An embedded system is a computer system that is a part of a larger device with hardware and mechanical parts. Such a system often has limited resources (such as processing power, memory, and power) and it typically has to meet hard real-time requirements. Today, as the area of application of embedded systems is constantly increasing, resulting in higher demands on system performance and a growing complexity of embedded software, there is a clear trend towards multi-core and multi-processor systems. Such systems are inherently concurrent, but programming concurrent systems using the traditional abstractions (i.e., explicit threads of execution) has been shown to be both difficult and error-prone. The natural solution is to raise the abstraction level and make concurrency implicit, in order to aid the programmer in the task of writing correct code. However, when we raise the abstraction level, there is always an inherent cost. In this thesis we consider one possible concurrency model, the concurrent reactive object approach that offers implicit concurrency at the object level. This model has been implemented in the programming language Timber, which primarily targets development of real-time systems. It is also implemented in TinyTimber, a subset of the C language closely matching Timber’s execution model. We quantify various costs of a TinyTimber implementation of the model (such as context switching and message passing overheads) on a number of hardware platforms and compare them to the costs of the more common thread-based approach. We then demonstrate how some of these costs can be mitigated using stack resource policy. On a separate track, we present a feasibility test for garbage collection in a reactive real-time system with automatic memory management, which is a necessary component for verification of correctness of a real-time system implemented in Timber

Studies about the industrial standard IEC 61499 and its relation to the RTFM Model of Computation represent the basis of this thesis. An overview of industrial automation software in general and in the scope of Svenska Kraftnät introduces the subject of software related issues. The thesis focuses on selected properties, which are important for software development to improve the robustness of industrial automation software. Among others, timing is essential due to its importance in real-time applications. An example case of the nuclear power plant Forsmark in Sweden illustrates problems correlated with timing issues and makes the lack of an overall system modelling (including timing) evident. A review of the relevant industrial standards for software development in industrial applications provides a background for various aspects of software compliance to safety requirements. Special attention lies on the standards IEC 61131 and IEC 61499 for industrial software development and their programming and execution model. The presented RTFM framework defines a concurrent model of execution based on tasks and resources together with a timing semantics that was designed from the outset for the development of embedded real-time systems. It can serve as a scheduling and resource management for the run-time environments of industrial applications, while addressing the aforementioned issues. Mappings from the functional layer (IEC 61499 function block networks) and safety layer (PLCopen safety function blocks) to RTFM show the applicability and possibility of using IEC 61499 as an overall, distributed, and hierarchical model. A discussion on options for future work presents choices to pursue the second half of the PhD studies. Formal methods for program specification and verification open up an interesting path to further increase the robustness of industrial automation software. / Frekvensomriktares funktion i beredskapskritiska system

This thesis investigates anomaly detection and classification in a simulated modular manufacturingenvironment using Machine Learning algorithm Random Forest. This algorithm is tested on a localcomputer and an embedded device, specifically the Raspberry PI. The performance of Random Forestmodels is evaluated for anomaly detection and classification tasks, considering different evaluationmetrics and execution time. The results indicate variations in model performance across differentmodules and classification tasks. It is observed that the limited computing resources of the RaspberryPI for anomaly detection tasks lead to significantly higher prediction times compared to a computer,highlighting the impact of embedded systems’ constraints on ML model execution

In the Internet of Things (IoT) era, the MQTT Protocol played a bigpart in increasing the flow of uninterrupted communication betweenconnected devices. With its functioning being on the publish/subscribe messaging system and having a central broker framework, MQTTconsidering its lightweight functionality, played a very vital role inIoT connectivity. Nonetheless, there are challenges ahead, especiallyin energy consumption, because the majority of IoT devices operateunder constrained power sources. In line with this, our research suggests how the MQTT broker can make an intelligent decision usingan intelligent algorithm. The algorithm idealizes wake-up times forsubscriber clients with the aid of previous data, including machinelearning (ML) regression techniques in the background that producesubstantial energy savings. The study combines the regression machine learning approaches with the quality of service levels’ incorporation into the decision framework through the introduction ofoperational modes designed for effective client management. The research, therefore, aims universally to enhance the efficiency availablein MQTT making it applicable across diverse IoT applications by simultaneously addressing both the broker and the client sides . Theversatile approach ensures more performance and sustainability forMQTT, further strengthening its build as one of the building blocksfor energy efficient and responsive communication in the IoT. Deeplearning approaches that follow regression will be the required leapfor the transformation of energy consumption and adoption of resource allocation within IoT networks to an optimization level thatwould unlock new frontiers of efficiency for a sustainable connectedfuture.

