With the requirements for CubeSats increasing, a push towards utilizing high­performance equipment has never been greater. Incorporating these equipment in an environment that is cost­, space­ and power­constrained, is challenging. Simultaneously, high­performance equipment require high pointing­accuracy and low­jitter. This thesis proposes the use of a cost­effective reaction­wheel, which utilizes three hall­sensors for accurate attitude­control. Additionally, the requirements state that the reaction­wheel operates in the low­speed region, whilst tracking low­acceleration commands. For this, a controller­structure is designed, simulated & implemented in order to provide accurate angular­velocity estimates at a constant rate. Whilst the angular­velocity estimates are within 1% of the measurements, the angular­acceleration tracking performance relies greatly on the noise of the reaction­wheel torque­friction. Nonetheless, the angular­acceleration mean error was found to be 0.005 RPM/s, with a variance of 0.3 RPM/s on a 0.01 RPM/s reference

This report details the design and development of an agar/NaCl gel-like tissue phantom mimicking the electrical properties of wet human skin. The skin phantom provides a reliable, reproducible testing ground for dry-contact polydimethylsiloxane (CNT/PDMS) electrodes, with the aim of recording electroencephalograms (EEGs) and stimulating brain activity in a controlled environment. These electrodes are being designed for the development of an in-ear brain-computer interface (BCI). The electrical properties of biological tissue are referred to as the conductivity σ and permittivity ε and denote the ability for a material to conduct and trap electric charge respectively. These properties are frequency dependent and particularly for EEGs, a frequency range of 1-1000 Hz is of interest (with some added leeway). Wet skin hereby has a conductivity of around 0.1 Siemens to 0.2 Siemens in the 1-1000 Hz frequency range whereas the permittivity ranges from 5.7 * 10^5 to 5.2 * 10^5. Different agar and agar/NaCl solutions are created to try and obtain solutions with the mentioned electric properties. Specifically, NaCl is added to improve the conductivity and obtain a non-linear frequency response similar to that of human skin. The electrical properties of the phantoms were verified/measured using the parallel plate method. This method is essentially sandwiching a material under test (MUT) (in this case the fabricated gel-like agar and agar/NaCl solutions) between two conducting plates. This method is most suited for measurements in the lower frequency spectrum. The skin phantom consisting of 3.04 mass fraction weight (wt.%) agar and 0.539 wt.% NaCl shows the closest similarity to the conductivity of wet skin. Namely, a conductivity of ~ 0.1 Siemens to 0.45 Siemens in the frequency range of 1-1000 Hz. A decrease of 0.250 wt.% NaCl will most likely achieve the desired conductivity response of 0.1 Siemens to 0.2 Siemens in the frequency range of 1-1000 Hz. The skin phantom consisting of 3.00 wt.% agar and 1.02 wt.% NaCl showed the permittivity closest to that of wet skin, but might have been a noisy outlier. Its permittivity ranges from 10 * 10^6 and 7.5 * 10^6. This is still a large error margin from the desired 5.7 * 10^5 to 5.2 * 10^5. Additional fillers like glycine or Al powder need to be added to the solutions to obtain a permittivity close to that of wet human skin. Multi-day and difference in applied pressure measurements are performed to check the sensitivity and reproducibility of the phantoms. Applied pressure hereby has little to no influence whereas a longer life-span of the fabricated phantom shows a drastic decrease of the electrical properties of the phantoms after day 1. The changes then seem to settle. Worth mentioning is that the change is only drastic when the solution has a high conductivity. This is generally not the case for solutions with conductivities close to wet skin.

The primary objective of the subsystem discussed in this report is to continuously estimate the transfer-response of the system using the position measured by the interferometer, in order to accomplish adapted model inverted control using a dynamically updating model. In addition, this subsystem design incorporates a controller that is crucial for obtaining undisturbed position measurements. This is achieved through a feedback loop that is controlling the DC operating point of the interferometer such that it is at its quadrature point.

