På det kommunalägda energiföretaget Sala Heby Energi AB pågår arbetet med att konvertera styrningen av en värmepanna från relästyrning och analog övervakning till digital styrning med PLC. Inom ramarna för detta examensarbete studeras tre specifika delar av denna konvertering: en teoretisk studie med syfte att kartlägga fördelar och nackdelar med konverteringen, en undersökning huruvida det finns möjlighet att förbättra en-skilda styrfunktioner i samband med konverteringen och upprättande av ett principschema eller översiktsbild över värmepannan som ska fungera som underlag till dess implementering i kraftvärmeverkets SCADA-system. En fördel med relästyrningen är att den kan vara kostnadseffektiv för att klara enskilda enkla styruppgifter. Dock, överlag och i synnerhet för styrning av större mer komplexa processer talar det mesta till den digitala styrningens fördel. Detta i form av kostnadseffektivitet för större system, möjligheter till mer effektiv larmhantering och möjligheten till loggning av driftdata. Två enskilda funktioner studeras: reglering av värmepannans siktskärm i förhållande till ångdomstrycket och styrning för bränslehanteringen. För reglering av siktskärmens höjd jämförs två alter-nativ med hjälp av simuleringar i Simulink. Konventionell PID-reglering jämförs mot en egenkonstruerad fuzzy-regulator. Mycket på grund av en bristfälligt underbyggd modellering av processen går inte fuzzy-regulatorns eventuella fördelar mot PID-regulatorn att fastställa. Framtida studier av fuzzy-regulatorn skulle potentiellt kunna ändra på detta förhållande. Vidare diskuteras styrningen av funktioner i bränslehantering och då främst autentisering av chaufförer som ska bränslefylla. För att de ska få tillgång till bränslehanteringens funktioner när kraftvärmeverket är obemannat föreslås det en lösning med nyckelbrytare för att aktivera aktuella styrfunktioner. Slutligen tas en ny digital översiktsbild fram över systemet för värmepannan. Denna innehåller mindre uppdateringar och anpassningar jämfört med två översiktsbilder som finns uppritade på de gamla styrskåpen. / At the municipality-owned energy company Sala Heby Energi AB work is underway to convert the control of a heating-pan from relay control and analog monitoring to digital control with PLC. Within the framework of this degree project, three specific parts of this conversion are studied: a theoretical study aimed at mapping the pros and cons of the conversion, an examination of the possibility of improving individual control functions in conjunction with the conversion and the establishment of a schematic diagram or overview of the heat boiler that will serve as a basis for its implementation in the SCADA-system of the combined power and heating plant. An advantage of relay control is that it can be cost-effective to handle individual simple control tasks. However, overall and in particular for controlling major more complex processes the benefits of digital control is large. This in terms of cost-effectiveness for larger systems, possibilities for more efficient alarm management and the ability to log operational data. Two individual functions are studied: control of the heating pans damper in relation to the steam pressure and fuel management control. To adjust the height of the damper, two control options are compared using Simulink simulations. Conventional PID control is com-pared to a self-configured fuzzy controller. Much due to a poorly substantiated modelling of the process, the fuzzy regulator's possible benefits to the PID regulator can not be determined. Future studies of the fuzzy regulator could potentially change this circumstance. Furthermore, the control of functions for the fuel management are discussed, mainly authorship of drivers who will fill fuel to the fuel storage. In order for them to have access to the fuel management functions when the combined power and heating plant is unmanned, a key switch solution is proposed to activate the control functions. Finally, a new digital overview picture is produced for the system of the heating pan. This contains minor up-dates and adjustments compared to two overview images depicted on the old control cabinets.

