweden},
}
The trend of automation in industry, and in the society in general, is something that probably all of us have noticed. The mining industry is no exception to this trend, and there exists a vision of having completely automated mines with all processes monitored and controlled through a higher level optimization goal. For this vision, access to a reliable positioning system has been identified a prerequisite. Underground mines posses extraordinary premises for localization, due to the harsh, unstructured and ever changing environment, where existing localization solutions struggle with accuracy and reliability over time.
This thesis addresses the problem of achieving accurate, robust and consistent position estimates for long-term autonomy of vehicles operating in an underground mining environment. The focus is on onboard positioning solutions utilizing sensor fusion within the probabilistic filtering framework, with extra emphasis on the characteristics of lidar data. Contributions are in the areas of improved state estimation algorithms, more efficient lidar data processing and development of models for changing environments. The problem descriptions and ideas in this thesis are sprung from underground localization issues, but many of the resulting solutions and methods are valid beyond this application.
In this thesis, internal localization algorithms and data processing techniques are analyzed in detail. The effects of tuning the parameters in an unscented Kalman filter are examined and guidelines for choosing suitable values are suggested. Proper parameter values are shown to substantially improve the position estimates for the underground application. Robust and efficient processing of lidar data is explored both through analysis of the information contribution of individual laser rays, and through preprocessing in terms of feature extraction. Methods suitable for available hardware are suggested, and it is shown how it is possible to maintain consistency in the state estimates with less computations.
Changes in the environment can be devastating for a localization system when characteristics of the observations no longer matches the provided map. One way to manage this is to extend the localization problem to simultaneous localization and mapping (slam). In its standard formulation, slam assumes a truly static surrounding. In this thesis a feature based multi-hypothesis map representation is developed that allows encoding of changes in the environment. The representation is verified to perform well for localization in scenarios where landmarks can attain one of many possible positions. Automatic creation of such maps are suggested with methods completely integrated with the slam framework. This results in a multi-hypothesis slam concept that can discover and adapt to changes in the operation area while at the same time producing consistent state estimates.
This thesis provides general insights in lidar data processing and state estimation in changing environments. For the underground mine application specifically, different methods presented in this thesis target different aspects of the higher goal of achieving robust and accurate position estimates. Together they present a collective view of how to design localization systems that produce reliable estimates for underground mining environments.
@phdthesis{diva2:1752033,
author = {Nielsen, Kristin},
title = {{Localization for Autonomous Vehicles in Underground Mines}},
school = {Linköping University},
type = {{Linköping Studies in Science and Technology. Dissertations No. 2318}},
year = {2023},
address = {Sweden},
}
In this thesis, we focus on vulnerabilities and robustness of two wireless communication technologies: global navigation satellite system (GNSS), a technology that provides position-velocity-time information, and massive multiple-input-multiple-output (MIMO), a core cellular 5G technology. In particular, we investigate spoofing and jamming attacks to GNSS and massive MIMO, respectively, and the robust massive MIMO receiver against impulsive noises. In this context, spoofing refers to the situation in which a receiver identifies falsified signals, that are transmitted by the spoofers, as legitimate or trustable signals.
Jamming, on the other hand, refers to the transmission of radio signals that disrupt communications by decreasing the signal to interference plus noise ratio (SINR) on the receiver side.
The reason why we investigate impulsive noises is that the standard wireless receivers assume that the noise has Gaussian distribution. However, the impulsive noises may appear in any communication link. The difference between impulsive noises and standard Gaussian noises is that it is more likely to observe outliers in impulsive noises. Therefore, we question whether the standard Gaussian receivers are robust against impulsive noises and design robust receivers against impulsive noises.
More specifically, in paper A we analyze the effects of distributed jammers on massive MIMO and answer the following questions: Is massive MIMO more robust to distributed jammers compared with previous generation's cellular networks? Which jamming attack strategies are the best from the jammer's perspective, and can the jamming power be spread over space to achieve more harmful attacks?
In paper B, we propose a detector for GNSS receivers that is able to detect multiple spoofers without having any prior information about the attack strategy or the number of spoofers in the environment.