In the recent years the satellite industry has progressed on the subject of optical communication for use in space. With recorded speeds over 5 Gb/s it has shown to be an alternative for radio communication. In the CubeSat market this technology is new, underdeveloped, and could lead to new missions that were not possible before. As such, TNO and Hyperion joined forces to create the CubeCAT LCT (Laser Communication Terminal). The core part of this LCT is the high-speed digital data path, which was not implemented. This thesis discusses the design, implementation, and verification of the high-speed data path of the CubeCAT LCT (Laser Communication Terminal) that has a targeted speed of 100 Mb/s, with a future upgrade path to 1 Gb/s. The CubeCAT module consists of multiple modules, of which the DMU (Data Management Unit) hosts the high-speed data path. As the DMU was not implemented, a design for the DMU is proposed in this thesis. For this design multiple architectures, interconnects, and components where considered. The proposed design is based on an Hyperion CP400.85 microprocessor connected to a Lattice ECP5-5G FPGA, together with extra external memory and external storage. Then, an implementation of the high-speed data path was made that is based on a QSPI link between the microprocessor and the FPGA. This implementation is based on streaming the data from the microprocessor to the FPGA, in which the data is encoded according to the TNO3k FEC (Forward Error Correction) scheme. After encoding, the data is outputted as an LVDS signal to the laser output. The implementation of the high-speed data path was verified by simulation and on a development setup. This was done by first verifying all submodules, with focus on the QSPI link and the TNO3k encoder, and then as a whole system. All submodule tests were successful, with a note to the verification of the QSPI link. It was found that the development setup was limited to a SPI frequency of 41.50 MHz due to signal integrity issues. During the system test it was found that the there was a lack of LVDS test material. As such the LVDS output was replaced by a UART output. With this output the whole system has been validated for a QSPI link speed of 119.2 Mb/s and an internal FPGA speed of 3.2 Gb/s. The system, with LVDS output, is estimated to consume 1 watts of power. With the validation of the whole system, the high-speed data path of the CubeCAT LCT has been implemented. The design of the DMU allows for a later, 1 Gb/s upgrade of the high-speed data path.

The urgency for high-security products for industrial networks is increasing as malicious hackers are improving their accessibility tools. A common practice for a company to protect its sensitive data is network segmentation. The network is segmented in different domains with distinctive security levels. The sensitive data is stored and managed within the domain of the highest security level. To access this domain from another domain of a lower security level, a highly reliable connection is required. You want to have full control over the incoming and outgoing data flow between these network segments. A variety of solutions provide a highly-secured connection to link those segments which differ in range of features and control. An upcoming trend is the network diode. This device will allow data flow in only one direction. All the flows going into the opposite direction are being blocked. However, to feature an arbitrary flow between two network segments, the diode should consist of a numerous amount of properties. To narrow down the optional features a network diode should provide, this thesis will focus on TCP streams. TCP is one of the most common protocols used in internet traffic. Furthermore, TCP is a challenging protocol as it is a connection-oriented bidirectional protocol, which is intrinsic controversial with the concept of the data diode. To ensure the security of the data diode, this thesis will focus on a complete hardware design of the data diode. Software inside the data diode is still a risk for a security breach. This thesis will investigate the critical operations of TCP to implement them in the data diode. The aim is to utilise TCP's characteristic operations of the acknowledgement managing, the sliding window system, the congestion control algorithm, and explores the advantage of existing TCP options. To evaluate the feasibility of a high performance data diode featuring TCP, the system is broken down to project the behaviour of a TCP stream on a one-way connection device. This results in two separate TCP connections, with only the precious data as common shared information. This model requires a buffer at one side of the diode to transmit data in a TCP stream. To analyse and examine the influence of the diode configuration to the size of the buffer, a diode module is created to simulate in a OMNeT++ environment. From this simulation tool, a minimal set of parameters can be extracted that are essential to configure the data diode. With the assumption of having control on the network management at the trusted side of the diode, a configuration without a congestion control algorithm and without adding radical TCP options is recommended to minimise the required buffer size. Thereafter, this thesis proposes a high-level hardware design to implement the data diode on a hardware project. The design focuses on the high data rate which should be available to satisfy the data diode's requirements. Finally, this thesis concludes with an elaboration on the assumptions of the limitations of the network environments and recommends features to implement in future work.