Background: Expertise retrieval is an information retrieval technique that focuses on techniques to identify the most suitable ’expert’ for a task from a list of individuals. Objectives: This master thesis is a collaboration with Volvo Cars to attempt applying this concept and match employees based on information that was extracted from an internal tool of the company. In this tool, the employees describe themselves in free-flowing text. This text is extracted from the tool and analyzed using Natural Language Processing (NLP) techniques. Methods: Through the course of this project, various techniques are employed and experimented with to study, analyze and understand the unlabelled textual data using NLP techniques. Through the course of the project, we try to match individuals based on information extracted from these techniques using Unsupervised MachineLearning methods (K-means clustering).Results. The results obtained from applying the various NLP techniques are explained along with the algorithms that are implemented. Inferences deduced about the properties of the data and methodologies are discussed. Conclusions: The results obtained from this project have shown that it is possible to extract patterns among people based on free-text data written about them. The future aim is to incorporate the semantic relationship between the words to be able to identify people who are similar and dissimilar based on the data they share about themselves.

The impressive evolution of modern high-performance microprocessors have resulted in chips with over one billion transistors as well as multi-GHz clock frequencies. As the silicon integrated circuit industry moves further into the nanometer regime, three of the main challenges to overcome in order for continuing CMOS technology scaling are; growing standby power dissipation, increasing variations in process parameters, and increasing power dissipation due to growing clock load and circuit complexity. This thesis addresses all three of these future scaling challenges with the overall focus on reducing the total clock-power for low-power, multi-GHz VLSI circuits. Power-dissipation related to the clock generation and distribution is identified as the dominating contributor of the total active power dissipation. This makes novel power reduction techniques crucial in future VLSI design. This thesis describes a new energy-recovering clocking technique aimed at reducing the total chip clock-power. The proposed technique consumes 2.3x lower clock-power compared to conventional clocking at a clock frequency of 1.56 GHz. Apart from increasing power dissipation due to leakage also the robustness constraints for circuits are impacted by the increasing leakage. To improve the leakage robustness for sub-90 nm low clock load dynamic flip-flops a novel keeper technique is proposed. The proposed keeper utilizes a scalable and simple leakage compensation technique. During any low frequency operation, the flip-flop is configured as a static flip-flop with increased functional robustness. In order to compensate the impact of the increasingly large process variations on latches and flip-flops, a reconfigurable keeper technique is presented in this thesis. In contrast to the traditional design for worst-case process corners, a variable keeper circuit is utilized. The proposed reconfigurable keeper preserves the robustness of storage nodes across the process corners without degrading the overall chip performance. / Report code: LiU-TEK-LIC-2006:21.

Wireless sensor networks (WSN) play an important role in today’s monitoring and controlsystems like environmental monitoring, military surveillance, industrial sensing and control, smarthome systems and tracking systems. As the application of WSN grows by leaps and bounds, there is anincreasing demand in placing a larger number of sensors and controllers to meet the requirements. Theincreased number of sensors necessitates flexibility in the functioning of nodes. Nodes in wirelesssensor networks should be capable of being dynamically reconfigured to perform various tasks is theneed of the hour.In order to achieve flexibility in node functionality, it is common to adopt reconfigurablearchitecture for WSN nodes. FPGA-based architectures are popular reconfigurable architectures bywhich WSN nodes can be programmed to take up different roles across time. Area and power are themajor overheads in FPGA based architectures, where interconnect consumes more power and area thanlogic cells. The contemporary WSN standard requires longer battery life and micro size nodes for easyplacement and maintenance-free operation for years together.Three solutions have been studied and evaluated to approach this problem: 1) Homogenousembedded FPGA platform, 2) Power gated reconfigurable finite state machines and 3) Pass transistorlogic (PTL) based reconfigurable finite state machines. Embedded FPGA is a CMOS 65nm customdeveloped small homogenous FPGA which holds the functionality of the WSN nodes and it will bedynamically reconfigured from time to time to change the functionality of the node. In Power gatedreconfigurable FSM architecture, the functionality of the node is expressed in the form of finite statemachines, which will be implemented in a LUT based power gated design. In PTL based reconfigurablefinite state machine architecture, the finite state machines are completely realized using PTL basedcustom designed sets of library components. Low power configuration memory is used to dynamicallyreconfigure the design with various FSMs at different times.