In paper C and D, we design robust receivers for massive MIMO against impulsive noise. In paper C, we model the noise having a Cauchy distribution and present a channel estimation technique, achievable rates and soft-decision metrics for coded signals. The main observation in paper C is that the proposed receiver works well in the presence of Cauchy and Gaussian noises, although the standard Gaussian receiver performs very bad when the noise has Cauchy distribution. In paper D, we compare two types of receivers, the Gaussian-mixture and the Cauchy-based, when the noise has symmetric alpha-stable (SαS) distributions. Based on the numerical results, the Gaussian-mixture receiver outperforms the Cauchy-based receiver.
@phdthesis{diva2:1747809,
author = {Gülgün, Ziya},
title = {{GNSS and Massive MIMO:
Spoofing, Jamming and Robust Receiver Design for Impulsive Noise}},
school = {Linköping University},
type = {{Linköping Studies in Science and Technology. Dissertations No. 2310}},
year = {2023},
address = {Sweden},
}
A mobile robot, instructed by a human operator, acts in an environment with many other objects. However, for an autonomous robot, human instructions should be minimal and only high-level instructions, such as the ultimate task or destination. In order to increase the level of autonomy, it has become a foremost objective to mimic human vision using neural networks that take a stream of images as input and learn a specific computer vision task from large amounts of data. In this thesis, we explore several different models for surround sensing, each of which contributes to a higher understanding of the environment being possible.
As its first contribution, this thesis presents an object tracking method for video sequences, which is a crucial component in a perception system. This method predicts a fine-grained mask to separate the pixels corresponding to the target from those corresponding to the background. Rather than tracking location and size, the method tracks the initial pixels assigned to the target in this so-called video object segmentation. For subsequent time steps, the goal is to learn how the target looks using features from a neural network. We named our method A-GAME, based on the generative modeling of deep feature space, separating target and background appearances.
In the second contribution of this thesis, we detect, track, and segment all objects from a set of predefined object classes. This information is how the robot increases its capabilities to perceive the surroundings. We experiment with a graph neural network to weigh all new detections and existing tracks. This model outperforms prior works by separating visually, and semantically similar objects frame by frame.
The third contribution investigates one limitation of anchor-based detectors, which classify pre-defined bounding boxes as either negative or positive and thus provide a limited set of handled object shapes. One idea is to learn an alternative instance representation. We experiment with a neural network that predicts the distance to the nearest object contour in different directions from each pixel. The network then computes an approximated signed distance function containing the respective instance information.
Last, this thesis studies a concept within model validation. We observed that overfitting could increase performance on benchmarks. However, this opportunity is insipid for sensing systems in practice since measurements, such as length or angles, are quantities that explain the environment. The fourth contribution of this thesis is an extended validation technique for camera calibration. This technique uses a statistical model for each error difference between an observed value and a corresponding prediction of the projective model. We compute a test over the differences and detect if the projective model is incorrect.
@phdthesis{diva2:1745714,
author = {Brissman, Emil},
title = {{Learning to Analyze Visual Data Streams for Environment Perception}},
school = {Linköping University},
type = {{Linköping Studies in Science and Technology. Dissertations No. 2283}},
year = {2023},
address = {Sweden},
}
As technology continues to advance, the interest in the relief of humans from tedious or dangerous tasks through automation increases. Some of the tasks that have received increasing attention are autonomous driving, disaster relief, and forestry inspection. Developing and deploying an autonomous robotic system to this type of unconstrained environments —in a safe way— is highly challenging. The system requires precise control and high-level decision making. Both of which require a robust and reliable perception system to understand the surroundings correctly.
The main purpose of perception is to extract meaningful information from the environment, be it in the form of 3D maps, dense classification of the type of object and surfaces, or high-level information about the position and direction of moving objects. Depending on the limitations and application of the system, various types of sensors can be used: lidars, to collect sparse depth information; cameras, to collect dense information for different parts of the visual spectra, of-ten the red-green-blue (RGB) bands; Inertial Measurements Units (IMUs), to estimate the ego motion; microphones, to interact and respond to humans; GPS receivers, to get global position information; just to mention a few.