Imitation Learning is a technique that enables programming the behavior of agents through demonstration, as opposed to manually engineering behavior. However, Imitation Learning methods require demonstration data (in the form of state-action labels) and in many scenarios, the demonstrations can be expensive to obtain or too complex for a demonstrator to execute. This lack or sub-optimality of demonstrations limits the applicability and performance of many Imitation Learning methods. Advancements in Interactive Imitation Learning techniques however, have made it easier for demonstrators to train agents and improve their performance. These techniques involve demonstrators interacting with and guiding the agent as it performs the requisite task. This guidance is typically in the form of corrections or feedback on the current actions being executed by the agent. In this thesis, a novel Interactive Learning technique is proposed that uses human corrective feedback in state-space to train and improve agent behavior. This technique is beneficial since providing guidance to the agent in terms of `changing its state' is often easier or more intuitive for the human demonstrator (as opposed to changing the actions being executed). For instance, in manipulation tasks using a robotic arm, it is easier for the demonstrator to provide state information such as the Cartesian position of the end-effector rather than low-level action information such as joint angles. Keeping such scenarios in mind, we propose our method titled: Teaching Imitative Policies in State-space (TIPS). We evaluate the performance of TIPS for various control tasks as part of the OpenAI Gym toolkit as well as for a manipulation task using a KUKA LBR iiwa robotic arm. We show that through continuous improvement via feedback, agents trained using TIPS outperform the demonstrator and in-turn outperform conventional Imitation Learning agents.

Decreasing injuries in football is a topic of interest for the KNVB and KNHB. To reach this goal, the use of smart sensor pants is researched. The data will be used to develop models for finding injury risk factors that are related to movements. Currently, the system is capable of reading out the IMUs and storing the data on an SD-card, for post-analysis of the data. As next step, the communication link should be implemented, for real time feedback to the football players. The design of an efficient communication system in terms of power, size, and reliability, is quite a big task. By looking at similar research to body-worn devices, it is noted, that most devices deal with a 10 times lower data rate than the current device. On top of that, it is noted that the absorption of the body is causing problems on the link reliability. Based on these observations, it is decided to focus the research of this Thesis on the reduction of the data load, and the optimization of the transmission part of the smart sensor pants. The Thesis is split into two parts, the first part will show the use of lossless data compression. The second part will start with showing the benefit of using a patch antenna over a dipole antenna and continues by showing the benefit of using a dual antenna configuration over a single antenna configuration. To be more specific, the first part of this Thesis starts by comparing different lossless data compression algorithms, from which the FELACS algorithm is chosen as most suited. This algorithm is then implemented on the current hardware and tested on data from the smart sensor pants in a realistic football scenario. These results show an average compression ratio of 43 %-45 % in the most intensive 5-minutes of a football game, with a minimum of 38 % in an interval of 10 s. To improve the compression algorithm, an adjustment to the FELACS algorithm is proposed. This adjustment is theoretically tested and shown to outperform the FELACS algorithm with a higher compression ratio. In the second part, the use of a dual antenna configuration is discussed, whereby the use of a patch antenna is compared to a dipole antenna. It will be shown, that a dual antenna configuration can significantly improve the signal strength around the player, resulting in an almost isotropic radiation pattern, using pattern diversity. On top of that, this form of pattern diversity is observed to increase the reliability of the link, using switched combining. Moreover, it will be shown that a patch antenna will be more suited for this application, due to the higher gain in the front, and the robustness against interference when placed close to a conducting material. In summary, the two main contributions of this Thesis, are the reductions in data load, and the testing and verification of the dual patch antenna configuration. These contributions provide the basis for the communication part of the smart sensor pants.

The movement of whiskers in head-fixed mice is of high interest for neurological research, as it allows scientists to learn more about learning processes during active touch. However, manual tracking of whiskers in thousands of frames is not feasible, and reliable tracking of individual whiskers is not possible with the best current software available. In this work, a new system for the automatic tracking of whiskers in top-view videos of untrimmed, head-fixed mice is presented. The design of this system is based on a variety of image processing and computer vision algorithms. For each step, several alternatives have been considered and assessed. The best options have been combined and implemented in MATLAB. This new system, the Full-Whisker Tracking System (FWTS), detects the centerline of whiskers on each frame along their whole length with sub-pixel accuracy. The whiskers are defined by their angle, position, shape and length. FWTS uses these features to track and recognize whiskers over multiple frames in video segments with a frame rate of 1000 Hz. The system was tested on representative, challenging video segments from two experiments using different mice, and showed itself superior to the pre-existing system BWTT, by detecting on average 21.2% more whiskers, and with twice as much precision. In contrast to earlier systems, FWTS has been demonstrated reliably track individual whiskers in untrimmed mice. Furthermore, FWTS's processing time is on average 41.7% lower than that of BWTT. This thesis work shows that it is possible to track whiskers in untrimmed mice, even in the face of partial occlusions, and makes it possible to study the functioning of the intact whisker system of mice in unprecedented detail.

