As drones become more widespread throughout modern industry, the demand for drone automation increases. Drones are used for many applications, but their effectiveness relies heavily on their battery life. By designing, implementing, and evaluating an automatic drone landing and battery exchange system, drone missions can be more streamlined and efficient by eliminating the need for manual battery exchange. Previous projects within this topic rely on high-precision landing combined with a manipulator with low degrees of freedom for battery removal. This project offers a solution that allows less strict landing requirements to better fit drones of different sizes and shapes for a wide variety of applications. This autonomous drone landing and battery exchange system uses a robotic arm with 6 degrees of freedom for battery removal and on-board image processing to locate and land on a large, rotatable landing pad.

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The Internet of Things (IoT) is an umbrella term for smart things connected to the Internet.Precision agriculture is a related concept where connected sensors can be used to facilitate, e.g. more effective farming. At the same time, Bluetooth has been making advancements into IoT with the release of Bluetooth Low Energy (BLE) or Bluetooth smart as it is also known by. This thesis describes the development of a Bluetooth Low Energy moisture- and temperature sensor intended for use in an agricultural wireless sensor network system. The sensor was evaluated based on its effectiveness in agricultural environments and conditions such as weather, elevation and in different crop fields. Bluetooth Low Energy was chosen as the technology for communication by the supervising company due to its inherent support for mobile phone accessibility.Field tests showed that the sensor nodes were largely affected by greenery positioned betweentransmitter and receiver, meaning that these would preferably be placed above growing crops foreffective communication. With ideal placement of the sensor and receiving unit, the signal wouldreach up to 100 m, meaning that a receiving unit would cover a circle area with radius 100 m.Due to Bluetooth being largely integrated in mobile devices it would mean that sensor data couldeasily be made accessible with a mobile app, rather than acquiring data from an online web server.

Background: Actuarial risk estimates are considered the gold-standard way to assess whether forensic psychiatry patients are likely to commit prospective criminal offences. However, these risk estimates cannot individually predict the type of criminal offence a patient will subsequently commit, and often simply assess the general likelihood of crime occurring in a group sample. In order to advance the predictive utility of risk assessments, better statistical strategies are required. Aim: To develop a machine learning model to predict the type of criminal offense committed in forensic psychiatry patients, at an individual level. Method: Machine learning algorithms (Random Forest, Elastic Net, SVM), were applied to a representative and diverse sample of 1240 patients in the forensic mental health system. Clinical, historical, and sociodemographic variables were considered as potential predictors and assessed in a data-driven way. Separate models were created for each type of criminal offence, and feature selection methods were used to improve the interpretability and generalizability of our findings. Results: Sexual and violent crimes can be predicted at an individual level with 83.26% sensitivity and 77.42% specificity using only 20 clinical variables. Likewise, nonviolent, and sexual crimes can be individually predicted with 74.60% sensitivity and 80.65% specificity using 30 clinical variables. Conclusion: The current results suggest that machine learning models have accuracy comparable to existing risk assessment tools (AUCs .70-.80). However, unlike existing risk tools, this approach allows for the prediction of cases at an individual level, which is more clinically useful. The accuracy of prospective models is expected to only improve with further refinement. / Thesis / Master of Science (MSc) / Individuals end up in the forensic mental health system when they commit crimes and are found to be not criminality responsible because of a mental disorder. They are released back into the community when deemed to be low risk. However, it is important to consider the accuracy of the method we use to determine risk at the level of an individual person. Currently, we use group average to assess individual risk, which does not work very well. The range of our predictions become so large, that they are virtually meaningless. In other words, the average of a group is meaningless with respect to you. Instead, statistical models can be developed that can make predictions accurately, and at an individual level. Therefore, the current work sought to predict the types of criminal offences committed, among 1240 forensic patients. Making accurate predictions of the crimes people may commit in the future is urgently needed to identify better strategies to prevent these crimes from occurring in the first place. Here, we show that it is possible to predict the type of criminal offense an individual will later commit, using data that is readily available by clinicians. These models perform similarly to the best risk assessment tools available, but unlike these risk assessment tools, can make predictions at an individual level. It is suggested that similar approaches to the ones outlined in this paper could be used to improve risk prediction models, and aid crime prevention strategies.