abstract: According to the Center for Disease Control and Prevention report around 29,668 United States residents aged greater than 65 years had died as a result of a fall in 2016. Other injuries like wrist fractures, hip fractures, and head injuries occur as a result of a fall. Certain groups of people are more prone to experience falls than others, one of which being individuals with stroke. The two most common issues with individuals with strokes are ankle weakness and foot drop, both of which contribute to falls. To mitigate this issue, the most popular clinical remedy given to these users is thermoplastic Ankle Foot Orthosis. These AFO's help improving gait velocity, stride length, and cadence. However, studies have shown that a continuous restraint on the ankle harms the compensatory stepping response and forward propulsion. It has been shown in previous studies that compensatory stepping and forward propulsion are crucial for the user's ability to recover from postural perturbations. Hence, there is a need for active devices that can supply a plantarflexion during the push-off and dorsiflexion during the swing phase of gait. Although advancements in the orthotic research have shown major improvements in supporting the ankle joint for rehabilitation, there is a lack of available active devices that can help impaired users in daily activities. In this study, our primary focus is to build an unobtrusive, cost-effective, and easy to wear active device for gait rehabilitation and fall prevention in individuals who are at risk. The device will be using a double-acting cylinder that can be easily incorporated into the user's footwear using a novel custom-designed powered ankle brace. The device will use Inertial Measurement Units to measure kinematic parameters of the lower body and a custom control algorithm to actuate the device based on the measurements. The study can be used to advance the field of gait assistance, rehabilitation, and potentially fall prevention of individuals with lower-limb impairments through the use of Active Ankle Foot Orthosis. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2020

The restrictions that have been introduced with the Covid-19 pandemic has led to stores having less customers and stores shutting down. There is a need to track people's occupancy in public places so there could be more people in those places but still following the restrictions. This study evaluates and compares three different heat sensors to see if they could be used for this. The three heat sensors that have been used in this study are both low-cost and low-resolution sensors. The choice of low-resolution heat sensors is important to not intrude on people's privacy. In this study, there were three different experiments performed on all the three sensors. The experiments were done to see how sunlight could affect the sensors, what advantages, and disadvantages there are by placing the sensors in different positions, and how good the sensors are at separating people. The collected results gave a good basis to answer the research questions and better understand the sensors chosen in this research. The results also showed that the sensors can be used as an aid against a pandemic when restrictions must be kept. In the discussion we talk about how the sensors performed in different experiments and how they performed against each other. Our chosen method is also discussed and what we think were good and what went wrong so others do not repeat our mistakes. The paper ends with our conclusions and suggestions for future work.

An IMU is a sensor with many differing use cases, it makes use of an accelerometer, gyroscope and sometimes a magnetometer. One of the biggest problems with IMU sensors is the effect vibrations can have on their data. The reason for this study is to find a solution to this problem by filtering the data. The tests for this study were conducted in cooperation with Husqvarna using two of their automowers. The tests were made by running the automowers across different surfaces and recording the IMU data. To find filters for the IMU data a comprehensive literature survey was conducted to find suitable methods to filter out vibrations. The two filters selected for further testing were the complementary filter and the LMS filter. When the tests had been run all the data was added to data sheets where it could be analyzed and have the filters added to the data. From the gathered data the data spikes were clearly visible and were more than enough to trigger the mower's emergency stop and need to be manually reset. The vibrations were too irregular to filter using the LMS filter since it requires a known signal to filter against. Hence only the complementary filter was implemented fully. With the complementary filter these vibrations can be minimized and brought well below the level required to trigger an emergency stop. With a high filter weight constant such as 0.98, the margin of error from vibrations can be brought down to +- 1 degrees as the lowest and +- 4,6 degrees as highest depending on the surface and automower under testing. The main advantage with using the complementary filter is that it only requires one weight constant to adjust the filter intensity making it easy to use. The one disadvantage is that the higher the weight constant is the more delay there is on the data.