This thesis investigates some of the necessities to approach the requirements of this type of system. Specifically, focusing on data-driven approaches, that is, machine learning, which has been shown time and again to be the main competitor for high-performance perception tasks in recent years. Although precision requirements might be high in industrial production plants, the environment is relatively controlled and the task is fixed. Instead, this thesis is studying some of the aspects necessary for complex, unconstrained environments, primarily outdoors and potentially near humans or other systems. The term in the wild refers exactly to the unconstrained nature of these environments, where the system can easily encounter something previously unseen and where the system might interact with unknowing humans. Some examples of environments are: city traffic, disaster relief scenarios, and dense forests.
This thesis will mainly focus on the following three key aspects necessary to handle the types of tasks and situations that could occur in the wild: 1) generalizing to a new environment, 2) adapting to new tasks and requirements, and 3) modeling uncertainty in the perception system.
First, a robotic system should be able to generalize to new environments and still function reliably. Papers B and G address this by using an intermediate representation to allow the system to handle much more diverse types of environment than otherwise possible. Paper B also investigates how robust the proposed autonomous driving system was to incorrect predictions, which is one of the likely results of changing the environment.
Second, a robot should be sufficiently adaptive to allow it to learn new tasks without forgetting the previous ones. Paper E proposed a way to allow incrementally adding new semantic classes to a trained model without access to the previous training data. The approach is based on utilizing the uncertainty in the predictions to model the unknown classes, marked as background.
Finally, the perception system will always be partially flawed, either because of the lack of modeling capabilities or because of ambiguities in the sensor data. To properly take this into account, it is fundamental that the system has the ability to estimate the certainty in the predictions. Paper F proposed a method for predicting the uncertainty in the model predictions when interpolating sparse data. Paper G addresses the ambiguities that exist when estimating the 3D pose of a human from a single camera image.
@phdthesis{diva2:1740415,
author = {Holmquist, Karl},
title = {{Data-Driven Robot Perception in the Wild}},
school = {Linköping University},
type = {{Linköping Studies in Science and Technology. Dissertations No. 2293}},
year = {2023},
address = {Sweden},
}
One of the scopes of Systems Biology is to propose mathematical models that best capture the dynamic behavior of intra-cellular processes. In this regard, the last two decades have brought up a shift in the field, with technological advances now allowing researchers to access a wide range of high-throughput technologies at an affordable cost. These techniques allow to simultaneously interrogate thousands of variables, such as genome-wide transcriptomics and proteomics. However, parallel to these technological advances, there is a growing need for mathematical models that are suited to integrate measurements obtained from different cellular processes.
In this thesis we aim to model combinations of three commonly used high-throughput data: epigenetic (namely ATAC-seq and DNA methylation), transcriptomic (RNA-seq) and proteomic data (MASS-spectrometry). In the first work we analyze paired ATAC-seq and RNA-seq data to integrate measurements of (i) chromatin openness, (ii) transcription factors (TFs) availability and (iii) gene expression. To model these data, we use elementary causal motifs, a class of mathematical models which is suited to represent causal interactions between three nodes. Indeed, our analysis shows that the elementary causal motifs in the data are enriched for biologically relevant TF-gene interactions. Moreover, a significant overlap is observed between the causal motifs identified in datasets representing similar cell stimuli, suggesting that causal motifs represent a robust biological signal.
This work is then extended to include another class of high-throughput data: MASS-spectrometry. More precisely, we propose a framework to model the flow of events that goes from chromatin remodeling to splice variants expression, and from splice variants to protein synthesis. As the underlying graph becomes more complex than the previous case, a more general mathematical framework is considered: Bayesian networks. Interestingly, this work shows that most putative associations between chromatin regions, splice variants and proteins that have been gathered by scientific community so far, are supported by the data. Moreover, similarly to the previous work, the causal interactions identified in the data highlight relevant biological features; more precisely, causal chains between chromatin regions, splice variants and proteins are enriched for splice variants that have a major role in protein synthesis.
From a technical point of view, causal motifs are characterized by a property known as conditional independence, which can be used to identify causal interactions in the data. However, particularly when the data available is limited, it is challenging to assess conditional independencies in the data. It is therefore of interest to investigate the existence of properties that allow us to predict conditional independence. In particular, in our work we propose two properties: structural balance and inverse balance, which are closely connected to what is known in the literature as positive association and multivariate total positivity of order 2 (MTP2), respectively. Our analysis shows that both heuristics are useful in predicting conditional independence, both from a theoretical perspective and in experimental data.
