Title: Security and usability of standard hash functions, in particular MD-5, SHA-1 and SHA-2 Author: Galaczová Barbora Department: Department of Algebra Supervisor: Doc. RNDr. Tůma Jiří, DrSc., Department of Algebra Consultant: Ing. Budiš Petr, Ph.D. Abstract: In the present work we try to digestedly describe standard hash functions, in particular MD-5, SHA-1 and SHA-2. We describe resume of existing attacks on these hash functions. We closely focused on MD-5 collision attacks, because the other hash function collision attacks result from these. Next we describe posibilities of practical usage of hash function collisions, in particular into the qualified certificates area and possible threats. At the end to the present work we describe new hash functions, which could replace current hash functions. This work also contains software to calculate MD-5 hash and search it`s collisions. The software is based on method invented by Czech cryptoanalytist Vlastimil Klíma. Keywords: hash function, collision, qualified certificate, security.

Objective: Systems that can warn the driver of a possible collision with a vulnerable road user (VRU) have significant safety benefits. However, incorrect warning times can have adverse effects on the driver. If the warning is too late, drivers might not be able to react; if the warning is too early, drivers can become annoyed and might turn off the system. Currently, there are no methods to determine the right timing for a warning to achieve high effectiveness and acceptance by the driver. This study aims to validate a driver model as the basis for selecting appropriate warning times. The timing of the forward collision warnings (FCWs) selected for the current study was based on the comfort boundary (CB) model developed during a previous project, which describes the moment a driver would brake. Drivers’ acceptance toward these warnings was analyzed. The present study was conducted as part of the European research project PROSPECT (“Proactive Safety for Pedestrians and Cyclists”). Methods: Two warnings were selected: One inside the CB and one outside the CB. The scenario tested was a cyclist crossing scenario with time to arrival (TTA) of 4 s (it takes the cyclist 4 s to reach the intersection). The timing of the warning inside the CB was at a time to collision (TTC) of 2.6 s (asymptotic value of the model at TTA = 4 s) and the warning outside the CB was at TTC = 1.7 s (below the lower 95% value at TTA = 4 s). Thirty-one participants took part in the test track study (between-subjects design where warning time was the independent variable). Participants were informed that they could brake any moment after the warning was issued. After the experiment, participants completed an acceptance survey. Results: Participants reacted faster to the warning outside the CB compared to the warning inside the CB. This confirms that the CB model represents the criticality felt by the driver. Participants also rated the warning inside the CB as more disturbing, and they had a higher acceptance of the system with the warning outside the CB. The above results confirm the possibility of developing wellsaccepted warnings based on driver models. Conclusions: Similar to other studies’ results, drivers prefer warning times that compare with their driving behavior. It is important to consider that the study tested only one scenario. In addition, in this study, participants were aware of the appearance of the cyclist and the warning. A further investigation should be conducted to determine the acceptance of distracted drivers.

<div>An Autonomous vehicle depends on the combination of latest technology or the ADAS safety features such as Adaptive cruise control (ACC), Autonomous Emergency Braking (AEB), Automatic Parking, Blind Spot Monitor, Forward Collision Warning or Avoidance (FCW or FCA), Lane Departure Warning. The current trend follows incorporation of these technologies using the Artificial neural network or Deep neural network, as an imitation of the traditionally used algorithms. Recent research in the field of deep learning and development of competent processors for autonomous or self driving car have shown amplitude of prospect, but there are many complexities for hardware deployment because of limited resources such as memory, computational power, and energy. Deployment of several mentioned ADAS safety feature using multiple sensors and individual processors, increases the integration complexity and also results in the distribution of the system, which is very pivotal for autonomous vehicles.</div><div><br></div><div>This thesis attempts to tackle two important adas safety feature: Forward collision Warning, and Object Detection using the machine learning and Deep Neural Networks and there deployment in the autonomous embedded platform.</div><div><br></div><div><div>This thesis proposes the following: </div><div>1. A machine learning based approach for the forward collision warning system in an autonomous vehicle.<br></div><div>2.3-D object detection using Lidar and Camera which is primarily based on Lidar Point Clouds. </div><div><br></div><div>The proposed forward collision warning model is based on the forward facing automotive radar providing the sensed input values such as acceleration, velocity and separation distance to a classifier algorithm which on the basis of supervised learning model, alerts the driver of possible collision. Decision Tress, Linear Regression, Support Vector Machine, Stochastic Gradient Descent, and a Fully Connected Neural Network is used for the prediction purpose.</div><div><br></div><div>The second proposed methods uses object detection architecture, which combines the 2D object detectors and a contemporary 3D deep learning techniques. For this approach, the 2D object detectors is used first, which proposes a 2D bounding box on the images or video frames. Additionally a 3D object detection technique is used where the point clouds are instance segmented and based on raw point clouds density a 3D bounding box is predicted across the previously segmented objects.</div></div>