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The development of neural networks has become one of the most interesting topics in the scientific community. Systems that are based on the brain behavior can find applications in a wide variety of fields, from simulating the brain to better understand it (applications in neuroscience), to control theory and super computing. Brain-like systems could possible be a new kind of computer architecture that will lead us away from the classic von Neumann architecture. That can help us bypass the problems that we now face, with the Moore’s low slowing down and complex problems becoming all the more common. With brain-like computing, we might be in the road to computer systems that are no longer programmed but taught. To-date, the most common platform for simulating such systems are the GPGPUs and super computers. But they lack on scalability, and real time simulations are far from trivial. Because of that there is an interest in custom hardware implementation of such system (in ASIC or FPGAs). In this work, we focus on the ASIC design of such a system. Specifically, with the characterization and design space exploration of the eBrain architecture, a hardware architecture for the BCPNN model. During the design process of an ASIC, in order to be able to characterize it, the simulation of the synthesized physical design of the RTL model is required. Those kinds of simulations require an extensive amount of time. In this thesis, to tackle with this problem a systemC model of the architecture is developed. This model is able to be modified and fits different configurations of a general hardware architecture. The systemC model can be used to reduce the amount of time the simulation requires and, by using back annotated data from synthesized parts of the hardware architecture, to provide us with accurate characterization of the design. In this work, we go through the basics of the BCPNN and the eBrain architecture. Then we develop a model that can emulate the behavior of the eBrain architecture in a probabilistic manner. A specific configuration is chosen to be explored. Furthermore, floating-point units are synthesized in the physical level in order to be able to back annotate their power measurements to the model. Moreover, the BCPNN equations are explored and implemented in an RTL level with the use of the floating-point units. Finally, an example configuration is simulated and its results are presented.

Neuromorphic computing is a recent and growing field of research. Its conceptual attractiveness is due to the potential it has in deep learning applications such as sensor networks, low-power computer vision, robotics and other fields. Inspired by the functioning of brain, different neural network models have been devised, each with their own special focus on certain applications. Using such computing models are already helping us in different cases such as image, character and voice recognition, data analysis, stock market prediction, etc. Among the multitude of artificial neural models available, spiking neurons are more deeply inspired by biological neural networks. Leaky, Integrate and Fire (LIF) neuron model is one such model that can reproduce a good number of functions, be simple and also extensible in structure. Current deep learning applications are tied to servers and datacenters for their power and resource hungry existence. This work aims at building a low power neuron core taking advantage of LIF neuron, that could possible result in independent battery powered devices. A hardware design of LIF neuron based scalable neural core is explored, constructed and analysis for power consumption is made.

