This thesis focuses on the Israeli detective genre. The Israeli detective genre is quite new in Israeli literature, not many works of the detective genre exist in Israel before 1980's, and even today, the detective genre is not so widespread in Israel. The thesis introduces four authors who represent the period from the beginnings of Israeli detective fiction until today. Batya Gur and Shulamit Lapid were chosen as representatives of the older period, Jair Lapid and Dror Mishani for the 21st century. The works chosen for the analysis are those which are parts of the detective series. Another criterion is the detective has to operate in Israel. The aim of the thesis is to outline the development of the detective novel in Israeli based on the example of selected works of these significant authors. In the opening, the thesis shortly presents a detective novel as a literary genre and also, as a literary genre in Israel. The main body of the thesis consists of four chapters on the authors and their approach to the detective genre while investigating several aspects. Firstly, it focuses on the characters who appear in the chosen novels as a detectives, criminals, and other characters. Secondly, the thesis concentrates on the background of the crime and investigation; where and under which circumstances...

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.