Robotic industries are growing faster than in any other era with the demand and rise of in home robots or assisted robots. Such a robot should be able to navigate between different rooms in the house autonomously. For autonomous navigation, the robot needs to build a map of the surrounding unknown environment and localize itself within the map. For home robots, distinguishing between different rooms improves the functionality of the robot. In this research, Simultaneously Localization And Mapping (SLAM) utilizing a LiDAR sensor is used to construct the environment map. LiDAR is more accurate and not sensitive to light intensity compared to vision. The SLAM method used is Gmapping to create a map of the environment. Gmapping is one of the robust and user-friendly packages in the Robotic Operating System (ROS), which creates a more accurate map, and requires less computational power. The constructed map is then used for room categorization using Convolutional Neural Network (CNN). Since CNN is one of the powerful techniques to classify the rooms based on the generated 2D map images. To demonstrate the applicability of the approach, simulations and experiments are designed and performed on campus and an apartment environment. The results indicate the Gmapping provides an accurate map. Each room used in the experimental design, undergoes training by using the Convolutional Neural Network with a data set of different apartment maps, to classify the room that was mapped using Gmapping. The room categorization results are compared with other approaches in the literature using the same data set to indicate the performance. The classification results show the applicability of using CNN for room categorization for applications such as assisted robots.

Light Detection And Ranging (LiDAR) is a hot topic today primarily because of its vast importance within autonomous vehicles. LiDAR sensors are capable of capturing and identifying objects in the 3D environment. However, a drawback of LiDAR is that they perform poorly under adverse weather conditions. Noise present in LiDAR scans can be divided into random and pseudo-random noise. Random noise can be modeled and mitigated by statistical means. The same approach works on pseudo-random noise, but it is less effective. For this, Deep Neural Nets (DNN) are better suited. The main goal of this thesis is to investigate how snow can be detected in LiDAR point clouds and filtered out. The dataset used is Winter Adverse DrivingdataSet (WADS). Supervised filtering contains a comparison between statistical filtering and segmentation-based neural networks and is evaluated on recall, precision, and F1. The supervised approach is expanded by investigating an ensemble approach. The supervised result indicates that neural networks have an advantage over statistical filters, and the best result was obtained from the 3D convolution network with an F1 score of 94.58%. Our ensemble approaches improved the F1 score but did not lead to more snow being removed. We determine that an ensemble approach is a sub-optimal way of increasing the prediction performance and holds the drawback of being more complex. We also investigate an unsupervised approach. The unsupervised networks are evaluated on their ability to find noisy data and correct it. Correcting the LiDAR data means predicting new values for detected noise instead of just removing it. Correctness of such predictions is evaluated manually but with the assistance of metrics like PSNR and SSIM. None of the unsupervised networks produced an acceptable result. The reason behind this negative result is investigated and presented in our conclusion, along with a model that suffers none of the flaws pointed out.

Three-dimensional information is often used in robotics and 3D-mapping. There exist several ways to obtain a three-dimensional map. However, the time of flight used in the laser scanners or the structured light utilized by Kinect-like sensors sometimes are not sufficient. In this thesis, we investigate two CNN based stereo matching methods for obtaining 3D-information from a grayscaled pair of rectified images.While the state-of-the-art stereo matching method utilize a Siamese architecture, in this project a two-channel and a two stream network are trained in an attempt to outperform the state-of-the-art. A set of experiments were performed to achieve optimal hyperparameters. By changing one parameter at the time, the networks with architectures mentioned above are trained. After a completed training the networks are evaluated with two criteria, the error rate, and the runtime.Due to time limitations, we were not able to find optimal learning parameters. However, by using settings from [17] we train a two-channel network that performed almost on the same level as the state-of-the-art. The error rate on the test data for our best architecture is 2.64% while the error rate for the state-of-the-art Siamese network is 2.62%. We were not able to achieve better performance than the state-of-the-art, but we believe that it is possible to reduce the error rate further. On the other hand, the state-of-the-art Siamese stereo matching network is more efficient and faster during the disparity estimation. Therefore, if the time efficiency is prioritized, the Siamese based network should be considered.

This diploma thesis explores the theory of possible worlds and its relation to broadcast during the coronavirus pandemic. It perceives news, especially broadcast, as a possible world. Using narrative analysis, this thesis describes the characteristics of two possible worlds that arose during the first wave of the coronavirus pandemic on Czech Television and television Prima. The main goal of this research is to examine what kind of possible worlds have these two TV stations constructed in their main news programs and to point out how these two possible worlds differed. My diploma thesis should primarily contribute to a clear comparison of how the depiction of the coronavirus pandemic differed by individual television channels in the first half of 2020.

Machine vision is hot research topic with findings being published at a high pace and more and more companies currently developing automated vehicles. Robotic lawn mowers are also increasing in popularity but most mowers still use relatively simple methods for cutting the lawn. No previous work has been published on machine learning networks that improved between cutting sessions by automatically collecting data and then used it for training. A data acquisition pipeline and neural network architecture that could help the mower in avoiding collision was therefor developed. Nine neural networks were tested of which a convolutional one reached the highest accuracy. The performance of the data acquisition routine and the networks show that it is possible to design a object detection model that improves between runs.

Much research has been done on the use of machine learning to extract features such as buildings, lakes et cetera from satellite imagery, and while this dataset is valuable for many use cases, it is limited to time periods in which satellites were used. Historical maps have a much greater range of available time periods but the viability of using machine learning to extract data from these has not been investigated to any great extent. This case study uses a real-world use case to show the efficacy of using a U-Net convolutional neural network to extract features drawn on hand-drawn maps. By implementing a random forest as a pre-screener to the U-Net the goal was to filter out noise that could lead to false positives. By filtering out the noise the hope was to increase the accuracy of the U-Net. The pre-screener in this study has not performed well on the dataset and has not improved the performance of the U-Net. The U-Nets ability to extrapolate the location of features not explicitly drawn on the map was not clearly established. The results of this study show that the U-Net CNN could be an invaluable tool for quickly extracting data from this typically cumbersome data source, allowing for easier access to a wealth of data. The fields of archeology and climate science would find this especially useful.

