Thanks to the major advancements in hardware and computational power, sensor technology, and artificial intelligence, the race for fully autonomous driving systems is heating up. With a countless number of challenging conditions and driving scenarios, researchers are tackling the most challenging problems in driverless cars. One of the most critical components is the perception module, which enables an autonomous vehicle to "see" and "understand" its surrounding environment. Given that modern vehicles can have large number of sensors and available data streams, this thesis presents a deep learning-based framework that leverages multimodal data – i.e. sensor fusion, to perform the task of 3D object detection and localization. We provide an extensive review of the advancements of deep learning-based methods in computer vision, specifically in 2D and 3D object detection tasks. We also study the progress of the literature in both single-sensor and multi-sensor data fusion techniques. Furthermore, we present an in-depth explanation of our proposed approach that performs sensor fusion using input streams from LiDAR and Camera sensors, aiming to simultaneously perform 2D, 3D, and Bird’s Eye View detection. Our experiments highlight the importance of learnable data fusion mechanisms and multi-task learning, the impact of different CNN design decisions, speed-accuracy tradeoffs, and ways to deal with overfitting in multi-sensor data fusion frameworks.

Fuel performance codes are used to demonstrate with confidencethat nuclear fuel rods will sustain normal operation and transientevents without being damaged. However, the execution time of a typ-ical fuel rod simulation ranges from tens of seconds to minutes which can be impractical in certain applications. In the scope of this work,at least two such applications are identified; code-calibration and fuelcore evaluations. In both of these cases, possible improvements can be obtainedby creating neural network surrogate models. For code calibration,a Deep Neural Network is enough since calibration is performed onmodel constants. But for full-core evaluations, a surrogate model mustbe able to predict a time-dependent target as a function of a time-dependent input. In this work, Temporal Convolutional Networks are investigated for the second application. In both applications, targetdata are generated with a Cladding Oxidation Model. The result of the study shows that both models succeeded in their respective tasks with good performance metrics. However, furtherwork is needed to increase the number of input and target variablesthat the Deep Neural Network can handle, verify the flexibility ofinput data files for the TCN, try out the TCN on a real code, and combine the two models and achieve a broader set of use-cases. / <p>Kursnamn: Fördjupande projektarbete i energisystem</p><p>Kurskod: 1FA394</p>

Deepfakes are an emerging problem in social media and for celebrities and political profiles, it can be devastating to their reputation if the technology ends up in the wrong hands. Creating deepfakes is becoming increasingly easy. Attempts have been made at detecting whether a face in an image is real or not but training these machine learning models can be a very time-consuming process. This research proposes a solution to training deepfake detection models cooperatively on the edge. This is done in order to evaluate if the training process, among other things, can be made more efficient with this approach.  The feasibility of edge training is evaluated by training machine learning models on several different types of iPhone devices. The models are trained using the YOLOv2 object detection system.  To test if the YOLOv2 object detection system is able to distinguish between real and fake human faces in images, several models are trained on a computer. Each model is trained with either different number of iterations or different subsets of data, since these metrics have been identified as important to the performance of the models. The performance of the models is evaluated by measuring the accuracy in detecting deepfakes.  Additionally, the deepfake detection models trained on a computer are ensembled using the bagging ensemble method. This is done in order to evaluate the feasibility of cooperatively training a deepfake detection model by combining several models.  Results show that the proposed solution is not feasible due to the time the training process takes on each mobile device. Additionally, each trained model is about 200 MB, and the size of the ensemble model grows linearly by each model added to the ensemble. This can cause the ensemble model to grow to several hundred gigabytes in size.

Convolutional Neural Networks (CNNs) are a widely accepted means of solving complex classification and detection problems in imaging and speech. However, problem complexity often leads to considerable increases in computation and parameter storage costs. Many successful attempts have been made in effectively reducing these overheads by pruning and compressing large CNNs with only a slight decline in model accuracy. In this study, two pruning methods are implemented and compared on the CIFAR-10 database and an ECG arrhythmia classification task. Each pruning method employs a pruning phase interleaved with a finetuning phase. It is shown that when performing the scale-factor pruning algorithm on ECG, finetuning time can be expedited by 1.4 times over the traditional approach with only 10% of expensive floating-point operations retained, while experiencing no significant impact on accuracy.

This thesis deals with human recognition on a videorecording. Convolution neural network was used for face recognition, from which we will get multidimensional vector, which will allow to determine person&#8217;s identity. There are demands imposed on the system, for it to be able to work in real time and could be used for example for person recognition at various conferences, or as a part of security system. Whole system is written in Python language. Part of this thesis is dataset in form of videorecords with persons.

This thesis deals with the issue of detection of anatomical structures in medical images using convolutional neural networks (CNN). At first there are described methods of machine learning, convolutional neural networks and selected methods for detection using CNN. In this work was created a database of annotated CT images of ten anatomical structures (head, heart, aorta, left and right lung, spine, liver, left and right kidney, spleen). A method for detecting these structures was designed, that contains two approaches of region proposals from image, CNN and postprocessing to obtain the detection result. The designed algorithm was implemented in the Python programming language using the TensorFlow library. Obtained results of validation of the network and the detection results are presented and discussed in the last chapter.

This document describes image compression using different types of neural networks. Features of neural networks like convolutional and recurrent networks are also discussed here. The document contains detailed description of various neural network architectures and their inner workings. In addition, experiments are carried out on various neural network structures and parameters in order to find the most appropriate properties for image compression. Also, there are proposed new concepts for image compression using neural networks that are also immediately tested. Finally, a network of the best concepts and parts discovered during experimentation is designed.

Manual segmentation of cartilage tissue in micro CT images of mouse embryos is a very time consuming process and significantly increases the time required for the research of mammal facial structure development. This problem might be solved by using a fully-automatic segmentation algorithm. In this diploma thesis a fully-automatic segmentation method is proposed using a convolutional neural network trained on manually segmented data. The architecture of the proposed convolutional network is based on the U-Net architecture with it's encoding part substituted for the encoding part of the VGG16 classification convolutional neural network pretrained on the ImageNet database of labeled images. The proposed network achieves Dice coefficient 0.8731 ± 0.0326 in comparison to manually segmented images.

This work focuses on automatization of neural network design via the so-called neuroevolution, which employs evolutionary algorithms to construct artificial neural networks or optimise their parameters. The goal of the project is to design and implement an evolutionary algorithm which can be used in the process of designing and optimizing topologies of convolutional neural networks. The effectiveness of the proposed framework was experimentally evaluated on tasks of image classification on datasets MNIST and CIFAR10 and compared with relevant solutions. The results showed that neuroevolution has a potential to successfully find accurate and effective convolutional neural network architectures.

This thesis deals with the segmentation of cranial bone in CT patient’s data after craniectomy. The U-Net architecture in 2D and 3D variant were selected for the intention of solving this problem. Jaccard index for 2D U-Net was evaluate as 89,4 % and for 3D U-Net it was 67,1 %. In the area after surgical intervention evaluating index has smaller difference between both variant, the average success rate of skull classification was 98,4 % for 2D U-Net and 97,0 % for 3D U-Net.