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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},
}
Massive MIMO (multiple-input-multiple-output) is a technology that uses an antenna array with a massive number of antennas at the wireless base station. It has shown widespread benefit and has become an inescapable solution for the future of wireless communication. The mainstream literature focuses on cases when high data rates for a handful of devices are of priority. In reality, due to the diversity of applications, no solution is one-size-fits-all. This thesis provides signal-processing solutions for three challenging situations.
The first challenging situation deals with the acquisition of channel estimates when the signal-to-noise-ratio (SNR) is low. The benefits of massive MIMO are unlocked by having good channel estimates. By the virtue of reciprocity in time-division duplex, the estimates are obtained by transmitting pilots on the uplink. However, if the uplink SNR is low, the quality of the channel estimates will suffer and consequently the spectral efficiency will also suffer. This thesis studies two cases where the channel estimates can be improved: one where the device is stationary such that the channel is constant over many coherence blocks and one where the device has access to accurate channel estimates such that it can design its pilots based on the knowledge of the channel. The thesis provides algorithms and methods that exploit the aforementioned structures which improve the spectral efficiency.
Next, the thesis considers massive machine-type communications, where a large number of simple devices, such as sensors, are communicating with the base station. This thesis provides a quantitative study on which type of benefits massive MIMO can provide for this communication scenario — many devices can be spatially multiplexed and their battery life can be increased. Further, activity detection is also studied and it is shown that the channel hardening and favorable propagation properties of massive MIMO can be exploited to design efficient detection algorithms.
The third part of the thesis studies a more specific application of massive MIMO, namely federated learning. In federated learning, the goal is for the devices to collectively train a machine learning model based on their local data by only transmitting model updates to the base station. Sum channel estimation has been advocated for blind over-the-air federated learning since fewer communication resources are required to obtain such estimates. On the contrary, this thesis shows that individually estimating each device's channel can save a huge number of resources owing to the fact that it allows for individual processing such as gradient sparsification which in turn saves a huge number of resources that compensates for the channel estimation overhead.
@phdthesis{diva2:1695482,
author = {Becirovic, Ema},
title = {{Signal Processing Aspects of Massive MIMO}},
school = {Linköping University},
type = {{Linköping Studies in Science and Technology. Dissertations No. 2251}},
year = {2022},
address = {Sweden},
}
Digital-to-analog converters (DACs) are key building blocks in various applications including radar and wireless communications. With the exponential growth of data throughput in modern communication standards, e.g., fifthgeneration (5G), DACs has been pushed to achieve direct frequency synthesis in the GHz-range with channel bandwidths preferably beyond 1 GHz. Yet, higher frequency synthesis results in augmented power consumption, which can significantly impact the wireless network if multiple DACs are utilized, e.g., in massive multiple-input and multiple-output (MIMO) antenna systems with digital beamforming as well as in end-user’s handheld devices subject to a less prolonged battery life. Moreover, advances in digital signal processing and integrated-circuit fabrication, leading to reduced power consumption and cost as well as more flexibility in software-defined radio transmitters have motivated the displacement of analog/RF circuits to the digital domain. At the same time, driving the DACs to cover the millimeter- Wave (mm-Wave) spectrum, ranging between 30-300 GHz. In this work, high-speed DACs operating in the GHz-range with maintained low power consumption is addressed. The Nyquist-rate DAC is chosen due to its simple conversion approach to facilitate the generation of channel bandwidths in the GHz-range.
A 10-bit current-steering (CS) Nyquist DAC realized in 65-nm CMOS is presented. The design is intended for low-complexity and power consumption while targeting high-speed operation with over 1 GHz channel bandwidth and maintained linearity. The binary-weighted architecture is considered to achieve straightforward digital-to-analog conversion. Next, a theoretical analysis to obtain the energy consumption bounds in CS DACs is presented. The analysis considers the digital, mixed-signal and analog power domains as well as the design corners of noise, speed and linearity. This is validated from reported measurement results in published CS DACs implemented in CMOS technology. Furthermore, design considerations with enhancement techniques are addressed. A digital switching scheme to avoid complementary switching transitions and counteract for timing errors is presented. The proposed scheme improves also the yield in linearity due to stochastic amplitude errors with reduced switching activity. Then, a comparative analysis of latch-drivers commonly implemented in CS DACs is realized. The comparison includes single- and dual-clocked latch-drivers and an alternative solution is proposed to reduce the switching-delay and power consumption.
@phdthesis{diva2:1697422,
author = {Morales Chacón, Oscar},
title = {{Studies on the Performance Bounds and Design of Current-Steering DACs}},
school = {Linköping University},
type = {{Linköping Studies in Science and Technology. Dissertations No. 2238}},
year = {2022},
address = {Sweden},
}
The global mining industry is currently facing a huge transition from manually operated individual vehicles, to autonomous vehicles being part of an industrial process-like environment. The change is driven by the never ending need for efficient, safe, and environmentally friendly operations. One intentional consequence is an increased distance between the operator, and the machine being operated. This enables safer working environments and reduced cost for ventilation and other supporting systems in a mine, but it also results in the loss of the systems most important sensor. The transition from manual to autonomous operation requires this gap to be filled from a system awareness perspective, which lately has become evident with the large resources that car manufacturers use to develop self-driving cars. This thesis also targets system awareness, but of the internal kind. By this we mean knowing the condition of the machine and its capabilities. The operator is the most important sensor also for internal condition, and if no operator is present on the machine, this gap needs to be filled.
