<p dir="ltr">This research explored a solution to a high accident rate in remotely operating Unmanned Aerial Vehicles (UAVs) in a complex environment; it presented a new Brain-Computer Interface (BCI) enabled supervisory control system to fuse human and machine intelligence seamlessly. This study was highly motivated by the critical need to enhance the safety and reliability of UAV operations, where accidents often stemmed from human errors during manual controls. Existing BCIs confronted the challenge of trading off a fully remote control by humans and an automated control by computers. This study met such a challenge with the proposed supervisory control system to optimize human-machine collaboration, prioritizing safety, adaptability, and precision in operation.</p><p dir="ltr">The research work included designing, training, and testing BCI and the BCI-enabled control system. It was customized to control a UAV where the user’s motion intents and cognitive states were monitored to implement hybrid human and machine controls. The DJI Tello drone was used as an intelligent machine to illustrate the application of the proposed control system and evaluate its effectiveness through two case studies. The first case study was designed to train a subject and assess the confidence level for BCI in capturing and classifying the subject’s motion intents. The second case study illustrated the application of BCI in controlling the drone to fulfill its missions.</p><p dir="ltr">The proposed supervisory control system was at the forefront of cognitive state monitoring to leverage the power of an ML model. This model was innovative compared to conventional methods in that it could capture complicated patterns within raw EEG data and make decisions to adopt an ensemble learning strategy with the XGBoost. One of the key innovations was capturing the user’s intents and interpreting these into control commands using the EmotivBCI app. Despite the headset's predefined set of detectable features, the system could train the user’s mind to generate control commands for all six degrees of freedom of adapting to the quadcopter by creatively combining and extending mental commands, particularly in the context of the Yaw rotation. This strategic manipulation of commands showcased the system's flexibility in accommodating the intricate control requirements of an automated machine.</p><p dir="ltr">Another innovation of the proposed system was its real-time adaptability. The supervisory control system continuously monitors the user's cognitive state, allowing instantaneous adjustments in response to changing conditions. This innovation ensured that the control system was responsive to the user’s intent and adept at prioritizing safety through the arbitrating mechanism when necessary.</p>

This study aimed to develop a novel end-to-end plant diagnosis model for the analysis of plant health conditions in near real-time to optimize the rate of production on farmlands for an intensive, yet environmentally safe farming production to preserve the natural environment. First, field research was conducted to determine the extent of the problems faced by farmers in agricultural production. This allowed us to refine the research statement and the level of technology involved in the production processes. The advantages of unmanned aerial systems were exploited in the continuous monitoring of farm plantations to develop automated and accurate measures of farm conditions. To this end, this thesis applies the Precision Agricultural technology as a data based management system that takes into account spatial variations by using the Global Positioning System, Geographical Information System, remote sensing, yield monitors, mapping, and guidance system for variable rate applications. An unmanned aerial vehicle embedded with an optic and radiometric sensor was used to obtain high spectral resolution images of plantation status during normal production/growth cycle. Then, an ensemble of classifiers with Convolution Neural Networks (CNN) was used as off the shelf feature extractor to train images to develop an end-to-end feature detection and multiclass classification system for plant overall health’s conditions. Whereby previous works have concentrated on using CNN as off the shelf feature extractor and model training to detect only plant diseases from plants. To date, no research has yet been carried out to develop an end-to-end model for the overall plant diagnosis system. Previous studies focused on the detection of diseases at any given time, making it difficult to implement comprehensive real-time PA systems. Applying the pretrained model to the new images showed that the model can accurately predict any plant condition with an average of 97% accuracy.

This project is an examination of the possibility of using existing software to develop Virtual Reality (VR) software that includes key aspects of objects in a user’s surroundings into a virtual environment, producing Augmented Virtuality (AV). A defining limitation is the requirement that the software be consumer-accessible, meaning it needs run on a common smartphone with no additional equipment. Two related AV concepts were considered: shape reconstruction and positional tracking. Two categories of techniques were considered for taking the measurements of reality necessary to achieve those AV concepts using only a monocular RGB camera as sensor: monocular visual SLAM (mvSLAM) and Structure from Motion (SfM). Two lists of requirements were constructed, formalising the notions of AV and consumer-accessibility. A search process was then conducted, where existing software packages were evaluated for their suitability to be included in a piece of software fulfilling all requirements. The evaluations of SfM systems were made in combination with Multi-View Stereo (MVS) systems – a necessary complement for achieving visible shape reconstruction using a system that outputs point clouds. After thoroughly evaluating a variety of software, it was concluded that consumer-accessible AV can not currently be achieved by combining existing packages, due to several issues. While future hardware performance increases and new software implementations would solve complexity and availability issues, some inaccuracy and usability issues are inherent to the limitation of using a monocular camera. / Detta projekt är en undersökning av möjligheten att använda befintlig programvara till att utveckla Virtual Reality (VR)-programvara som infogar framstående aspekter av objekt från en användares omgivning in i en virtuell miljö och därmed skapar Augmented Virtuality (AV). En definierande begränsning är kravet på att programvaran skall vara konsumenttillgänglig, vilket innebär att den behöver kunna köras på en vanlig smartphone utan extra utrustning. Två besläktade AV-koncept beaktades: formrekonstruktion och positionsspårning. Två kategorier av tekniker togs i beaktande, vilka kunde användas för att göra de uppmätningar av verkligheten som var nödvändiga för att uppnå de tänkta AV-koncepten med hjälp av endast en monokulär RGB-kamera som sensor: monocular visual SLAM (mvSLAM) och Structure from Motion (SfM). Två listor med kriterier konstruerades, vilka formaliserade begreppen AV och konsumenttillgänglighet. En sökprocess utfördes sedan, där befintliga programvarupaket utvärderades för sin lämplighet att inkluderas i en programvara som uppfyllde alla kriterier. Utvärderingarna av SfM-system gjordes i kombination med Multi-View Stereo (MVS)-system – ett nödvändigt komplement för att uppnå synlig formrekonstruktion med ett system vars utdata är punktmoln. Efter att noggrant ha utvärderat en mängd programvara var slutsatsen att konsumenttillgänglig AV inte för närvarande kan uppnås genom att kombinera befintliga programvarupaket, på grund av ett antal olika problem. Medan framtida prestandaökningar hos maskinvara och nya programvarutillämpningar skulle lösa problem med komplexitet och tillgänglighet, är vissa problem med tillförlitlighet och användbarhet inneboende hos begränsningen till att använda en monokulär kamera.