The current consumption of the IQ modulator is a significant part of the totalcurrent consumption of a mobile transmitter platform and reducing it is of greatinterest. Also, as the WCDMA/LTE standards specifies full duplex transmissionsand Tx and Rx are most often using the same antenna, it is crucial to have asolution with low noise generation. Two new proposals have been studied with theaim to reduce the current consumption and noise contribution of the IQ modulator. A current mode envelope tracking IQM is the first of the studied designs. Thisimplementation lowers the bias currents in the circuit in relation to the amplitudeof the baseband input signals, meaning that a low input amplitude results in alowering of the current consumption. It proves to be very efficient for basebandsignals with a high peak-to-average ratio. Simulations and calculations have shownthat an average current reduction of 56 % can be achieved for an arbitrary LTEbaseband signal. The second is an entirely new passive mixer design where the baseband voltagesare sequentially copied to the RF node, removing the need for V-to-I conversion inthe mixer which reduces current consumption and noise. Results from simulationshas proven that this design is fully capable of improving both current consumptionas well as the noise levels. With an output power of 4.0 dBm, the power consumptionwas 43.3 mW, including clock generating circuits. This, combined with thefact that the design is small and simple, means that there is definitely a possibilityto replace the present IQM design with a passive mixer.

This study reviews the theoretical and experimental aspects of the X-ray diffraction (XRD) technique and evaluates its use in identifying toxic elements or compounds in waste that has been incinerated. Many industries incinerate materials that contain large significant amounts of toxic elements, and these elements should be identified and re-moved to reduce environmental pollution. The aim of this project is to identify the elemental content of an incinerated ash sample, and to recommend a proper identification method when using XRD. Here, we test two ash samples (raw ash without any treatment and ash that has been stabilized by washing) using the software DIFFRAC.EVA that is integrated into Bruker’s diffractometer D2Phaser to match different diffraction patterns to identify the contents of the ash sample. Finally concluding the results XRF is more suitable than XRD for ash surveil-lance.

This paper concerns itself with object detection as a possible solution to Valmet’s quest for a visual-feedback system that can help operators and other personnel to more easily interact with their machines and equipment. New advancements in deep learning, specifically CNN models, have been exploring neural networks with detection-capabilities. Object detection has historically been mostly inaccessible to the industry due the complex solutions involving various tricky image processing algorithms. In that regard, deep learning offers a more easily accessible way to create scalable object detection solutions. This study has therefore chosen to review recent literature detailing detection models with a selective focus on factors making them realizable on ARM hardware and in turn mobile devices like phones. An attempt was made to single out the most lightweight and hardware efficient model and implement it as a prototype in order to help Valmet in their decision process around future object detection products. The survey led to the choice of a SSD-MobileNetsV2 detection architecture due to promising characteristics making it suitable for performance-constrained smartphones. This CNN model was implemented on Valmet’s phone of choice, Samsung Galaxy S8, and it successfully achieved object detection functionality. Evaluation shows a mean average precision of 60 % in detecting objects and a 4.7 FPS performance on the chosen phone model. TensorFlow was used for developing, training and evaluating the model. The report concludes with recommending Valmet to pursue solutions built on-top of these kinds of models and further wishes to express an optimistic outlook on this type of technology for the future. Realizing performance of this magnitude on a mid-tier phone using deep learning (which historically is very computationally intensive) sets us up for great strides with this type of technology in the future; and along with better smartphones, great benefits are expected to both industry and consumers. / Den här rapporten behandlar objekt detektering som en möjlig lösning på Valmets efterfrågan av ett visuellt återkopplingssystem som kan hjälpa operatörer och annan personal att lättare interagera med maskiner och utrustning. Nya framsteg inom djupinlärning har dem senaste åren möjliggjort framtagande av neurala nätverksarkitekturer med detekteringsförmågor. Då industrisektorn svårare tar till sig högst specialiserade algoritmer och komplexa bildbehandlingsmetoder (som tidigare varit fallet med objekt detektering) så ger djupinlärningsmetoder istället upphov till att skapa självlärande system som är återanpassningsbara och närmast intuitiva i dem fall där sådan teknologi åberopas. Den här studien har därför valt att studera ett par sådana teknologier för att hitta möjliga implementeringar som kan realiseras på något så enkelt som en mobiltelefon. Urvalet har därför bestått i att hitta detekteringsmodeller som är hårdvarumässigt resurssnåla och implementera ett sådant system för att agera prototyp och underlag till Valmets vidare diskussioner kring objekt-detekteringsslösningar. Studien valde att implementera en SSD-MobileNetsV2 modellarkitektur då den uppvisade lovande egenskaper kring hårdvarukraven. Modellen implementerades och utvärderades på Valmets mest förekommande telefon Samsung Galaxy S8 och resultatet visade på en god förmåga för modellen att detektera objekt. Den valda modellen gav 60 % precision på utvärderingsbilderna och lyckades nå 4.7 FPS på den implementerade telefonen. TensorFlow användes för programmering och som stödjande mjukvaruverktyg för träning, utvärdering samt vidare implementering. Studien påpekar optimistiska förväntningar av denna typ av teknologi; kombinerat med bättre smarttelefoner i framtiden kan det leda till revolutionerande lösningar för både industri och konsumenter.