Indiana University-Purdue University Indianapolis (IUPUI) / Deep learning solutions to computer vision tasks have revolutionized many industries in recent years, but embedded systems have too many restrictions to take advantage of current state-of-the-art configurations. Typical embedded processor hardware configurations must meet very low power and memory constraints to maintain small and lightweight packaging, and the architectures of the current best deep learning models are too computationally-intensive for these hardware configurations. Current research shows that convolutional neural networks (CNNs) can be deployed with a few architectural modifications on Field-Programmable Gate Arrays (FPGAs) resulting in minimal loss of accuracy, similar or decreased processing speeds, and lower power consumption when compared to general-purpose Central Processing Units (CPUs) and Graphics Processing Units (GPUs). This research contributes further to these findings with the FPGA implementation of a YOLOv4 object detection model that was developed with the use of transfer learning. The transfer-learned model uses the weights of a model pre-trained on the MS-COCO dataset as a starting point then fine-tunes only the output layers for detection on more specific objects of five classes. The model architecture was then modified slightly for compatibility with the FPGA hardware using techniques such as weight quantization and replacing unsupported activation layer types. The model was deployed on three different hardware setups (CPU, GPU, FPGA) for inference on a test set of 100 images. It was found that the FPGA was able to achieve real-time inference speeds of 33.77 frames-per-second, a speedup of 7.74 frames-per-second when compared to GPU deployment. The model also consumed 96% less power than a GPU configuration with only approximately 4% average loss in accuracy across all 5 classes. The results are even more striking when compared to CPU deployment, with 131.7-times speedup in inference throughput. CPUs have long since been outperformed by GPUs for deep learning applications but are used in most embedded systems. These results further illustrate the advantages of FPGAs for deep learning inference on embedded systems even when transfer learning is used for an efficient end-to-end deployment process. This work advances current state-of-the-art with the implementation of a YOLOv4 object detection model developed with transfer learning for FPGA deployment.

Clouds are powerful computer centers that provide computing and storage facilities that can be remotely accessed. The flexibility and cost-efficiency offered by clouds have made them very popular for business and web applications. The use of clouds is now being extended to safety-critical applications such as factories. However, cloud services do not provide time predictability which creates a hassle for such time-sensitive applications. Moreover, delays in the data communication between clouds and the devices the clouds control are unpredictable. Therefore, to increase predictability an intermediate layer between devices and the cloud is introduced. This layer, the Fog layer, aims to provide computational resources closer to the edge of the network. However, the fog computing paradigm relies on resource-constrained nodes, creating new potential challenges in resource management, scalability, and reliability. Solutions such as lightweight virtualization technologies can be leveraged for solving the dichotomy between performance and reliability in fog computing. In this context, container-based virtualization is a key technology providing lightweight virtualization for cloud computing that can be applied in fog computing as well. Such container-based technologies provide fault tolerance mechanisms that improve the reliability and availability of application execution.  By the study of a robotic use-case, we have realized that persistent data storage for stateful applications at the fog layer is particularly important. In addition, we identified the need to enhance the current container orchestration solution to fit fog applications executing in container-based architectures. In this thesis, we identify open challenges in achieving dependable fog platforms. Among these, we focus particularly on scalable, lightweight virtualization, auto-recovery, and re-integration solutions after failures in fog applications and nodes. We implement a testbed to deploy our use-case on a container-based fog platform and investigate the fulfillment of key dependability requirements. We enhance the architecture and identify the lack of persistent storage for stateful applications as an important impediment for the execution of control applications. We propose a solution for persistent fault-tolerant storage at the fog layer, which dissociates storage from applications to reduce application load and separates the concern of distributed storage. Our solution includes a replicated data structure supported by a consensus protocol that ensures distributed data consistency and fault tolerance in case of node failures. Finally, we use the UPPAAL verification tool to model and verify the fault tolerance and consistency of our solution.

