I det här examensarbetet har hårdvara och mjukvara för att styra switchningen av en högspänning till en röntgendetektor utvecklats. Examensarbetet har utförts på uppdrag av företaget XCounter som utvecklar avancerade röntgendetektorer. Resultatet av arbetet är ett kretskort som monteras i en av företagets röntgendetektorer. Kretskortet och mjukvarans funktionalitet är att switcha en högspänning samt reglera fem dioder enligt ett mönster som är ställbart i mjukvaran. Mönstret ställs utifrån en synksignal som kommer från detektorn. Timingen av händelserna är kritisk. Denna rapport beskriver utvecklingen av detta kretskort och den tillhörande mjukvaran. Från planering av komponenter till simulering av konstruktionen till själva utvecklingen. / In this thesis the hardware and software has been developed for controlling the switching of a high voltage to an X-ray detector. This thesis has been done on the behalf of the company XCounter that develops advanced X-ray detectors. The result of this work is a circuit board that is mounted in one of XCounters X-ray detectors. The circuit board and software functionality is to switch the high voltage and regulate the five diodes according to a pattern that is adjustable in the software. The pattern is adjustable in relation to a sync signal coming from the detector. The timing of the events is critical. This report describes the development of this circuit board and associated software. From the planning of components to the simulation of the design to the actual development.

Traditionally, real-time applications have been executed within an isolatedembedded system, but this is becoming outdated. These systems are growing andbecoming larger, more distributed and complex, and are often closely integratedwith the external structure. The ability to dynamically adapt and regulate thesesort of systems during runtime is an increasingly desired feature. It can increaseits lifespan and save costs in the form of both money and time. This thesisproposes a method to perform this dynamic adaptation and regulation with theconcept of computational reflection. The method is conformed to support theconstrained and varied environment faced when working with distributedembedded real-time systems. A prototype framework of the method has beenrealized in the programming language C++. This framework is lightweight anduses a minimum amount of dependencies. By including this framework into anexisting program and registering variables into the framework, the variables gainreflective properties. These properties are dynamic regulation and limited selfawareness.Lastly, the framework has been evaluated regarding its computationalload and memory consumption. This, in order to show how much extra strainthis sort of method would inflict on an existing system. The results show that,relative the functionality it provides, the strain is low in most of the cases.However, in a hard real-time environment this might not be a viable solution. / Traditionellt har realtidsapplikationer körts inom ett isolerat inbyggt system,men detta har blivit ett föråldrat synsätt. Dessa system växer och blir allt större,mer distribuerade och komplexa, och är ofta nära integrerad med den yttrestrukturen. Förmågan att dynamiskt anpassa sig och reglera denna typ av systemunder drift är en allt mer önskad egenskap. Det kan öka dess livslängd och sparakostnader i form av både pengar och tid. Denna examensrapport föreslår enmetod för att utföra denna dynamiska anpassning och reglering med hjälp avkonceptet kring computational reflection. Metoden är anpassad för att stödja denansträngda och varierad miljö man möter när man arbetar med distribueradeinbyggda realtidssystem. Ett prototyp ramverk för metoden har skapats iprogrammeringsspråket C++. Detta ramverk är lättviktigt och använder ettminimalt antal beroenden. Genom att inkludera detta ramverk i ett befintligtprogram och registrera variabler till ramverket så får variablerna reflektivaegenskaper. Dessa egenskaper är bland annat dynamisk reglering och enbegränsad självkännedom. Slutligen har ramverket utvärderats genom att testadess beräkningslast och minnesförbrukning. Detta, för att visa hur mycket extrapåfrestning denna typ av metod skulle orsaka i ett befintligt system. Resultatenvisar att, relativt dess funktionalitet, så är belastning låg i de flesta av fallen.Men i en hård-realtidsmiljö så är detta antagligen inte en hållbar lösning.

