Recent advances in smartphones and affordable open-source hardware platforms have enabled the development of low-cost architectures for Internet-of-Things (IoT)-enabled home automation and security systems. These systems usually consist of sensing and actuating layer that is made up of sensors such as passive infrared sensors, also known as motion sensors; temperature sensors; smoke sensors, and web cameras for security surveillance. These sensors, smart electrical appliances, and other IoT devices connect to the Internet through a home gateway. This paper lays out an architecture for a cost-effective smart door sensor that will inform a user through an Android application, of door open events in a house or office environment. The proposed architecture uses an Arduino-compatible Elegoo Mega 2560 microcontroller board along with the Raspberry Pi 2 board for communicating with a web server that implements a RESTful API. Several programming languages are used in the implementation and further applications of the door sensor are discussed as well as some of its shortcomings such as possible interference from other radio frequency devices.

Recent advances in smartphones and affordable open-source hardware platforms have enabled the development of low-cost architectures for IoT-enabled home automation and security systems. These systems usually consist of a sensing and actuating layer that is made up of sensors such as PIR (Passive Infra-red) sensors, also known as motion sensors; temperature sensors; smoke sensors, and web cameras for security surveillance. These sensors, smart electrical appliances and other IoT devices connect to the Internet through a home gateway. This paper lays out architecture for a cost effective “smart” door sensor that will inform a user through an Android application, of door open events in a house or office environment. The proposed architecture uses an Arduino-compatible Elegoo Mega 2560 microcontroller (MCU) board along with the Raspberry Pi 2 board for communicating with a web server that implements a RESTful API. Several programming languages are used in the implementation and further applications of the door sensor are discussed as well as some of its shortcomings such as possible interference from other RF (Radio Frequency) devices.

A self-driven car is a vehicle that can drive without human intervention by making correct decisions based on the environmental conditions. Since the innovation is in its beginning periods, totally moving beyond the human inclusion is still a long shot. However, rapid technological advancements are being made towards the safety of the driver and the passengers. One such safety feature is a Lane Detection System that empowers vehicle to detect road lane lines in various climate conditions. This research provides a feasible and economical solution to detect the road lane lines while driving in a sunny, rainy, or snowy weather condition. An algorithm is designed to perform real time road lane line detection on a low voltage computer that can be easily powered in a regular auto vehicle. The algorithm runs on a RaspberryPi computer placed inside the car. A camera, attached to the vehicle’s windshield, captures the real time images and passes them to the RaspberryPi for processing. The algorithm processes each frame and determines the lane lines. The detected lane lines can be viewed on a 7 inch display screen connected to the Raspberry Pi. The entire system is mounted inside a Jeep Wrangler to conduct the experiments and is powered by the vehicle’s standard charger of 12V-15V power supply. The algorithm provides approximately 97% accurate detection of road lane lines in all weather conditions.

An algorithm is presented to count bee numbers in images of Langsthroth hive entrances. The algorithm computes approximate bee counts by adjusting the brightness of the image, cropping a white or green area in the image, removing the background and noise from the cropped area, finding the total number of bee pixels, and dividing that number by the average number of pixels in a single bee. On 1005 images with green landing pads, the algorithm achieved an accuracy of 80 percent when compared to the human bee counting. On 776 images with white landing pads, the algorithm achieved an accuracy of 85% compared to the human bee counting.

The essence of this master thesis is design and implementation of sensor system based on robotic framework which is called ROS (Robot Operating System). The main task is to perform detailed analysis and test of capabilities of the framework with final implementation on specific robot application (sensor system) with following evaluation of applicability of the system in mobile robotics. As parallel aim is to create detailed general and practical guide for beginners with ROS which they are also beginners in Linux based operating systems.