The mining industry is categorized by small series and significant customization of machinery. This is a direct result of the geological prerequisites, where differently shaped ore bodies cause large differences in mine layout and mining methods. This thesis explores how methods estimating the health of mining vehicles can be used in this setting, by utilizing sensor signals to make assessments of the current vehicle condition and tasks.
The resulting health information can be used both to aid in tasks such as maintenance planning, but also as an important input to decision making for the planning system, i.e. how to run the vehicle for minimum wear and damage, while maintaining other mission objectives.
Two applications are studied. Mine trucks have slow degradation modes, such as crack propagation and fatigue, that are difficult to handle with data driven approaches since data collection requires significant amounts of time. A contribution in this thesis, is a method to utilize short term measurement data together with data driven methods to obtain the loads of a vehicle, and then to use physics based approaches to estimate the actual damage.
The second application considers monitoring faults in hydraulic rock drills using online measurements during operation. The rock drill is a specifically difficult case, since severe vibration levels limits the locations and types of sensors that can be used. The main contribution is a method to handle individual differences when classifying internal faults using a single pressure sensor on the hydraulic supply line. A complicating factor is the large influence of wave propagation, causing different individuals to show different behavior.
@phdthesis{diva2:1652801,
author = {Jakobsson, Erik},
title = {{Condition Monitoring in Mobile Mining Machinery}},
school = {Linköping University},
type = {{Linköping Studies in Science and Technology. Dissertations No. 2225}},
year = {2022},
address = {Sweden},
}
Adopting centralized optimization approaches in order to solve optimization problem arising from analyzing large-scale systems, requires a powerful computational unit. Such units, however, do not always exist. In addition, it is not always possible to form the optimization problem in a centralized manner due to structural constraints or privacy requirements. A possible solution in these cases is to use distributed optimization approaches. Many large-scale systems have inherent structures which can be exploited to develop scalable optimization approaches. In this thesis, chordal graph properties are used in order to design tailored distributed optimization approaches for applications in control and estimation, and especially for model predictive control and localization problems. The first contribution concerns a distributed primal-dual interior-point algorithm for which it is investigated how parallelism can be exploited. In particular, it is shown how the computations of the algorithm can be distributed on different processors so that they can be run in parallel. As a result, the algorithm execution time is accelerated compared to the case where the algorithm is run on a single processor. Simulation studies on linear model predictive control and robust model predictive control confirm the efficiency of the framework. The second contribution is to devise a tailored distributed algorithm for nonlinear least squares with application to a sensor network location problem. It relies on the Levenberg-Marquardt algorithm, in which the computations are distributed using message passing over the computational graph of the problem, which is obtained from what is known as the clique tree of the problem. The results indicate that the algorithm provides not only a good localization accuracy, but also it requires fewer iterations and communications between computational agents in order to converge compared to known first-order methods. The third contribution is a study of extending the message passing idea in order to design tailored distributed algorithm for general non-convex problems. The framework relies on an augmented Lagrangian algorithm in which a primal-dual interior-point method is used for the inner iteration. Application of the framework for general model predictive control of systems with several interconnected sub-systems is extensively investigated. The performance of the framework is then compared with distributed methods based on the alternating direction method of multipliers, where the superiority of the framework is illustrated.
@phdthesis{diva2:1632653,
author = {Parvini Ahmadi, Shervin},
title = {{Distributed Optimization for Control and Estimation}},
school = {Linköping University},
type = {{Linköping Studies in Science and Technology. Dissertations No. 2207}},
year = {2022},
address = {Sweden},
}
Vision is the primary means by which we know where we are, what is nearby, and how we are moving. The corresponding computer-vision task is the simultaneous mapping of the surroundings and the localization of the camera. This goes by many names of which this thesis uses Visual Odometry. This name implies the images are sequential and emphasizes the accuracy of the pose and the real time requirements. This field has seen substantial improvements over the past decade and visual odometry is used extensively in robotics for localization, navigation and obstacle detection.
The main purpose of this thesis is the study and advancement of visual odometry systems, and makes several contributions. The first of which is a high performance stereo visual odometry system, which through geometrically supported tracking achieved top rank on the KITTI odometry benchmark.
The second is the state-of-the-art perspective three point solver. Such solvers find the pose of a camera given the projections of three known 3d points and are a core part of many visual odometry systems. By reformulating the underlying problem we avoided a problematic quartic polynomial. As a result we achieved substantially higher computational performance and numerical accuracy.
The third is a system which generalizes stereo visual odometry to the simultaneous estimation of multiple independently moving objects. The main contribution is a real time system which allows the identification of generic moving rigid objects and the prediction of their trajectories in real time, with applications to robotic navigation in in dynamic environments.
The fourth is an improved spline type continuous pose trajectory estimation framework, which simplifies the integration of general dynamic models. The framework is used to show that visual odometry systems based on continuous pose trajectories are both practical and can operate in real time.
The visual odometry pipeline is considered from both a theoretical and a practical perspective. The systems described have been tested both on benchmarks and real vehicles. This thesis places the published work into context, highlighting key insights and practical observations.
@phdthesis{diva2:1635583,
author = {Persson, Mikael},
title = {{Visual Odometryin Principle and Practice}},
school = {Linköping University},
type = {{Linköping Studies in Science and Technology. Dissertations No. 2201}},
year = {2022},
address = {Sweden},
}
Senast uppdaterad: 2020-10-01