The field of autonomous vehicle research is growing rapidly. The Congressional mandate for the military to use unmanned vehicles has, in large part, sparked this growth. In conjunction with this mandate, DARPA sponsored the Urban Challenge, a competition to create fully autonomous vehicles that can operate in urban settings. An extremely important feature of autonomous vehicles, especially in urban locations, is their ability to perceive their environment. The research presented in this thesis is directed toward providing an autonomous vehicle with real-time data that efficiently and compactly represents its forward environment as it navigates an urban area. The information extracted from the environment for this application consists of stop line locations, lane information, and obstacle locations, using a single camera and LIDAR scanner. A road/non-road binary mask is first segmented. From the road information in the mask, the current traveling lane of the vehicle is detected using a minimum distance transform and tracked between frames. The stop lines and obstacles are detected from the non-road information in the mask. Stop lines are detected using a variation of vertical profiling, and obstacles are detected using shape descriptors. A laser rangefinder is used in conjunction with the camera in a primitive form of sensor fusion to create a list of obstacles in the forward environment. Obstacle boundaries, lane points, and stop line centers are then translated from image coordinates to UTM coordinates using a homography transform created during the camera calibration procedure. A novel system for rapid camera calibration was also implemented. Algorithms investigated during the development phase of the project are included in the text for the purposes of explaining design decisions and providing direction to researchers who will continue the work in this field. The results were promising, performing the tasks fairly accurately at a rate of about 20 frames per second, using an Intel Core2 Duo processor with 2 GB RAM.

There are many forms of speed control for an autonomous ground vehicle currently in development. Most use a simple PID controller to achieve a speed specified by a higher-level motion planning algorithm. Simple controllers may not provide a desired acceleration profile for a ground vehicle. Also, without extensive tuning the PID controller may cause excessive speed overshoot and oscillation. This paper examines an approach that was designed to allow a greater degree of control while reducing the computing load on the motion planning software. The SpAM+PI (Speed-based Acceleration Map + Proportional Integral controller) algorithm outlined in this paper uses three inputs: current velocity, desired velocity and desired maximum acceleration, to determine throttle and brake commands that will allow the vehicle to achieve its correct speed. Because this algorithm resides on an external controller it does not add to the computational load of the motion planning computer. Also, with only two inputs that are needed only when there is a change in desired speed or maximum desired acceleration, network traffic between the computers can be greatly reduced. The algorithm uses splines to smoothly plan a speed profile from the vehicle's current speed to its desired speed. It then uses a lookup table to determine the correct pedal position (throttle or brake) using the current vehicle speed and a desired instantaneous acceleration that was determined in the splining step of the algorithm. Once the pedal position is determined a PI controller is used to minimize error in the system. The SpAM+PI approach is a novel approach to the speed control of an autonomous vehicle. This academic experiment is tested using Odin, Team Victor Tango's entry into the 2007 DARPA Urban Challenge which won 3rd place and a $500,000 prize. The evaluation of the algorithm exposed both strengths and weaknesses that guide the next step in the development of a speed control algorithm. / Master of Science

Master`s thesis is focused on mobile robotics and computer vision. There is briefly introduced a library of functions for image processing OpenCV. Then it deals with image processing and navigation of mobile robots using image data. There are described segmentation methods and methods for navigating through feature points.

Although cloud network flows are similar to conventional network flows in many ways, there are some major differences in their statistical characteristics. However, due to the lack of adequate public datasets, the proponents of many existing cloud intrusion detection systems (IDS) have relied on the DARPA dataset which was obtained by simulating a conventional network environment. In the current thesis, we show empirically that the DARPA dataset by failing to meet important statistical characteristics of real-world cloud traffic data centers is inadequate for evaluating cloud IDS. We analyze, as an alternative, a new public dataset collected through cooperation between our lab and a non-profit cloud service provider, which contains benign data and a wide variety of attack data. Furthermore, we present a new hypervisor-based cloud IDS using an instance-oriented feature model and supervised machine learning techniques. We investigate 3 different classifiers: Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) algorithms. Experimental evaluation on a diversified dataset yields a detection rate of 92.08% and a false-positive rate of 1.49% for the random forest, the best performing of the three classifiers. / Graduate

The past decades have seen the state of the art in aerospace system design progress from a scope of simple optimization to one including robustness, with the objective of permitting a single system to perform well even in off-nominal future environments. Integrating flexibility, or the capability to easily modify a system after it has been fielded in response to changing environments, into system design represents a further step forward. One challenge in accomplishing this rests in that the decision-maker must consider not only the present system design decision, but also sequential future design and operation decisions. Despite extensive interest in the topic, the state of the art in designing flexibility into aerospace systems, and particularly space systems, tends to be limited to analyses that are qualitative, deterministic, single-objective, and/or limited to consider a single future time period. To address these gaps, this thesis develops a stochastic, multi-objective, and multi-period framework for integrating flexibility into space system design decisions. Central to the framework are five steps. First, system configuration options are identified and costs of switching from one configuration to another are compiled into a cost transition matrix. Second, probabilities that demand on the system will transition from one mission to another are compiled into a mission demand Markov chain. Third, one performance matrix for each design objective is populated to describe how well the identified system configurations perform in each of the identified mission demand environments. The fourth step employs multi-period decision analysis techniques, including Markov decision processes (MDPs) from the field of operations research, to find efficient paths and policies a decision-maker may follow. The final step examines the implications of these paths and policies for the primary goal of informing initial system selection. Overall, this thesis unifies state-centric concepts of flexibility from economics and engineering literature with sequential decision-making techniques from operations research. The end objective of this thesis' framework and its supporting analytic and computational tools is to enable selection of the next-generation space systems today, tailored to decision-maker budget and performance preferences, that will be best able to adapt and perform in a future of changing environments and requirements. Following extensive theoretical development, the framework and its steps are applied to space system planning problems of (1) DARPA-motivated multiple- or distributed-payload satellite selection and (2) NASA human space exploration architecture selection.

The main task of this master’s thesis is to design circuits for feedback control of the main drives Minidarpa robot. It contains the description of power-driven mobile robot control theory and the DC motor. The second part deals with the design options of the control module and its mechanical design. The last part of the proposal made cascade speed control with current loop by using optimal module and the symmetric optimum methods.

Imagine a world where someone’s personal information is constantly compromised, where federal government entities AKA Big Brother always knows what anyone is Googling, who an individual is texting, and their emoticons on Twitter. Government entities have been doing this for years; they never cared if they were breaking the law or their moral compass of human dignity. Every day the Federal government blatantly siphons data with programs from the original ECHELON to the new series like PRISM and Xkeyscore so they can keep their tabs on issues that are none of their business; namely, the personal lives of millions. Our allies are taking note; some are learning our bad habits, from Government Communications Headquarters’ (GCHQ) mass shadowing sharing plan to America’s Russian inspiration, SORM. Some countries are following the United States’ poster child pose of a Brave New World like order of global events. Others like Germany are showing their resolve in their disdain for the rise of tyranny. Soon, these new found surveillance troubles will test the resolve of the American Constitution and its nation’s strong love and tradition of liberty. Courts are currently at work to resolve how current concepts of liberty and privacy apply to the current conditions facing the privacy of society. It remains to be determined how liberty will be affected as well; liberty for the United States of America, for the European Union, the Russian Federation and for the people of the World in regards to the extent of privacy in today’s blurred privacy expectations.