Moore’s law is the observation that over the years, the transistor density will increase,allowing billions of transistors to be integrated on a single chip. Over the lasttwo decades, Moore’s law has enabled the implementation of complex systems on asingle chip(SoCs). The challenge of the System-on-Chip(SoC) era was the demandof an efficient communication mechanism between the growing number of processingcores on the chip. The outcome established an new interconnection scheme (amongothers, like crossbars, rings, buses) based on the telecommunication networks andthe Network- on-Chip(NoC) appeared on the scene.The NoC has been developed not only to support systems embedded into asingle processor, but also to support a set of processors embedded on a singlechip.Therefore, the Multi-Processors System on Chip(MPSoC) has arisen, whichincorporate processing elements, memories and I/O with a fixed interconnection infrastructurein a complete integrated system. In such systems, the NoC constitutesthe backbone of the communication architecture that targets future SoC composedby hundred of processing elements. Besides that, together with the deep sub-microntechnology progress, some drawbacks have arisen. The communication efficiencyand the reliability of the systems rely on the proper functionality of NoC for onchipdata communication. A NoC must deal with the susceptibility of transistors tofailure that indicates the demand for a fault tolerant communication infrastructure.A mechanism that can deal with the existence of different classes of faults(transient,intermittent and permanent [11]) which can occur in the communication network.In this thesis, different algorithms are investigated that implement fault toleranttechniques for permanent faults in the NoC. The outcome would be to deliver a faulttolerantmechanism for the NoC System Generator Tool [29] which is a researchin Network-on-Chip carried out at the Royal Institute of Technology. It will beexplicitly described the fault tolerant algorithm that is implemented in the switchin order to achieve packet rerouting around the faulty communication links.

Work done at Luleå Technical University regarding the RTIC RTOS framework is expanded upon to yield a convenient toolset for event-based instrumentation by exploiting debug peripherals available on the ARMv7-M platform. By parsing the source of an RTIC application and recovering instrumentation metadata from user-supplied information, the target-emitted trace stream is decoded and mapped to RTIC task events, yielding a timeline of events that can be analyzed live and postmortem by help of a recording host-side daemon. Relevant sections of the ARMv7-M standard are covered, and peripheral configuration covered in detail. An instrumentation result of a trivial RTIC application is presented and graphically plotted to exemplify the value of the toolset, and topics of future work to improve the toolset are outlined.

Image classification is a memory- and compute-intensive task. It is difficult to implement high-speed image classification algorithms on resource-limited systems like FPGAs and embedded computers. Most image classification algorithms require many fixed- and/or floating-point operations and values. In this work, we explore the use of binary values to reduce the memory and compute requirements of image classification algorithms. Our objective was to implement these algorithms on resource-limited systems while maintaining comparable accuracy and high speeds. By implementing high-speed image classification algorithms on resource-limited systems like embedded computers, FPGAs, and ASICs, automated visual inspection can be performed on small low-powered systems. Industries like manufacturing, medicine, and agriculture can benefit from compact, high-speed, low-power visual inspection systems. Tasks like defect detection in manufactured products and quality sorting of harvested produce can be performed cheaper and more quickly. In this work, we present ECO Jet Features, an algorithm adapted to use binary values for visual inspection. The ECO Jet Features algorithm ran 3.7x faster than the original ECO Features algorithm on embedded computers. It also allowed the algorithm to be implemented on an FPGA, achieving 78x speedup over full-sized desktop systems, using a fraction of the power and space. We reviewed Binarized Neural Nets (BNNs), neural networks that use binary values for weights and activations. These networks are particularly well suited for FPGA implementation and we compared and contrasted various FPGA implementations found throughout the literature. Finally, we combined the deep learning methods used in BNNs with the efficiency of Jet Features to make Neural Jet Features. Neural Jet Features are binarized convolutional layers that are learned through deep learning and learn classic computer vision kernels like the Gaussian and Sobel kernels. These kernels are efficiently computed as a group and their outputs can be reused when forming output channels. They performed just as well as BNN convolutions on visual inspection tasks and are more stable when trained on small models.

