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. 

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.  

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.). 

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.

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.