Lastly, a network-based approach is used to integrate DNA methylation and RNA-seq in a case-control study centered around multiple sclerosis, in order to identify common regulatory patterns in DNA methylation and gene expression during the course of pregnancy. The strategy is based on the rationale that proteins that are interconnected in the protein-protein network are more likely to be involved in similar cellular functions. Indeed, the analysis highlights that similar pathways are altered at epigenetic and transcriptomic level, leading to a set of genes that are likely involved in the modification of the disease symptoms that is observed during pregnancy.
@phdthesis{diva2:1729981,
author = {Zenere, Alberto},
title = {{Integration of epigenetic, transcriptomic and proteomic data}},
school = {Linköping University},
type = {{Linköping Studies in Science and Technology. Dissertations No. 2294}},
year = {2023},
address = {Sweden},
}
Transport is an integral part of society and one of its basic prerequisites. Society is now facing a transition as it must go from dependence on fossil fuels to sustainability. Despite large investments by the vehicle manufacturers, the transition needs to be accelerated for the two-degree (Celsius) target to be reached, which requires new innovations and solutions.
The development of computers has led to efficient software being available today to numerically solve optimization problems, which enables mathematical modeling and optimization as a systematic problem-solving method. However, taking advantage of the numerical solvers requires specialized knowledge and is a barrier for many engineers. To overcome this and make the problem-solving methodology available, tools that bridge the gap between the engineer’s problem and the numerical solvers are needed.
The dissertation covers the complete chain from problem to solution, with methods and tools that support the problem-solving process. Software for optimal control is investigated with the aim of making the numerical solvers available to the user. The result is a design based on the introduction of a domain-specific programming language. It makes it possible to automatically reformulate the user’s problem into a form that the computer can handle, while making the program more user-friendly by reducing the difference between the problem domain and the computer’s domain. The result has been developed together with the software Yop, which is used by engineers and researchers nationally and internationally to solve control engineering problems, in academia as well as in industry.
The software is used to investigate whether an electrified powertrain can be made more efficient by equipping the diesel engine with a larger and more efficient turbocharger, at the expense of increased inertia. The result indicates a gain and that the increased inertia can be compensated by the electric motor. As part of the work, a diesel engine model has been developed, where it has been investigated how relevant effects for turbocharger selection can be included in a way suitable for optimal control. The result is a validated and dynamic diesel engine model that has been made available to the research community through publications and open-source code.
@phdthesis{diva2:1709608,
author = {Leek, Viktor},
title = {{Optimal Control for Energy Efficient Vehicle Propulsion:
Methodology, Application, and Tools}},
school = {Linköping University},
type = {{Linköping Studies in Science and Technology. Dissertations No. 2270}},
year = {2022},
address = {Sweden},
}
As marine vessels are becoming increasingly automated, having accurate simulation models available is turning into an absolute necessity. This holds both for the facilitation of development and for achieving satisfactory model-based control. Such models can be obtained through system identification, and in this thesis, particular emphasis is given to experiment design and parameter estimation, which constitute two central steps in the system identification process. The analysis is carried out for a special class of nonlinear regression models called second-order modulus models, which is a type of model that is often used for describing nonlinear hydrodynamic effects in greybox identification of ships.
First, it is demonstrated that the accuracy of an instrumental variable (iv) estimator can be improved by conducting experiments where the input signal has a static offset of sufficient amplitude and the instruments are forced to have zero mean. This two-step procedure is shown to give consistent estimators for second-order modulus models in cases where an off-the-shelf applied iv method does not, in particular when measurement uncertainty is taken into account. Further, it is shown that the possibility of obtaining consistent parameter estimators for models of this type depends on how the process disturbances enter the system and on the amount of prior knowledge that is available about the disturbances’ probability distributions. In cases where the first-order moments are known, the aforementioned approach gives consistent estimators even when disturbances enter the system before the nonlinearity. To obtain consistent estimators in cases where the first-order moments are unknown, a framework for estimating auxiliary nuisance parameters that depend on the disturbances’ first and second-order moments is suggested. This can be done by describing the process disturbances as stationary stochastic processes in an inertial frame and utilizing the fact that their effect on a vessel depends on the vessel’s attitude.