To utilize space better, warehouses stack their products on top of each other. This increases the risk of injury for workers when storing and retrieving the products. Some warehouses counteract this by using robots to retrieve products to a picking area where a human worker picks the products needed to fulfill an order. This means that it is important for the robots to be effective when retrieving products to reduce the time the worker spends waiting in the picking area. This thesis focuses on the grouping of products in the containers when they are stored in the warehouse. The robots will then retrieve one container at a time and if the grouping of products is done correctly this should decrease the number of retrievals required to fulfill an order. In order to make the decision on which products to group together, an application was developed that data mined previous orders that the warehouse had received in an attempt to extract information about the products. With the help of this information the application then suggests different product layouts that focus on different goals when they are created. The different layouts are then compared against each other in order to determine which layout technique produces the best results. This algorithm has been named the PLO-algorithm. The results showed that when a product is placed with the PLO-algorithm, the most important aspect to consider is the relations it has with the other products it is grouped with. The results also showed that data mining orders that are too old can have a negative impact on the result if not handled correctly. The results also showed that when constructing the warehouse you should try to avoid restrictions that affect which products can be placed together as much as possible since these restrictions can impact the effectiveness of the warehouse in a negative way. The thesis draws the conclusion that there is a clear gain in effectiveness for warehouses to have a planed layout for their products. It is recommended to data mine previous orders to extract relations between the products if possible since this piece of information showed the best results in this thesis. It is also in the warehouse best interest to avoid as many restrictions as possible that affect which products can be placed together since this can impact the results in a negative way. It is also beneficial to not include data that is too old in the data mining since this can impact the results in a negative way if not handled correctly. / För att utnyttja sitt utrymme bättre staplar lagerhus sina produkter på höjden. Detta medför högre risker för personskada vid hämtning och lämning av produkter, en del lagerhus löser detta genom att använda sig av robotar som hämtar och lämnar produkterna i lagerhuset. Robotarna hämtar och lämnar produkterna i en plock zon där en mänsklig arbetare plockar de produkter som behövs för en order. Detta innebär att det är viktigt att robotarna är effektiva i sin hämtning av produkter för att minska väntetiden för arbetarna i plock zonen. I ett försök att effektivisera robotarna fokuserar denna avhandling på gruperingen av produkterna i behållarna. Detta innebär att beslutet om vilka produkter som ska grupperas tillsammans i samma behållare är viktig eftersom om rätt produkter lagras tillsammans så kommer detta minska antalet hämtningar och lämningar som krävs för att uppfylla en beställning. För att hjälpa till med detta beslut skapades en applikation som analyserade tidigare beställningar som varuhuset erhållit i ett försök att extrahera information om produkterna. Applikationen skapar sedan olika förslag på produkt placeringar där de olika förslagen fokuserar på olika mål för att undersöka vilket mål som är viktigast att fokusera på när en produkt ska placeras. Algoritmen i denna applikation har valts att kallas för PLO-algoritmen. Resultaten visade att när en produkt ska placeras med PLO-algoritmen så är det viktigt att gruppera produkten med produkter den har starka relationer till. Resultatet visade också att när data ska analyseras bör inte för gammal data analyseras då äldre relationer mellan produkter som inte stämmer längre kan påverka resultatet negativt om algoritmen ej hanterar detta på något sätt. Resultaten visade också att vid konstruktionen av lagerhuset bör restriktioner som begränsar hur produkter kan placeras, undvikas om möjligt då dessa kan påverka lagerhusets effektivitet negativt. Slutsatsen som kan dras är att ett lagerhus kan tjäna väldigt mycket på att ha en plan när de bestämmer hur deras produkter ska placeras. Om det finns möjlighet att analysera tidigare beställningar efter relationer mellan produkter så är detta rekommenderat då det visade bäst resultat i denna undersökning. Det är även till lagerhusets fördel att försöka undvika restriktioner på deras lagersystem när det byggs eftersom det möjliggör för fler kombinationer när produkterna ska grupperas. Till sist så visar avhandlingen att med datan som användes att det var fördelaktigt att inte göra analys på för gammal data, då detta ger sämre resultat.