Current brain simulators do no scale linearly to realistic problem sizes (e.g. >100,000 neurons), which makes them impractical for researchers. The goal of the thesis is to explore the use of true multi-GPU acceleration on computationally challenging brain models and to assess the scalability of such models given sufficient access to multi-node acceleration platforms. The brain model used is a state-of-the-art, extended HodgkinHuxley, biophysically- meaningful, three-compartmental model of the inferior-olivary nucleus. Not only the simulation of the cells, but also the setup of the network is taken into account when designing and benchmarking the multi-GPU version. For network sizes varying from 65K to 4M cells, 10, 100 and 1000 synapses per neuron are simulated. These simulations are executed on 8, 16, 32 and 48 GPUs. A Gaussian-distributed network of 4 million cells with a density of 1,000 synapses per neuron executed on 48 GPUs, is setup and simulated in 465.69 seconds, of which the cell-computation phase takes 4.57 seconds, obtaining a speedup of 50 times the execution time on a single GPU. A uniform-distributed network of same size and density is setup and simulated in 889.89 seconds of which the cell-computation phase takes 10.09 seconds, obtaining a speedup of 8 times the execution on a single GPU. For the implemented design, the inter-GPU communication becomes the major bottleneck, as latency increases when the sent packet sizes increase. This communication overhead does not dominate the overall execution while scaling network sizes is tractable. This scalable design gives a good prospect for neuroscientists, proving that large network-size simulations are possible, using a multi-GPU setup.

Negotiation Support Systems (NSSs) can provide help based on the preference setting (domain, issue weights, issue ranking, strategies, etc.) of the users of the systems. However, sometimes the users of the systems might make mistakes in the preference setting. With wrong preferences, the NSSs might provide suggestions that conflict with the users' desires. This thesis focuses on designing a mechanism that could detect the mismatches in issue weights of the users of NSSs and provide recommendations on updating the issue weights with a scoring method. This mechanism also has the ability to provide different recommendations based on the users' self-confidence level. A case study is done with six simulation experiments to test the mechanism. The results show that during the negotiation, the users of the NSS (PN) can have a chance to change the issue weights when mismatches occurred and the NSS (PN) can provide better suggestions after the issue weights are updated. The limitations of the mechanism are concluded at the end together with the future thoughts

The mission control of a fixed-wings UAV is slightly more complex compared to other models like quad-rotors or unicycles since the dynamic of a fixed-wing aircraft does not allow rapid direction changes or stop-rotate-go type of movements. Those restriction needs to be taken into consideration when building a mission control system, moreover, the problem increases in complexity when the goal is to fly in formation with several UAVs, indeed during its development, we have to handle also the scalability of the communication link and the robustness of the formation during harsh weather conditions and disturbances. In this research we propose a solution based on a vector field This vector field is computed based on the leader attitude and the followers positions. From the vector field, the follower can calculate the heading and the speed that it has to keep in order to reach the formation. To reach this solution, we first defined a desired heading and ground speed, then, by defining the goals of the UAV and the dynamic of the UAV we found the commanded heading and the commanded ground speed for each follower. The stability of the result is checked by dividing the convergence problem into two smaller problems and applying the Lyapunov stability theory. The solution is fully implemented using state of the art technology in embedded and autopilots for UAV. The system was physically simulated with ROS in Gazebo. At the end, the performances are compared to another state of the art solution, in which VFF outperformed in most of the cases