<div>Self driving cars becoming more popular nowadays, which transport with it's own intelligence and take appropriate actions at adequate time. Safety is the key factor in driving environment. A simple fail of action can cause many fatalities. Computer Vision has major part in achieving this, it help the autonomous vehicle to perceive the surroundings. Detection is a very popular technique in helping to capture the surrounding for an autonomous car. At the same time tracking also has important role in this by providing dynamic of detected objects. Autonomous cars combine a variety of sensors such as RADAR, LiDAR, sonar, GPS, odometry and inertial measurement units to perceive their surroundings. Driver-assistive technologies like Adaptive Cruise Control, Forward Collision Warning system (FCW) and Collision Mitigation by Breaking (CMbB) ensure safety while driving.</div><div>Perceiving the information from environment include setting up sensors on the car. These sensors will collect the data it sees and this will be further processed for taking actions. The sensor system can be a single sensor or multiple sensor. Different sensors have different strengths and weaknesses which makes the combination of them important for technologies like Autonomous Driving. Each sensor will have a limit of accuracy on it's readings, so multi sensor system can help to overcome this defects. This thesis is an attempt to develop a multi sensor multi object tracking method to perceive the surrounding of the ego vehicle. When the Object detection gives information about the presence of objects in a frame, Object Tracking goes beyond simple observation to more useful action of monitoring objects. The experimental results conducted on KITTI dataset indicate that our proposed state estimation system for Multi Object Tracking works well in various challenging environments.</div>

We studied the mutual neutralization of Li+ and H- at effective collision energies of a few hundred meV, which corresponds to temperatures of around 2000 K, in the double ion storage ring DESIREE.We present a new approach to match beam velocities and a new general analysis method for non-fragmenting mutual neutralization at DESIREE.Our results show two features, which we could clearly assign to the product channel into the electronically   excited  3s state of neutral lithium and an unresolved combination of 3p and 3d final state contributions.Branching fractions into 3s are extracted for ten different collision energies via spectral binning and compared to several theoretical investigations and two previous measurements, which focused on the heavier isotope deuterium.We find a significant isotope effect, as theoretically predicted, but in contrast to previous experimental results. The branching fractions agree well with different theoretical approaches using non-empirical couplings and  best with a combination of ab initio potentials and Landau-Zener transition probabilities.

SammanfattningExamensarbetet har syftat till att undersöka möjligheten att utrusta Husqvarnas autonomagräsklippare med avståndssensorer. Dessa ska hindra gräsklipparen från att kollidera med hinder i sinomgivning. Gräsklipparen arbetar i en miljö där väder, temperatur, ljusförhållanden och underlagvarierar kraftigt. Detta innebär att kravet på sensorerna är högt.Av de sensorer som undersökts har det visat sig att ultraljud och ”structured light” är de system sombäst skulle kunna uppfylla all dessa krav till ett rimligt pris.I examensarbetet undersöks därför ultraljudssensorer från företaget Maxbotix närmare ochimplementeras i en prototyp. Fyra stycken sensorer placeras på gräsklipparens kaross. Dessakommunicera med en mikrokontroller som i sin tur förmedlar mätdata till en dator. På datorn körsen sökalgoritm som tolkar och sammanfogar sensorernas mätdata.Efter ett antal tester visar det sig att de sensorer som valts inte uppfyller de krav som ställts. Dockbör den metod och algoritm som används kunna uppfylla kraven om en annan typ av sensor används. / AbstractThis master thesis is aiming to investigate the possibility of adding distance sensors to Husqvarna’sautonomic lawnmower. The goal is to prevent the lawnmower from colliding with obstacles in itssurroundings. The environment where the lawnmower is working is very dynamic in sense ofweather, temperature, ambient lightning and terrain. This gives high requirements on the sensors.Of all the sensors that were examined, ultrasonic and structured lightning came out as the ones bestfit to fill these requirements to a reasonable cost.In this thesis ultrasonic sensors from Maxbotix were implemented on a prototype. Four sensors wereplaced on the lawnmower body and connected to a microcontroller. The microcontroller then passesalong the sensor data to a computer that is running a search algorithm. The algorithm is used tointerpret the data and merge the different measurements.After certain amount of testing it was shown that the chosen sensor did not meet all therequirements. However the method and the chosen algorithm should suffice with another type ofsensor.