After this, the attention is more clearly focused on experiment design, and a systematic approach for choosing the most informative combination of independent sub-experiments out of a predefined set of candidates is proposed. Further, a technique to account for an upcoming subtraction of the instruments’ mean during the experiment design is suggested, and the consequences of various ways of having the mean subtracted are explored. Additionally, it is shown how the dictionary-based method for finding an excitation signal can be combined with a motion-planning framework to obtain a trajectory that is both informative and spatially feasible.
The suggested methods are evaluated in experimental work and show promising results on both simulated and real data, the latter from a full-scale marine vessel as well as a small-scale model ship.
@phdthesis{diva2:1706923,
author = {Ljungberg, Fredrik},
title = {{Identification of Nonlinear Marine Systems}},
school = {Linköping University},
type = {{Linköping Studies in Science and Technology. Dissertations No. 2258}},
year = {2022},
address = {Sweden},
}
Portable and implantable electronics are becoming increasingly important in the healthcare sector. One of the challenges is to guarantee stable systems for longer periods of time. If we consider applications such as electrical nerve stimulation or implanted ion pumps, the requirements for, e.g., levels, duration, etc., vary over time, and there may be a need to be able to remotely reconfigure devices, which in turn extends the life of the implant.
This dissertation studies the efficient healthcare wireless network, wireless power supply, and its use in implantable biomedical systems. The body-area network (BAN) and near-field communication (NFC) are studied. Several Application Specific Integrated Circuits (ASICs) solutions are implemented, manufactured, and characterized.
ASICs for portable and implantable sensors and actuators still have high research value. In addition, advances in flexible, implantable inductive coils, along with near-field energy harvesting technology, have driven the development of wireless, implantable devices. The ASICs are used to initiate and generate controlled signals that govern actuators in multiple locations in the body. Electronics specifications may include operations related to tissue-specific absorption rate, stimulation duration or levels to avoid tissue temperature rise, power transmission distance, and controlled current or voltage drivers.
In this work, the feasibility of BAN as a healthcare network has been investigated. The functionality of an existing BodyCom communication system was expanded, sensors and actuators are added. The system enables data transfer between several sensor nodes placed on a human body. In BAN, the information is propagated along the skin in a capacitive, electric field. The network was demonstrated with a sensor node (stretchable glove) and implantable ion pump (actuator) for drug delivery. With the stretchable glove, movement patterns could be captured, and ions were delivered from a reservoir in the ion pump.
Furthermore, NFC is studied, and the advantages of NFC compared to BAN are discussed. An ST Microelectronics system was used together with a planar coil developed on a flexible plastic substrate to demonstrate the concept. The efficiency between the primary and secondary coils is measured and characterized. A temperature sensor was chosen as the implantable sensor, and the signal strength at several distances between the primary and secondary inductive coils is characterized.
The next phase of the work focuses on the implementation of ASICs. The first proposed system describes a wirelessly powered peripheral nerve stimulator. The system contains a full-wave rectifier-based energy harvester that operates at 13.56 MHz with the option to select a stimulation current. The stimulation current can be selected in the range of 15 nA up to 1 mA. A reference clock is extracted from the AC input and used to synchronize the data and generate the required control. In addition, a state machine is used to generate the time parameters required for cathodic and anodic nerve stimulation. The design is fabricated in the standard 180 nm CMOS process and is 0.22 mm2 large, excluding an integrated 3.6 nF capacitor. The chip is measured to verify the energy harvester, power cells, and timing control logic with an input amplitude |VAC | = 3 V and a load of 1 kΩ.
Subsequently, a multichannel system was developed that makes it possible to dynamically set the biphase simulation profile. The amplitude modulated data packets transmitted through the inductively coupled interface are demodulated, and the information is extracted. The data stream is then used to generate control signals that activate the desired configuration (channel, stream, time, etc.).
@phdthesis{diva2:1698749,
author = {Kifle, Yonatan Habteslassie},
title = {{Studies On Design of Near-Field Wireless-Powered Biphase Implantable Stimulators}},
school = {Linköping University},
type = {{Linköping Studies in Science and Technology. Dissertations No. 2237}},
year = {2022},
address = {Sweden},
}
Last updated: 2020-10-01