Whilst GPS positioning has been a well used technology for many years in outdoor environments,a ubiquitous solution for indoor positioning is yet to be found, as GPS positioning is unreliableindoors. This thesis focuses on the combination of Inertial Sensor Dead Reckoning and positionsobtained from the Bluetooth Low Energy (BLE) Direction Finding technique. The main objectiveis to reduce the error rate and size of a BLE Direction Finding system. The positioned object is aMicro-Electrical Mechanical System (MEMS) with an accelerometer and a gyroscope, placed on atrolley. The accelerometer and gyroscope are used to obtain an orientation, velocity vector, andin turn a position which is combined with the BLE Direction Finding position. To further reducethe error rate of the system, a Stationary Detection functionality is implemented. Because of thetrolley movement pattern causing noise in the sensor signals, and the limited sensor setup, it is notpossible to increase the accuracy of the system using the proposed method. However, the StationaryDetection is able to correctly determine a stationary state and thus decreasing error rate and powerconsumption. / GPS är en väl använd teknologi sedan många år, men på grund av dess bristande precision vid inomhuspositionering, behöver en ny teknologi för detta område hittas. Denna studie är fokuserad på Dead Reckoning som ett stöd till ett Bluetooth Direction Finding positioneringssystem. Det främsta målet är att minska felfrekvensen och felstorleken i BLE Direction Finding systemet. Föremålet som positioneras är en Micro-Electrical Mechanical System (MEMS) med en accelerometer och ett gyroskop, placerad på en vagn. Accelerometern och gyroskopet används för att erhålla en orientering, hastighetsvektor och därefter en position som kombineras med den position som ges av BLE Direction Finding. För att minska felfrekvensen ytterligare hos systemet, implementeras en funktionalitet som detekterar om MEMS-enheten är stillastående, kallad Stationary Detection. På grund av vagnens rörelsemönster, som bidrar till brus hos sensorsignalerna, samt den begränsade sensorkonfigurationen, är det inte möjligt att förbättra systemets precision med den föreslagna metoden. Dock kan Stationary Detection korrekt fastställa ett stationärt tillstånd och därmed minska felfrekvensen och energiförbrukningen för enheten.

Unmanned Air Vehicles (UAVs) enable the observation of hazardous areas without endangering a pilot. Observational capabilities are provided by on-board video cameras and images are relayed to remote operators for analysis. However, vibration and wind cause video camera mounts to move and can introduce unintended motion that makes video analysis more difficult. Video stabilization is a process that attempts to remove unwanted movement from a video input to provide a clearer picture. This thesis presents an onboard video stabilization solution that removes high-frequency jitter, displays output at 20 frames per second (FPS), and runs on a Blackfin embedded processor. Any video stabilization algorithm will have to contend with the limited space, weight, and power available for embedded systems hardware on a UAV. This thesis demonstrates how architecture-specific optimizations improve algorithm performance on embedded systems and allow an algorithm that was designed with more powerful computing systems in mind to perform on a system that is limited in both size and resources. These optimizations reduce the total clock cycles per frame by 157 million to 30 million, which yields a frame rate increase from 3.2 to 20 FPS.

This master’s thesis deals with analysis of state machines for embedded applications. The issue of finite-state machine is described theoretically. The document also contains a proposal for funding for modeling finite state machines in Matlab/Simulink. It is designed data representation of finite automaton. Over this data representation algorithm of minimization is applied. Finally, the algorithm is implemented to generate code in C language.

The goal of the thesis is to design a development board that may be used for embedded systems prototyping. The board's key parts are an ARM-Cortex-based microcontroller and a FPGA programmable circuit. The platform is designed with .NET Micro Framework support in mind. The thesis contains specifications of the development board, describes the design process as well as the task of .NET Micro Framework porting and the establishment of communication bus between the FPGA and microcontroller circuits. The thesis is concluded by a set of demonstration examples which outline how to develop new applications for the designed platform.

The CUBES (CUbesat x-ray Background Explorer using Scintillators) is a payload on the KTH student satellite MIST (MIniature STudent satellite) to evaluate Silicon Photo-multiplier technology and new scintillators such as GAGG (Gadolinium Aluminium Gallium Garnet, Gd3Al2Ga3O12) for future use in hard X-ray polarisation studies of Gamma-Ray Bursts. CUBES itself is designed to study the MIST in-orbit radiation environment by using a detector which is comprised of a silicon photomultiplier coupled to different scintillator materials. Three of these detectors will be mounted on the payload platform and then coupled to inputs of an Application Specific Integrated Circuit (ASIC) and connected to a Field-programmable Gate Array (FPGA) which will store and send data through the downlink on the MIST satellite to ground. This thesis covers the software development for the FPGA, together with two radiation tests of components and the preparation of these. / CUBES / MIST