In this modern era, it has been very difficult for a driver to judge the distance between a vehicle and obstacle while parking in a blind spot areas like garage. The driver should be aware of the surroundings in order to park a vehicle safely without any damage. But without any guidance it will be difficult for a driver to judge the distance manually which in most of the cases ends up in a collision. This causes damage of property and sometimes leads to the injuries to the people. In this proposed work, a Parking Assistance system is introduced to avoid the collision between a vehicle and an obstacle while parking in a blind spots. While parking, the System detects the presence of obstacles and alerts the driver accordingly. The main objective of this project is to build a system which is used to avoid the collision between a vehicle and an obstacle while parking in a blind spot areas like garage. Parking Assistance System uses ultrasonic sensor to calculate the distance between a vehicle and an obstacle. Arduino board is used as the microcontroller. LED's are used to indicate the respective zones of the vehicle while parking, LCD is used to display the distance between the vehicle and obstacle. A buzzer is used to warm the driver and the people present around the vehicle when the vehicle is too close to the obstacle. The proposed system makes the driver fully aware of the surroundings while parking a vehicle. Parking Assistance System is wall mounted device which is designed to guide and help the driver to park a vehicle safely without any damages while parking in a blind spot areas like garage. The manual efforts to calculate the distance can be avoided and helps in reducing the time consumption. This parking system can be useful and needed to avoid the collision while parking. It prevents accidents and damages caused during the parking.

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This thesis can be viewed as two parts. The first part focuses on performance analysis of millimeter wave (mmWave) communications. We investigate how the interference behaves in the outdoor mesh network operating at 60-GHz when block age and shadowing are present using the probability of collision as a metric, under both the protocol model and the physical model. In contrast with results reported in mmWave mesh networks at 60-GHz that advocates that interference has only a marginal effect, our results show that for a short-range link of 100 m, the collision probability gets considerably larger (beyond 0.1) at the signal-to-interference-plus-noise ratio (SINR) of interest (for example, the reference value is chosen as 15 dB for uncoded quadrature phase shift keying (QPSK)). Compensation or compromise should be made in order to maintain a low probability of collision, either by reducing transmitter node density which is to the detriment of the network connectivity, or by switching to a compact linear antenna array with more at-top elements, which places more stringent requirements in device integration techniques. The second part of this thesis focuses on finding the optimal unmanned aerial vehicle (UAV) deployment in the sense that it can maximize over specific network connectivity. We have introduced a connectivity measure based on the commonly used network connectivity metric, which is refered to as global soft connectivity. This measure can be easily extended to account for different propagation models, such as Rayleigh fading and Nakagami fading. It can also be modified to incorporate the link state probability and beam alignment errors in highly directional networks. As can be shown, under the line-of-sight (LOS) and Rayleigh fading assumptions, the optimization regarding the global soft connectivity can be expressed as a weighted sum of the square of link distances between the nodes within the network, namely the ground-to-ground links, the UAV-to-UAV links and the ground-to-UAV links. This can be shown to be a quadratically constrained quadratic program (QCQP) problem with non-convex constraints. We have also extended our global connectivity to other types of connectivity criteria: network k-section connectivity and k-connectivity. In all the three cases, we have proposed a heuristic and straightforward way of finding the suboptimal UAV locations. The simulation results have shown that all these methods can improve our network connectivity considerably, which can achieve a gain of up to 30% for a five UAV scenario.

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