The aim of this project is to implement a Radio Controlled (RC) car with a Light Detection and Ranging (LiDAR) sensor and a stereoscopic camera setup based on the Robot Operating System (ROS) to conduct Simultaneous Localization and Mapping (SLAM). The LiDAR sensor used is a 2D LiDAR, RPlidar A1, and the stereoscopic camera setup is made of two monocular cameras, Raspberry Pi Camera v2. The sensors were mounted on the RC car and connected using two Raspberry Pi microcomputers.  The 2D LiDAR sensor was used for two-dimensional mapping and the stereo vision from the camera setup for three-dimensional mapping. RC car movement information, odometry, necessary for SLAM was derived using either the LiDAR data or the data from the stereoscopic camera setup. Two means of SLAM were implemented both separately and together for mapping an office space. The SLAM algorithms adopted the Real Time Appearance Based Mapping (RTAB-map) package in the open-source ROS.    The results of the mapping indicated that the RPlidar A1 was able to provide a precise mapping, but showed difficulty when mapping in large circular patterns as the odometry drift resulted in the mismatch of the current mapping with the earlier mapping of the same positions and secondly in localization when turning quickly. The camera setup derived more information about surrounding and showed more robust odometry. However, the setup performed poorly for the mapping of visual loop closures, i.e., the current mapping did not match the earlier mapping of earlier visited positions.

Recycling is becoming more crucial due to the fast pace of consumerism. This thesis explores how well a robot arm, with three degrees of freedom, can be implemented to give an autonomous recycling process. After the prototyping phase it was found that a cylindrical robot arm was best suited for the project. Computer vision in addition with machine learning is used for sorting and detecting objects. The end effector is a suction cup, connected to a plastic 60-milliliter syringe. Negative pressure is created by pulling and pushing a lead screw connected to a stepper motor. The accuracy of the ML-model, the robot’s movement andmax weight are evaluated. The ML-model is trained to detect four classes; plastic, metal, paper, and glass. The thesis found that the ML-model could classify plastic the most sufficient and paper the least. The robot arm’s movement had an average error of 0.54 cm and the maximum weight was 900 grams. For future development it would be interesting to compare a range of different suction cups, to see how the material, diameter, and depth would affect its ability to pick up various objects. Additionally, the model could be enhanced by training it on a larger dataset.

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To study communication between people about solutions to combat climate change a megagame was created. The megagame is played like a boardgame with multiple boards which various pieces can be placed on. Players play different communities like farmers, elderly and politicians. To study the game the board needs to be tracked. The game goes through many changes to facilitate various tests and to develop the game further. Because of this a general solution is preferred. The solution this paper will investigate is a QR code based one. Various other solutions exist such as computer vision and RFID (Radio Frequency Identification). These solutions have pros and cons. To track pieces on the board a camera is mounted above the board filming the boards tracking QR codes. The data is sent to a central system which combines it with other monitoring systems creating a CSV file. The CSV file can be used by researchers and game developers to track how the game progresses. Results show that the system works and fulfills the needs, but the data needs to be filtered to reduce false information. This paper will discuss difficulties and solutions for implementing a QR code scanner with the purpose of tracking game pieces. Ways to further develop the solution will also be discussed.

The development of smart ID cards necessitates the development of secure methods for verifying the authenticity of these cards by organizations, businesses, and government authorities. This motivated the development of such a scanner capable of reading several ID cards from various countries. 365id presented its solution in 2016 by introducing a unique scanner that can verify the authenticity of ID cards. The verification process works by capturing three images using the scanner's three distinct techniques to validate the ID cards' hidden features. After many years of the company's success and the services it provides, including the 365id scanner, it is now the time to take the scanner a step further and improve its services. Thus, this project was founded by the 365id company to research and generate various suggestions for improving the quality of the scanner's photo environment. Either by modifying the way light is reflected inside the scanner or by modifying the parameters that control the camera's operation and investigating the possibility of replacing the camera with a better alternative. This is to increase the image quality and accuracy of the verification procedure. The results proved that the desired effect could be achieved at the lowest costs by adjusting the camera settings and adding new materials to reduce and isolate the light's reflection inside the scanner. In comparison, the results proved that it is not feasible to replace the camera at present. Nevertheless, it is an option that may be available soon.