Predicting when a neonate will fall victim to an infection or a disease allows prevention through early medicine administering. Such physiological conditions can be made visible using infrared thermography (IRT). This is a technique for measuring heat emitted in the infrared spectrum and transforming them into visible signals that can be recorded photographically. This thesis will contribute to the prediction of infection in (pre)term neonates by quantifying the interaction between IRT and a neonatal incubator without (and with) a neonate in it.  A system was designed that consisted of three modules: a measurement (incubator and IRT camera), back-end (embedded system and server), and front-end module. The scope of this thesis is limited to the measurement module and the embedded system of the back-end module. Minimum camera requirements were set up which required the camera to: be inexpensive (i.e. ≤ €1000,-), be mobile, be open-source (for Linux), have a minimum frames-per-second of 5, have a resolution of at least 160x160 pixels with a field of view (FOV) of 27°, sensitivity of < 0.1°C, and safe to the patient. Such a camera was found in the FLIR One Pro. For this thesis a different FLIR camera was used due to lack of budget, namely the FLIR A305sc, which was already available at the TU Delft. The A305sc is not open-source, which required a work-around. The Aravis Open Source Project allowed for communication with the camera. Internal camera parameters had to be determined to calculate temperature based on analogue-to-digital values. ExifTool was used on a file stored by the camera to extract these parameters. This calculated temperature was compared to the temperature as determined by FLIR’s software and led to a difference in the range of 1·106°C. An open-source application was written that can connect with this IRT camera that has a GenICam interface using Aravis. Additionally, this application implemented the temperature calculation based on the internal camera parameters. The hood of the incubator is opaque to infrared, which required the design of a measurement setup to circumvent this. Three different setups were discussed, with the final choice falling on placing the camera in front of an opened incubator porthole on a tripod, and sealing this porthole with high or low density ethyl polyethylene (HDPE/LDPE). Regular H/LDPE used for construction site was found to have a attenuated transmissivity as found in literature. To quantify the interaction between IRT and a neonatal incubator, the IRT measurements were to be compared against the current golden standard sensor, namely thermistors. These sensor values were to be read out from the incubator as this would also be used in the final product. Code was written which allows for automatic detection between the GE GiraffeTM Omnibed and the Dräger Caleo® incubator, automatic connecting, and manipulation of all sensors values to a standard string which allows for easy uploading to the InfluxDB database on the server. To be allowed to perform measurements on human subjects, approval had to be acquired by the human research ethics committee (HREC) of the TU Delft and the respective hospital. A “non-wet medisch wetenschappelijk onderzoek met mensen” (nWMO) request was submitted and approved, which resulted in 25 recorded sick and healthy neonates in incubators divided over two hospitals (the JKZ in The Hague, and the RDGG in Delft), with over 25 hours of recording material. Simultaneously, measurements were performed on an empty incubator to gain an understanding in the behaviour of an incubator when actors from outside interacted with the internal environment. Measurements that were performed included determining the reflected apparent temperature (RAT) for every possible opened porthole and for both incubator types. The RAT for the Caleo was found to be higher for every measurement for the GE. The accuracy of the IRT and hospital skin temperature sensors was compared against a calibrated Pt-100 sensor, which show that the Pt-100 sensor measures an equal value as the hospital skin temperature sensor, whereas The IRT camera measured .6°C higher. The effect of changing the distance on IRT values was measured, which shows that for a distance of 0.2m to 1.2m the accuracy of the IRT camera is within the specified accuracy. Finally, the effect of opening additional portholes on IRT was measured, the effect of the airboost setting on IRT, and the measurement of opening additional portholes was repeated with a different IRT camera. Overall the IRT camera measures a higher temperature than the hospital skin temperature sensors, but follows the skin temperature sensors’ pattern.     ... Read more

New measures have to be taken to combat fatalities caused by traffic accidents. Intelligent vehicles have the potential to increase safety, but depend heavily on their automated perception ability.
Acoustic perception, an unused sensing modality in this field, has potential for the detection of nearby vehicles, an ability both human drivers and autonomous vehicles could use assistance with. In this thesis two existing datasets, AudioSet a large general purpose dataset and RoadCube a small dedicated vehicle recognition set, are evaluated. Furthermore commonly used acoustic features and classifier algorithm are evaluated. Special attention is given to the influence of a moving listener vehicle on the performance. For the evaluation a new dataset, DriveSound, is captured. It contains samples captured from a listener car, both when its moving or idle. Results show that RoadCube can be used for the detection of road vehicles, but only when the listener is idle. The best performing classifier from RoadCube, a Gaussian Mixture Model classifier surpassed classifiers trained on the evaluation dataset itself with a Matthews Correlation Coefficient (MCC) of 0.34. None of the classifiers performed well on the samples captured by a moving listener, except for the DriveSound-driving classifiers. The Support Vector Machine trained on this dataset attained a MCC of 0.56. ... Read more

