Artificiell intelligens syftar på en maskins förmåga att fatta egna beslut. Maskinen skall sedan utföra en handling baserat på beslutet, allt detta utan människans inblandning. Positionsnoggrannheten för fartyg är något som på senare tid ställs allt högre krav på, inte minst i offshoreindustrin. Med hjälp av ett Dynamic Positioning system kan högre noggrannhet uppnås. I detta projekt undersöks vilket system som är lämpligast att använda för ett positioneringssystem för en modell av skolfartyget Calmare Nyckel. Projektet utvärderar positionering med hjälp av fyra accesspunkter jämnt fördelat över två nätverk. Projektet belyser en rad olika tekniker baserade på datasignaler som sedan moduleras av en hårdvaruenhet. Då projektet är av så kallat low-cost resulterade valet i en ESP32 och WiFi som teknik. Laborationer påvisade ett väl fungerande system. Uppmätt noggrannhet var dock inte tillräcklig för att använda rakt av i det fortsatta projektet. / Artificial intelligence is a machines ability to make its own decisions. The machine is then supposed to take action based on the decision, this without the involvement of a human. The positional accuracy for ships is something that has become increasingly more demanding, especially in the offshore industry. With the aid of a Dynamic positioning system, a great accuracy can be achieved. This undertaking investigates which system that will be most suited to use for a positioning system aimed for a model of the ship, Calmare Nyckel. The project evaluates positioning with the aid of four access points evenly distributed over two networks. The project illustrates a number of different techniques based on data signals, which are then modulated by a hardware unit. Because of the low-cost aim, this resulted in the usage of ESP32 and WiFi as the systems of choice. Laborations in the undertaking proved a well working system. Measured accuracy, however, was not sufficient to use directly in the continued project.

The diploma thesis describe design and realization of smart energy meter. The smart energy meter allows remote reading of the measured values and with connected externals modules also switching of connected load. The theoretical part covered measured values definition, required function analysis, components selection and design of smart energy meter. The practical part deal with firmware development, programming, remote control and reading of the measured values. The final chapter deal with realization and calibration of the smart energy meter.

This master thesis deals with the design of beehive security system against unauthorized manipulation. The system uses LoRaWAN wireless communication technology for data transmission. The device records the movement of the beehive using an accelerometer and then locates the beehive using a GPS module. The low-energy system works via a battery-powered ESP32 microcontroller and is located on a beehive. The battery is charged using a solar panel. The system can be modularly expanded with additional sensors to monitor the condition of the bees. The device has been tested in practice, where it has been confirmed that it meets the required properties for securing beehives.

This thesis attempts with the design of a device for contactless control of the computer presentation. In the first part there are theoretically described the methods of contactless computer control. The second part is focused on the description of different types of sensors and also the key elements for this thesis – the development board TinyPICO ESP32 and sensor BNO055. Finally, in the third part there is described the design of the device and gestures.

The diploma thesis deals with measuring pressure in microfluidic systems. The theoretical part of the work is devoted to microfluidics, pressure in liquids, pressure measurement, distribution of pressure sensors, and their specifications. Then follows the hardware and software solution of the measuring system for measuring the pressure in the microfluidic system using microprocessor technology. The practical part covers the hardware and software implementation of the measuring system.

The COVID-19 pandemic brought increased need for measuring human temperature. This thesis deals with solution of using low-cost thermal camera module FLIR Lepton 3.5 for measuring human forehead temperature and examines the real usability of this in terms of an accuracy. In the beginning, I describe various methods of measuring temperature and factors, which can influence the measurement. Subsequently, I examine various factors influencing human body and surface temperature. As of result of thesis is full design and implementation of thermal image system for temperature measurement, which I tested in different environmental conditions and compared its the precision against medical contactless infrared thermometer.

With increasing popularity of indoor farming, more and more home growers are faced with sub-optimal lighting conditions in northern countries or poorly lit windows. We have designed and built a proof-of-concept system capable of reducing electricity cost and CO2 footprint of the electricity used for consumer grade grow lights without adversely impacting the grow cycle of the plant. Our system provides optimal grow light conditions for a given plant while using forecasts and live grid data from the ENTSO-E transparency platform to automatically use or store electricity during low-cost hours and avoid using grid electricity during high-cost hours, but can also be configured to prioritize electricity use when the available grid power’s carbon intensity is low. The system, consisting of a server and an embedded control unit, was designed and implemented according to Nunamaker and Chen’s five-step iterative systems development research method and later evaluated by simulating the system for 14 days using real world sunlight and grid data. The results of the simulation show a significant reduction in both spending and carbon emissions related to electricity use, with figures of 73% and 28%, respectively. However, when accounting for life-cycle cost and emissions from the battery, the prototype in its current configuration is neither profitable nor a net positive for the environment. With changes to battery type and taking advantage of economies of scale, a future version could be economically viable, but to be environmentally sustainable, further advances in eco-friendly battery production are needed.