Indiana University-Purdue University Indianapolis (IUPUI) / Every year the most effective Deep learning models, CNN architectures are showcased based on their compatibility and performance on the embedded edge hardware, especially for applications like image classification. These deep learning models necessitate a significant amount of computation and memory, so they can only be used on high-performance computing systems like CPUs or GPUs. However, they often struggle to fulfill portable specifications due to resource, energy, and real-time constraints. Hardware accelerators have recently been designed to provide the computational resources that AI and machine learning tools need. These edge accelerators have high-performance hardware which helps maintain the precision needed to accomplish this mission. Furthermore, this classification dilemma that investigates channel interdependencies using either depth-wise or group-wise convolutional features, has benefited from the inclusion of Bottleneck modules. Because of its increasing use in portable applications, the classic inverted residual block, a well-known architecture technique, has gotten more recognition. This work takes it a step forward by introducing a design method for porting CNNs to lowresource embedded systems, essentially bridging the difference between deep learning models and embedded edge systems. To achieve these goals, we use closer computing strategies to reduce the computer’s computational load and memory usage while retaining excellent deployment efficiency. This thesis work introduces HBONext, a mutated version of Harmonious Bottlenecks (DHbneck) combined with a Flipped version of Inverted Residual (FIR), which outperforms the current HBONet architecture in terms of accuracy and model size miniaturization. Unlike the current definition of inverted residual, this FIR block performs identity mapping and spatial transformation at its higher dimensions. The HBO solution, on the other hand, focuses on two orthogonal dimensions: spatial (H/W) contraction-expansion and later channel (C) expansion-contraction, which are both organized in a bilaterally symmetric manner. HBONext is one of those versions that was designed specifically for embedded and mobile applications. In this research work, we also show how to use NXP Bluebox 2.0 to build a real-time HBONext image classifier. The integration of the model into this hardware has been a big hit owing to the limited model size of 3 MB. The model was trained and validated using CIFAR10 dataset, which performed exceptionally well due to its smaller size and higher accuracy. The validation accuracy of the baseline HBONet architecture is 80.97%, and the model is 22 MB in size. The proposed architecture HBONext variants, on the other hand, gave a higher validation accuracy of 89.70% and a model size of 3.00 MB measured using the number of parameters. The performance metrics of HBONext architecture and its various variants are compared in the following chapters.

I modern industrimiljö finns fortfarande en stor mängd analoga visarinstrument. Det är önskvärt att övervaka dessa instrument digitalt vilket medför att kontroll av mätdata kan göras utan att personal behöver vara på plats. På marknaden finns idag ingen aplikation som är utvecklad för att uppfylla denna funktion.  Detta examensarbete har undersökt metoder för hur en analog visares värde ska läsas av digitalt och utvecklat en prototyp som kan utföra uppgiften.  Prototypen utvecklades med hjälp av datorseende algoritmer för att läsa av den analoga visarens värde. Algoritmerna för datorseende implementerades på en Raspberry Pi4 Model B och en kamera, Rasperry Pi Kameramodul V2. Prototypen som utvecklades uppfyller de funktioner som efterfrågades, och uppnåde en noggranhet på 0.97% +- 0.75 av det procentuella uppmätta värdet hos en analog visares fulla mätspann med en upplösning på 2.5%

With the emergence of cloud based solutions, companies such as Ericsson AB have started investigating different means of modernizing current implementations of software systems. With many new programming languages emerging such as Rust and Go, investigating the suitability of these languages compared to C++ can be seen as a part of this modernization process. There are many important aspects to consider when investigating the suitability of new programming languages, and this thesis makes an attempt at considering most of them. Therefore both performance which is a common metric as well as development efficiency which is a less common metric, were combined to provide a holistic view. Performance was defined as CPU usage, maximum memory usage, processing time per sequence and latency at runtime, which was measured on both x86 and ARM based hardware. Development efficiency was defined as the combination of the productivity metric, the maintainability index metric and the cognitive complexity metric. Combining these two metrics resulted in two general guidelines: if the application is constantly under change and performance is not critical, Go should be the language of choice. If instead performance is critical C++ should be the language of choice. Overall, when choosing a suitable programming language, one needs to weigh development efficiency against performance to make a decision.

The market for electric vehicles (EV) is growing rapidly. The rise of EVs is most prominent for passenger vehicles, but trucks and busses are also quickly becoming electrified. Scania aims to be the leader of this transition. A central part of the EV is the Lithium-ion battery. In order to use the battery in the most efficient manner a Battery Management System (BMS) is needed. A key part of the BMS is a model that describes the battery as a system where the input is the current and the output is the terminal voltage. The dynamics of the battery is affected by external factors, called scheduling variables, that should be taken in to account in order to acquire an accurate model. This thesis aims to capture this behavior by the identification of a Linear Parameter Varying (LPV) model that has State of Charge (SOC) and temperature as scheduling variables. The LPV model was identified by first performing a set of local system identifications at varying levels of the scheduling variables. From this, a set of different LPV model structures were set up and then optimized with the use of datasets with a wider coverage of the scheduling variables. The results showed that there are clear advantages in using an LPV model compared to a traditional constant model, but that the robustness of the model largely is dependent on the choice of the data used for optimization.