Malware is becoming more intelligent as static methods for blocking communication with Command and Control (C&C) server are becoming obsolete. Domain Generation Algorithms (DGAs) are a common evasion technique that generates pseudo-random domain names to communicate with C&C servers in a difficult way to detect using handcrafted methods. Trying to detect DGAs by looking at the domain name is a broad and efficient approach to detect malware-infected hosts. This gives us the possibility of detecting a wider assortment of malware compared to other techniques, even without knowledge of the malware’s existence. Our study compared the effectiveness of three different machine learning classifiers: Convolutional Neural Network (CNN), Generative Adversarial Network (GAN) and Random Forest (RF) when recognizing patterns and identifying these pseudo-random domains. The result indicates that CNN differed significantly from GAN and RF. It achieved 97.46% accuracy in the final evaluation, while RF achieved 93.89% and GAN achieved 60.39%. In the future, network traffic (efficiency) could be a key component to examine, as productivity may be harmed if the networkis over burdened by domain identification using machine learning algorithms.

Context : Air being the free source is used in different ways commercially. In earlier days windmills generate power, water, and electricity. The excessive establishment of windmills for commercial purposes affected avifauna. Most of the birds lost their lives due to collisions with windmills. Turbines used to generate power near airports are also one of the causes for the extinction of birdlife. According to a survey in 2011 in Canada a total of 23,300 bird deaths were caused by wind turbines and also it is estimated that the number of deaths would increase to 2,33,000 in the following 10-15 years. Objectives : The main objective of this thesis is to find a suitable software solution to detect the birds on a series of grayscale images in real-time and in minimum full HD resolution with at least a 15 FPS rate. User-Driven Design Methodology is used for developing, tools are Python and Open-CV. Methods : In this research, a system is designed to detect the bird in an HD Video. Possible methods that can be considered are convolutional neural networks (CNN), vision based detection with background subtraction, contour detection and confusion matrix classification. These methods detect birds in raw images and with help of a classifier make it possible to see the bird in desired pixels with full resolution. We will investigate a bird classification method consisting of two steps, background subtraction, and then object classification. Background subtraction is a fundamental method to extract moving objects from a fixed background. For classification, we will use the YOLO v3 model version for object classification. Results : The project is expected to result in a system design and prototype for the bird identification on a grayscale video stream in at least full HD resolution in a minimum of 15 FPS. The bird should be distinguished from other moving objects like wind turbine blades, trees, or clouds. The proposed solution should identify up to 5 birds simultaneously. Conclusion : After completing each step and arriving at the classification, the methods we have tried, such as Haar Cascades and mobile-net SSD, were not providing us with the desired results. So we opted to use YOLO v3, which had the best accuracy in classifying different objects. By using the YOLO v3 classifier, we have detected the bird with 95% accuracy, blades with 90% accuracy, clouds with 80% accuracy, trees with 70% accuracy. Moreover, we conclude that there is a need for further empirical validation of the models in full-scale industry trials.

Artificial neural networks (ANNs) are used in many types of computing applications. Traditionally, ANNs have been implemented in software, executing on CPUs and even GPUs, which capitalize on the parallelizable nature of ANNs. More recently, FPGAs have become a target platform for ANN implementations due to their relatively low cost, low power, and flexibility. Some safety-critical applications could benefit from ANNs, but these applications require a certain level of reliability. SRAM-based FPGAs are sensitive to single-event upsets (SEUs), which can lead to faults and errors in execution. However there are techniques that can mask such SEUs and thereby improve the overall design reliability. This thesis evaluates the SEU reliability of neural networks implemented in SRAM-based FPGAs and investigates mitigation techniques against upsets for two case studies. The first was based on the LeNet-5 convolutional neural network and was used to test an implementation with both fault injection and neutron radiation experiments, demonstrating that our fault injection experiments could accurately evaluate SEU reliability of the networks. SEU reliability was improved by selectively applying TMR to the most critical layers of the design, achieving a 35% improvement reliability at an increase in 6.6% resources. The second was an existing neural network called BNN-PYNQ. While the base design was more sensitive to upsets than the CNN previous tested, the TMR technique improved the reliability by approximately 7× in fault injection experiments.

Microscopy imaging based techniques, such as the Cell Painting assay, could be used to generate images that visualize the Mechanism of Action (MoA) of a drug, which could be of great use in drug development. In order to extract information and predict the MoA of a new compound from these images we need powerful image analysis tools. The purpose with this project is to further develop a Deep Learning model to predict the MoA of different drugs from Cell Painting images using Convolutional Neural Networks (CNNs) and Conformal Prediction. The specific task was to compare the accuracy of different CNN architectures and to compare the efficiency of different nonconformity functions.  During the project the CNN architectures ResNet50, ResNet101 and DenseNet121 were compared as well as the nonconformity functions Inverse Probability, Margin and a combination of them both. No significant difference in accuracy between the CNNs and no difference in efficiency between the nonconformity functions was measured. The results showed that the model could predict the MoA of a compound with high accuracy when all compounds were used both in training, validation and test of the model, which validates the implementations. However, it is desirable for the model to be able to predict the MoA of a new compound if the model has been trained on other compounds with the same MoA. This could not be confirmed through this project and the model needs to be further investigated and tested with another dataset in order to be used for that purpose.