This thesis presents how deep learning can be utilized for detecting humans ina wildlife setting using image classification. Two different solutions have beenimplemented where both of them use a camera-equipped microprocessor to cap-ture the images. In one of the solutions, the deep learning model is run on themicroprocessor itself, which requires the size of the model to be as small as pos-sible. The other solution sends images from the microprocessor to a more pow-erful computer where a larger object detection model is run. Both solutions areevaluated using standard image classification metrics and compared against eachother. To adapt the models to the wildlife environment,transfer learningis usedwith training data from a similar setting that has been manually collected andannotated. The thesis describes a complete system’s implementation and results,including data transfer, parallel computing, and hardware setup. One of the contributions of this thesis is an algorithm that improves the classifi-cation performance on images where a human is far away from the camera. Thealgorithm detects motion in the images and extracts only the area where thereis movement. This is specifically important on the microprocessor, where theclassification model is too simple to handle those cases. By only applying theclassification model to this area, the task is more simple, resulting in better per-formance. In conclusion, when integrating this algorithm, a model running onthe microprocessor gives sufficient results to run as a camera trap for humans.However, test results show that this implementation is still quite underperform-ing compared to a model that is run on a more powerful computer.

Sjöfartshögskolan i Kalmar har ett långtgående studentprojekt att bygga en modell av skolfartyget M/S Calmare Nyckel som autonomt ska köra runt i en damm på skolan för att visa upp skolans profil. Projektet är tänkt att sammanfoga kunskaper från många olika delar av utbildningen såsom elektronik, programmering, och stabilitet. Detta arbete syftar till att undersöka Ultra Wideband som teknik för positionering av modellen för att i framtiden kunna använda positionen för att navigera i dammen. Specifikt så användes tre chip av typen ESP32 UWB som implementation av Ultra Wideband då det var ett billigt alternativ som verkade lovande. Två av chippen var stillastående på kända positioner och positionen för det tredje chippet räknas ut med hjälp av triangulering. Den uppmätta noggrannheten för systemet anses inte vara tillräckligt bra för att fortsätta med ESP32 UWB i fortsättningen av projektet.En båtmodell av M/S Calmare Nyckel togs fram med hjälp av CAD programmet Autodesk Fusion 360 och tanken är att båtmodellen ska 3D printas på universitetet och sedan byggas ut med RC komponenter så båtmodellen kan göra fart genom vatten. Tanken är även att båtmodellen ska kunna manövrera helt autonomt till en laddstation för att sedan återvända ut på sin planerade rutt. Med hjälp av forum och experter online så har alternativ till vilka typer av komponenter såsom, elmotor, styrservo, propeller och annat som behövs för driften tagits fram. Alternativen är bara förslag på vilka komponenter som rekommenderas och inga exakta modeller. / The Kalmar Maritime Academy have an ongoing student project to build a model of the academy’s training ship M/S Calmare Nyckel. The model is supposed to autonomously sail around a pond located in the academy’s premises to show off the academy’s profile. The project is intended to combine knowledge from many different parts of the program such as electronics, programming, and stability. This project aims to investigate Ultra Wideband as a technology for positioning the model to be able to use the position to navigate the pond. Three ESP32 UWB chips were used as an implementation of Ultra Wideband as it was a cheap alternative that showed promise. Two of the chips were stationary at known positions and the position of the third chip is then calculated using triangulation. The measured accuracy of the system is not considered good enough to continue with the ESP32 UWB in the continuation of the project.A boat model of M/S Calmare Nyckel was produced using the CAD program Autodesk Fusion 360 and the idea is that the boat model can be 3D printed at the university and then expanded with RC components so that the boat model can make speed through water. The idea is also that the boat model should be able to maneuver completely autonomously to a charging station and then return to its planned route. With the help of online forums and experts, alternatives to the types of components such as electric motor, steering servo, propeller, and other things needed for operation have been developed. The options are only suggestions of which components are recommended and not exact models.

Context. Generally, health monitoring systems are used in hospitals, which are pricey and gigantic. But with the up gradation of sensors and modules, these devices are also available in portable sizes. These devices are divided into different types according to the disease. So, our project aims to provide a device with multiple parameters monitoring with fall detection, and it is budget-friendly.  Objectives. The objective of our project is to provide freedom to users and monitor simultaneously, using IoT and sensors. People with an illness that may be physical or mental, children, and older people with some issues. They are the users of our idea. Generally, they need to be monitored by a person, which is costly and requires endurance. So, the main objective is to monitor the patient’s health condition and alert in an emergency and store the data on the user’s health.  Methods. After a lot of observation, we found that we can monitor the patient’s health status using an ESP32 Wroom dev kit, which is a microcontroller that consists of Bluetooth and Wi-Fi. We use an MPU6050 accelerometer that detects the falling and motion of the user using three axial movements. We use MAX30102 pulse oximetry which measures the oxygen level, pulse rate, and temperature. Along with these, we use a mobile application that receives data and stores it.  Results. The device reads the parameters regularly and stores the data in the cloud or mobile application. It contains a push button that alerts the relatives and respected authorities. It transmits the location. Finally, it will trigger the command of alerting when the user falls. Conclusions. Every person can use our health monitoring system. The person should wear the device properly and be connected to the Wi-Fi. Once the fall is detected, the contacts are notified. And the detection is more accurate. Regular usage of the device increases the accuracy and analysis of the user’s health.