To bring down the number of traffic accidents and increase people’s mobility companies, such as Robot Engineering Systems (RES) try to put automated vehicles on the road. RES is developing the WEpod, a shuttle capable of autonomously navigating through mixed traffic. This research has been done in cooperation with RES to improve the localization capabilities of the WEpod. The WEpod currently localizes using its GPS and lidar sensors. These have proven to be not accurate and reliable enough to safely navigate through traffic. Therefore, other methods of localization and mapping have been investigated. The primary method investigated in this research is monocular Simultaneous Localization and Mapping (SLAM). Based on literature and practical studies, ORB-SLAM has been chosen as the implementation of SLAM. Unfortunately, ORB-SLAM is unable to initialize the setup when applied on WEpod images. Literature has shown that this problem can be solved by adding depth information to the inputs of ORB-SLAM. Obtaining depth information for the WEpod images is not an arbitrary task. The sensors on the WEpod are not capable of creating the required dense depth-maps. A Convolutional Neural Network (CNN) could be used to create the depth-maps. This research investigates whether adding a depth-estimating CNN solves this initialization problem and increases the tracking accuracy of monocular ORB-SLAM. A well performing CNN is chosen and combined with ORB-SLAM. Images pass through the depth estimating CNN to obtain depth-maps. These depth-maps together with the original images are used in ORB-SLAM, keeping the whole setup monocular. ORB-SLAM with the CNN is first tested on the Kitti dataset. The Kitti dataset is used since monocular ORB- SLAM initializes on Kitti images and ground-truth depth-maps can be obtained for Kitti images. Monocular ORB-SLAM’s tracking accuracy has been compared to ORB-SLAM with ground-truth depth-maps and to ORB-SLAM with estimated depth-maps. This comparison shows that adding estimated depth-maps in- creases the tracking accuracy of ORB-SLAM, but not as much as the ground-truth depth images. The same setup is tested on WEpod images. The CNN is fine-tuned on 7481 Kitti images as well as on 642 WEpod images. The performance on WEpod images of both CNN versions are compared, and used in combination with ORB-SLAM. The CNN fine-tuned on the WEpod images does not perform well, missing details in the estimated depth-maps. However, this is enough to solve the initialization problem of ORB-SLAM. The combination of ORB-SLAM and the Kitti fine-tuned CNN has a better tracking accuracy than ORB-SLAM with the WEpod fine-tuned CNN. It has been shown that the initialization problem on WEpod images is solved as well as the tracking accuracy is increased. These results show that the initialization problem of monocular ORB-SLAM on WEpod images is solved by adding the CNN. This makes it applicable to improve the current localization methods on the WEpod. Using only this setup for localization on the WEpod is not possible yet, more research is necessary. Adding this setup to the current localization methods of the WEpod could increase the localization of the WEpod. This would make it safer for the WEpod to navigate through traffic. This research sets the next step into creating a fully autonomous vehicle which reduces traffic accidents and increases the mobility of people. ... Read more

One of the remaining challenges in the development of intelligent vehicles is the topic of behavior planning in urban scenarios. Based on perception of the environment around the intelligent vehicle, driving behavior has to be optimized to achieve a comfortable driving experience without sacrificing safety. This work covers the development of a novel longitudinal behavior planning method for an intelligent passenger vehicle in urban scenarios. A multi-layer situation analysis architecture consisting of localization, maneuver prediction and trajectory prediction is used to predict traffic object trajectories on urban intersections and roundabouts. A universal model predictive controller is developed to determine the optimal longitudinal acceleration behavior of the intelligent vehicle online in a variety of urban scenarios, based on the predicted trajectories. Real-time simulations show that the behavior planning method is able to maintain safe driving conditions with reasonable comfort during scenarios with varying traffic maneuvers. ... Read more