Resting-state fMRI (rs-fMRI) has become an important imaging modality and is commonly used to study intrinsic brain networks. These networks can be obtained by decomposing rs-fMRI data into components, using independent component analysis (ICA). Recently, these ICA components have been used as inputs for neural networks to learn complex relations between the intrinsic networks of the brain and mental disorders or demographic variables. Instead of training a non-linear classifier on these linearly decomposed components, this work asks whether unsupervised representation learning can lead to linearly separable representations for multiple downstream tasks. We propose to apply non-linear representation learning to voxelwise rs-fMRI data. Learning the non-linear representations is done using two versions of a variational autoencoder (VAE). The first version is a vanilla VAE with 3D residual blocks in both its encoder and decoder. The second version is based on the identifiable VAE and uses a time-dependent prior. The models train to reconstruct the original input data from latent variables it infers. Three predictive models then evaluate the predictive power of the latent variables on an age regression, a sex classification, and a schizophrenia classification task. Each of the predictive models performs predictions for each of the three tasks. The predictive models are a support vector machine (SVM), a k-nearest neighbor (k-NN) model, and a long short-term memory (LSTM) neural network. We show that our method performs exceptionally well on the age regression and sex classification tasks without any supervision. These results imply that VAEs can model predictive variations in their latent spaces for demographic variables. The models, however, do not do well on the schizophrenia classification task, even when the models are pretrained. Despite the lower performance on the schizophrenia classification task, the overall results are encouraging and pave the way for future work on voxelwise representation learning.

The off-side rule is the most complicated rule in soccer. Today, off-sides are determined with the help of referees running on the sides of the field or with the help of cameras. However, for lower league games the installation of a camera based system is very expensive. This work proposes a sensor based system that can be worn by players which can automatically detect off-sides. In order to create such a system, two aspects had to be researched (a) localisation and (b) ball detection. This work provides an evaluation of an ultra-wide band system in outdoor conditions to solve the localisation problem and various proximity based sensors to solve the ball detection problem. The primary research objective of this work was to find the pit-falls of the ultra-wide band system with respect to our application. It was found that the tri-lateration algorithm implemented on the devices contributed to a lot of errors in localisation accuracy and the overall system suffered from significant packet loss. A custom tri-lateration algorithm along with hardware improvements are proposed. Our results show that the localisation errors are reduced by 56 % and improved system suffers from nearly no packet loss. As for ball detection, results show that ToF sensors provided the best accuracy in detecting the ball.

Mobile robots need to be fully autonomous in order to perform their tasks inside their environment. To do that, robots need to have an understanding of their environment, so that they can successfully localize and navigate themselves within it. The understanding of the robots' environment is created by solving the SLAM (Simultaneous Localization And Mapping) problem. The SLAM problem is usually approached using probabilistic laws; both the robot's path and the environment are estimated and represented by probability distributions. The estimate of the environment is incorporated into a map, which could either be one global map or a network of smaller connected local maps. In small areas where the sensors' errors and inaccuracies remain low, the creation of one global map is preferred. However, as the area grows larger, the global map becomes inaccurate due to accumulated errors. Therefore the strategy of creating a network of local maps is employed. Even though this strategy produces low error results, there is still a limit beyond which it does not scale. Specifically, as the mapping area grows even wider, the number of local maps increases which in turn causes the amount of required memory to increase. In addition, when it comes to loop closure or in other words the procedure of estimating whether the robot returned to a previously visited point in the map, the required computational power increases linearly or sometimes quadratically with respect to the number of local maps. Consequently, SLAM algorithms suffer from scalability problems, especially when the mapping area becomes too large. Since robots are real-world devices, mapping solutions should be efficient and applicable to the robot's resources, even when these resources are limited. In this thesis we propose a framework which addresses this scalability issue, aiming to reduce the computational needs and memory usage during the SLAM procedure. Additionally, our framework intends to facilitate loop closure, which can become troublesome in large environments. Our framework uses multiple sensors and creates a network of hierarchically structured local maps. In order to assess our framework, we compare it to a technique which produces a network of non-hierarchically structured local maps. Our experiments show that in large areas our framework works ideally; the speedup is encouraging and the RAM memory overhead is negligible or conditionally lower.