The need for electromagnetic compatibility is growing steadily as the usage of electronics in our daily lives is increasing more than ever. A common issue encountered in electromagnetic compatibility testing is analogue sensors that fail when exposed to electromagnetic fields. Testing how well electronics do when exposed to electromagnetic fields is called susceptibility testing, and standards for how to do these tests have been developed to ensure that the results of the tests can be reproduced. In this thesis work, analogue temperature sensors have been shielded using a few common techniques. The susceptibility of the sensors has been analysed by looking at their output voltage when the sensors were exposed to electro- magnetic fields of different field strengths. The output of the sensors was read by an Arduino that was shielded and tested to make sure it would not be affected by the electromagnetic fields used in the sensor tests. The result of the first set of sensor tests shows that shielding the cables running to the analogue temperature sensors and filtering away disturbances using ferrites gives a considerable decrease in susceptibility against electro- magnetic fields, while twisted cables and RC-filters did not. The results also showed that the introduction of a ground plane increased the susceptibility of the sensors, which most likely was due to it not providing the current with a path of less impedance and only served to increase the length of the unintentional antenna, which made it couple to the electromagnetic field more easily. However, during a second round of testing, the results of all the tests were hard to reproduce exactly, which calls into question how trustable the results of standardised susceptibility tests are. Therefore, when designing for the electromagnetic susceptibility of a product, a rather wide margin should be used in order to make sure that the product can reliably pass susceptibility tests. / Användningen av elektronik ökar i samhället och därför även nödvändigheten för testning av elektromagnetisk kompatibilitet. Ett vanligt problem inom elektromagnetisk kompatibilitet är att analoga sensorer lätt blir utstörda av elektromagnetiska fält. Hur man ska testa en elektronisk produkts känslighet mot elektromagnetiska fält styrs av standarder som ser till att resultaten av testerna går att återskapa. I detta examensarbete har analoga temperatursensorer skärmats med ett par vanliga metoder. Sensorernas känslighet har analyserats genom att undersöka hur deras utspänning påverkas när sensorn blir utsatt för elektromagnetiska fält med olika fältstyrkor. Sensorernas utspänning lästes av en Arduino som skärmades och testades för att se till all att den inte påverkades av de elektromagnetiska fälten som användes under testandet av sensorerna. Resultaten från de första sensortesterna visar att använda skärmade kablar till de analoga temeperatursensorerna och att filtrera bort störningar med ferriter sänkte sensorernas känslighet mot elektromagnetiska fält betydligt medan tvinnade kablar och RC filter inte gjorde det. Testerna visade också att jord- plan i detta fall ökade sensorernas känslighet då de inte erbjöd en bättre väg för strömmen att gå utan endast skapade en längre oavsiktlig antenn, vilket gjorde att den lättare kunde koppla till det elektromagnetiska fältet. Däremot visade det sig i en andra testomgång, att resultaten inte gick att återskapa ex- akt. Detta ifrågasätter hur tillförlitliga dessa standardiserade tester är och visar att man bör ha en ganska bred marginal när man designar för att minska en produkts känslighet mot elektromagnetiska fält, så att den på ett tillförlitligt sätt kommer kunna klara av känslighetstester.

Wireless power and data transfer systems are experiencing an ever-growing consumer and industrial adoption. Its use in common devices has made the technology more accessible to people, but seldomly does it replace a physical connection inside a product. When two parts of an assembly are to be electrically connected, the solution has traditionally been connectors or wires. However, typical connection methods using physical connectors between two devices can be fragile and sensitive to dust and debris. In this degree project, a wireless power and data connection between two parts of an industrial nutrunner are evaluated. A very compact nutrunner encasement calls for a minimal wireless interface with high efficiency. Additional complications are met when the nutrunner body is made of metal, which introduces losses. Electromagnetic simulations of a flexible PCB transformer with ferrite film backing are performed in the simulation software Finite Element Method Magnetics (FEMM), and electrical performance is evaluated in the circuit simulator LT-Spice. From the best performing solution, a physical model is constructed and evaluated. The final implementation uses a flyback converter for power transfer, and amplitude modulated data for bi-directional data transfer. Results indicate the potential for excellent performance with 1 W power transfer with more than 50 % efficiency whilst simultaneously transferring data at a rate greater than 1 Mbit/s. / Trådlös effekt och dataöverförings system ser allt fler tillämpningar i konsument och industriella sektorn. Dess implementation i vardagliga enheter har gjort teknologin mer tillgänglig för människor, men den ersätter sällan fysiska kontakter inuti produkter. När två delar av en produkt skall sammankopplas elektriskt har den traditionella lösningen varit kontakter eller sladdar. Däremot så kan typiska anslutningsmetoder med fysiska kontakter vara ömtåliga samt känsliga för damm och smuts. I detta examensarbete undersöks ett trådlöst effekt och data gränssnitt mellan två delar av en industriell mutterdragare. En väldigt kompakt mutterdragrinkapsling medför ett väldigt kompakt trådlöst gränssnitt med hög verkningsgrad. Ytterligare komplikationer uppstår när mutterdragaren är gjord av metall, vilket medför förluster. Elektromagnetiska simuleringar av en flexibel kretskorts transformator utförs i simuleringsprogrammet Finite Element Method Magnetics (FEMM) och elektrisk prestanda undersöks i kretssimulatorn LT-Spice. En prototyp konstrueras från den lösning som presterat bäst och denna utvärderas. Den slutgiltiga implementationen använder en flyback omvandlare för effektöverföring och amplitud modulerade data för dubbelriktad datakommunikation. Resultat indikerar god prestanda med 1 W effekt överfört vid mer än 50 % verkningsgrad samtidigt som data överförs med en hastighet mer än 1 Mbit/s.

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