A Single pixel camera is just that, a camera that uses only a single pixel to take images. Though, it is a bit more to it than just a pixel. It requires several components which will be explained in the thesis. For it to be viable it also needs the sampling technology Compressive sensing which compresses the data in the sampling stage, thus reducing the amount of data required to be sampled in order to reconstruct an image. This thesis will present the method of building an SPC with the required hardware and software. Different types of experiments, such as detection of small changes in a scene and different wavelength bands, has been conducted in order to test the performance and application areas for the SPC. The resulting system is able to produce images of resolutions up to 512x512 pixels. Disturbances such as movement in the scene or the camera itself being shaken became less of a problem with the addition of a second pixel. This thesis proves that an SPC is a viable technology with many different areas of application and it is a relatively cheap way of making a camera for the infrared spectrum.

Radars have been under steady development to track, identify, image, and classify targets. Modern radar systems, with the help of embedded systems, have additional comprehensive signal processing capabilities. They can extract useful information from very noisy data, e.g. interference from the environment and unwanted echoes which is collectively known as clutter in radar terms. Concerning the healthcare industry, radar applications for detection of vital signs, i.e. breathing and heart rate, have been extensively developed during the last few decades. Modern radar systems are expected to be a large part of non-intrusive monitoring in the coming smart home industry, where vital signs need to be monitored in the currently aging population. The research presented here is to break new ground in the radar-based healthcare technology, enabling detection of cold-induced shivering to such level that the micro-shivering can be clearly identified. To simplify the radar software optimization, a commercially available radar kit with demo application and a muscle model system using a vibration generator is used. The model is quantified through precise measurements. A simulated human body vital sign plus shivering is applied. By optimizing the radar software, the shivering amplitude and frequency are measured.

Behavior Driven Development (BDD) is a modern agile software development approach that originates from Test Driven Development (TDD) and Acceptance Test Driven Development (ATDD). Other than TDD and ATDD, BDD introduces new methods and strategies that intend to discover the behavior of software in greater detail which is achieved through enhanced communication and cooperation between everyone involved in software projects. In this paper it is examined how BDD can be taken even further and be connected to the products of UX-strategy in order to explore the possibilities to improve internal communication within software project teams. The report guides the reader through the theoretical frameworks of BDD, UX-strategy and Communication and presents suggestions of how BDD and the products UX-strategy can be connected to improve communication and understanding.