In the past years, small Earth Observation (EO) satellites have become increasingly capable of taking high-resolution images at high sample rates. These images contain valuable information for different sectors, such as the agricultural and military sector. Furthermore they can contain important information about the climate and climate change. Sending these images to earth requires a large amount of down-link bandwidth. This results in heavy, large power modules and communication modules, resulting in larger, more expensive (in terms of launch cost as well as in terms of production cost) satellites. This phenomenon already results in satellites not sending all information they gather, with examples of being able to send 2 minutes worth of data per orbit (approx. 90 minutes) not being out of the ordinary. As more and more satellites are transmitting data towards earth the communication is also expected to become even more power-intensive (or even more limited), since the (theoretically) available bandwidth per satellite is reduced. Therefore a shift towards a different approach is necessary. Smarter ways to get the relevant information to earth have to be developed. In contrast with the "common knowledge" that is often applied in the field of object detection, using the highest possible image quality does not transfer to the best trained network when gathering a dataset of satellite images, since the main constraint is the bandwidth available for transmitting images, where "normally" the largest constraint is the amount of man-hours spent on annotation. Compression of training images with JPEG-XR quality level 2 during the gathering of training images results in a better "bandwidth-efficiency", in the dataset used for this research at least up to 30000 images. It was also found that when more bandwidth is available and thus more images can be added to the training set, the optimal amount of compression tends to decrease. This results also lies in line with the result that the "final accuracy" (the predicted accuracy of a model trained on an infinite amount of images) of the models tend to improve with better image quality. From this it can be concluded that for the optimal approach the training images should be compressed as far as possible at the start of training, to then decrease the amount of compression as the mission progresses and more cumulative bandwidth is available.

How do automatic object detector outputs align with what humans consider good object detection? Our study is based on the responses of 70 participants for a survey. The participants are presented with images having bound- ing box predictions, their task is to choose images which according to them have an acceptable or a good detection. The results show a correlation between the size of the object and the evaluation metric IoU (Intersection over Union), with the size of the bounding box. Furthermore, the data indicates that the kind of box they prefer most for a detection output, is also the most accepted detection by them. Additionally, the results suggest that based on the symmetry of the object, position of the bounding box may or may not play a role for considering a detection valid. Our study investigates through human subjective choices if the traditional threshold value of IoU for evaluation, and tight bounding box outputs are always the best outputs in object detection techniques.

Underwater robots are widely used in various operations below the surface of the sea. In many of these operations the precise localization and positioning is quite important, especially in cases that the robot must navigate between man-made installations. Although underwater vehicles can carry a number of different sensors, many of them can not be exploited for efficient navigation, due to costs, complexity, weight and inaccuracy. Most of robots feature at least one camera, a sensor that can be cheap, small, with low power requirements and that offers vast amount of information. The advances in computer vision and embedded systems have set cameras as an attractive mean for navigation. This combination allows computer vision as a viable alternative to provide data in real-time. This data can be used locally as a positioning source for the own robot control system, or to inform the navigation coordinates to an observer or another system. Visual Odometry (VO) is the process of estimating the position and orientation of a robot based completely on data acquired by cameras. The camera-only system can be enhanced with the fusion of an Inertial Measurement Unit (IMU) to estimate motion even more accurately, resulting in Visual Inertial Odometry (VIO). This thesis researches various approaches for underwater navigation with edge computing and focuses on systems that utilize a camera and an IMU. A VIO system architecture is proposed, comprised by a machine vision camera, an IMU, a micro-controller and the embedded system Jetson Xavier from NVIDIA. Some of the most recent VO/VIO algorithms are evaluated on a number of underwater datasets and the impact of the IMU on their performance is assessed. Design issues are discussed and challenges related to the camera - IMU fusion are analyzed. The Visual-Inertial ORB-SLAM (ORB-SLAM + IMU) algorithm is deployed on the proposed VIO system and is optimized on the embedded GPU. The whole framework is evaluated on an artificial underwater structured environment which was created in an indoor tank. While the outcome of the algorithm on motion estimation is examined, the computational performance of the embedded system is profiled for various power modes as well. Results show that the proposed VIO system is able to estimate the underwater robot’s traversed trajectory in the tank with adequate accuracy and that can execute the Visual-Inertial ORB-SLAM in real-time with sufficient speed.

Space has always been fascinating to humans, since the dawn of civilization. From the first astronomers and philosophers of ancient times, who looked at the night sky searching for answers, to the scientists and engineers of modern missions, commanding space probes to the edge of the solar system, space has always been at the epicenter of scientific discovery and human curiosity. From the launch of Sputnik 1 in 1957, to the robotic rovers exploring Mars, space missions have always relied on the latest technological advancements in order to enable physical or remote exploration of celestial bodies. Traditionally, designing a space computer required significant amount of resources, leading to designs with impressive radiation performance records. However, such designs were lacking computational performance, required years of development and as a result increased the total cost of the mission. In recent years, the advent of CubeSats meant that access to space became available to a wider community of enthusiasts, researchers and private companies who were developing low-mass spacecraft made out of Commercial Off-The-Shelf components (COTS). These components however, were designed with the goal of maximizing performance and power, with little to no flight heritage. Several novel technologies were proposed in the field of error detection and mitigation, in an effort to bridge the gap between COTS processors and their radiation-hardened counterparts. Even though the commercial semiconductor industry has increased the reliability of its products by continuously improving their designs and processes, CubeSats or other low-mass spacecraft that use these components are still less reliable than their larger counterparts. Given the aforementioned, this thesis aimed at exploring the latest developments in the field of space embedded systems and error detection techniques, in an effort to produce a software flow able to detect faults with increased compatibility across processor models. In order to accomplish this goal, the thesis was carried out at ARM Limited, as part of the Software Test Libraries (STL) team responsible for developing efficient assembly tests for detecting random faults. The Cortex-M55 CPU was chosen as the test-bed for this work, in order to develop STL routines for a reference module. TheMain Interface Unit (MIU) was chosen to act as the proof-of-concept, since it is an important module in every Cortex-M processor, interfacing the core with the main memory. More specifically, a series of tests were developed for every major module within the MIU. The design started from the largest module first, which yielded a good trade-off between coverage and time. The tests comprised of efficient assembly routines designed to trigger specific memory access patterns, targeting different portions of logic each time. At the same time, a verification software flow was developed in order to test the newly designed routines against a multitude of possible configurations and initialization parameters. This activity was necessary to ensure that the developed software will be able to operate in a variety of end applications, either in the context of a Real-Time Operating System or baremetal application. The developed STL tests were subjected to a series of fault simulations using a state-of-the-art hardware simulation tool called ZOIX. Permanent faults caused by accumulated damage were modeled as stuck-at-faults, whereas transient soft-errors were modeled as transient toggle faults. Determining an accurate fault injection interval, required knowledge of the radiation environment that a COTS-based mission would encounter. A state-of-the-art space simulation environment called SPENVIS was used in order to acquire metrics on selected reference missions on Low Earth Orbit and Geosynchronous Equatorial Orbit. This helped setting the upper limits on upset rates, which were in turn used during fault simulation to recreate realistic conditions. The developed software tests exhibited solid performance in detecting permanent faults, while achieving promising results in transient fault simulations, given certain assumptions. A series of recommendations is given for future research work on the current framework, in an effort to learn from the challenges faced and tackle some of the identified limitations. Given certain assumptions, there is evidence to believe that STLs could be indeed used for random error detection in future CubeSat missions, without increasing the total cost disproportionately.

This thesis report focuses on possible methods of digital implementation of motional feedback in a bass loudspeaker. In this thesis, the control system will be created for a monopole and a dipole speaker specifically. It does so by sketching the outlines of such a system and examining possible designs for the components required to suppress distortion which is mainly created by the speaker. This thesis shows that the digital implementation indeed allows filtering with very little latency but there are limitations in accuracy due to the conversion from the analogue to the digital domain. It is shown in this thesis that a partly analogue and partly digital system is desired for better error correction. A desired input-output gain of 30=29.54dB is achieved. The controller that is suggested is a controller based on the transfer function of the speaker. By transforming the input signal with the inverse of this open-loop transfer, the closed-loop transfer will be flattened. The instrumentation amplifier used to subtract the feedback signal from the input signal is designed such that the influence of the quantization noise is minimized. Loop-gain is added by the instrumentation amplifier, the controller and voltage-to-current amplifier. By creating loop-gain as much as possible using the digital implementation, noise is suppressed from 30dB up to 90dB within the range of frequencies of interest. The designed components in the motional feedback system allow a loop-gain up to 70dB and an input to output voltage gain of 30dB before instability occurs.