This thesis is trying to appraise benefits of implementation Cisco Wide Area Application Services (WAAS) in the organization. It's a relatively new technology, which seeks to answer the question, how to maintain control over the ever-increasing volume of traffic. This constant increase is mainly due to the use of new applications and business processes. WAAS should transparently improve throughput, speed and ease of use of all applications in the organization. The question is if this technology can reduce costs, improve productivity, simplify data protection, or what manner it is compatible with the corporate infrastructure. If is really consolidating and pooling application infrastructure for remote branch back into the data center best current solution, which facilitates access to remote users, in same way to which they are accustomed from the Local Area Network (LAN). The next one sections of this work will be summarized findings on the principle of Cisco WAAS. How the technology works. What is main optimizing method and how is currently use in the organization. It's weaknesses and strengths against the largest suppliers of similar WAN optimization solutions. The next one section will carry out an analysis, which showed us results to determine which applications are suitable for optimization technology and statements for the policy, which will be applied to the applications. This raises so many questions to be answered. The outcome of this work will be whether this technology is really what the organization needs, and whether this technology is applicable to all or only for certain types of applications or application protocols.

Diploma thesis deals with the possibilities of using GPS when analysing road accidents. The history and structure of U.S. global positioning system, Russian GLONASS system and European Galileo system are described. GPS receivers are sorted out by the possibility of use. The thesis deals with the methods of refinement GPS and how the vehicles can be monitored using GPS. In the practical part of this diploma thesis measurements with several types of navigations were made. And a comparison of accuracy of measured data with the real situation where the car was found at the moment of measuring followed and how fast was gone.

This master’s thesis describes a sensorial subsystem of a mobile robot. The thesis mentions the structure and a basis of the satellite navigation. There are also described each systems and visualization techniques as e. g. WGS 84, including also other systems for specification of more accurate position on the Earth (EGNOS, WAAS and others). This thesis describes closely also a differential GPS and the corrections. For the purposes of the thesis there has been assembled GPS software for a fundamental and a remote station. There have been chosen and used modules for receiving a GPS signal and wireless modules for their communication. There are also used outcomes from the project of the Open Street Map in the thesis. Results of this project are described in details, decoded, adjusted and converted into RNDF file. At the end of this thesis there has been composed a function of the program for portable camera which works on principle time of flight. This portable camera is dedicated to look for immediate barrier and also for 3D space view in front of the robot. This thesis also including the process how the power source has been built up for this portable scanner.

In this dissertation, we address the problem of detecting humans and vehicles, tracking them in crowded scenes, and finally determining their activities in aerial video. Even though this is a well explored problem in the field of computer vision, many challenges still remain when one is presented with realistic data. These challenges include large camera motion, strong scene parallax, fast object motion, large object density, strong shadows, and insufficiently large action datasets. Therefore, we propose a number of novel methods based on exploiting scene constraints from the imagery itself to aid in the detection and tracking of objects. We show, via experiments on several datasets, that superior performance is achieved with the use of proposed constraints. First, we tackle the problem of detecting moving, as well as stationary, objects in scenes that contain parallax and shadows. We do this on both regular aerial video, as well as the new and challenging domain of wide area surveillance. This problem poses several challenges: large camera motion, strong parallax, large number of moving objects, small number of pixels on target, single channel data, and low frame-rate of video. We propose a method for detecting moving and stationary objects that overcomes these challenges, and evaluate it on CLIF and VIVID datasets. In order to find moving objects, we use median background modelling which requires few frames to obtain a workable model, and is very robust when there is a large number of moving objects in the scene while the model is being constructed. We then iii remove false detections from parallax and registration errors using gradient information from the background image. Relying merely on motion to detect objects in aerial video may not be sufficient to provide complete information about the observed scene. First of all, objects that are permanently stationary may be of interest as well, for example to determine how long a particular vehicle has been parked at a certain location. Secondly, moving vehicles that are being tracked through the scene may sometimes stop and remain stationary at traffic lights and railroad crossings. These prolonged periods of non-motion make it very difficult for the tracker to maintain the identities of the vehicles. Therefore, there is a clear need for a method that can detect stationary pedestrians and vehicles in UAV imagery. This is a challenging problem due to small number of pixels on the target, which makes it difficult to distinguish objects from background clutter, and results in a much larger search space. We propose a method for constraining the search based on a number of geometric constraints obtained from the metadata. Specifically, we obtain the orientation of the ground plane normal, the orientation of the shadows cast by out of plane objects in the scene, and the relationship between object heights and the size of their corresponding shadows. We utilize the above information in a geometry-based shadow and ground plane normal blob detector, which provides an initial estimation for the locations of shadow casting out of plane (SCOOP) objects in the scene. These SCOOP candidate locations are then classified as either human or clutter using a combination of wavelet features, and a Support Vector Machine. Additionally, we combine regular SCOOP and inverted SCOOP candidates to obtain vehicle candidates. We show impressive results on sequences from VIVID and CLIF datasets, and provide comparative quantitative and qualitative analysis. We also show that we can extend the SCOOP detection method to automatically estimate the iv orientation of the shadow in the image without relying on metadata. This is useful in cases where metadata is either unavailable or erroneous. Simply detecting objects in every frame does not provide sufficient understanding of the nature of their existence in the scene. It may be necessary to know how the objects have travelled through the scene over time and which areas they have visited. Hence, there is a need to maintain the identities of the objects across different time instances. The task of object tracking can be very challenging in videos that have low frame rate, high density, and a very large number of objects, as is the case in the WAAS data. Therefore, we propose a novel method for tracking a large number of densely moving objects in an aerial video. In order to keep the complexity of the tracking problem manageable when dealing with a large number of objects, we divide the scene into grid cells, solve the tracking problem optimally within each cell using bipartite graph matching and then link the tracks across the cells. Besides tractability, grid cells also allow us to define a set of local scene constraints, such as road orientation and object context. We use these constraints as part of cost function to solve the tracking problem; This allows us to track fast-moving objects in low frame rate videos. In addition to moving through the scene, the humans that are present may be performing individual actions that should be detected and recognized by the system. A number of different approaches exist for action recognition in both aerial and ground level video. One of the requirements for the majority of these approaches is the existence of a sizeable dataset of examples of a particular action from which a model of the action can be constructed. Such a luxury is not always possible in aerial scenarios since it may be difficult to fly a large number of missions to observe a particular event multiple times. Therefore, we propose a method for v recognizing human actions in aerial video from as few examples as possible (a single example in the extreme case). We use the bag of words action representation and a 1vsAll multi-class classification framework. We assume that most of the classes have many examples, and construct Support Vector Machine models for each class. Then, we use Support Vector Machines that were trained for classes with many examples to improve the decision function of the Support Vector Machine that was trained using few examples, via late weighted fusion of decision values.

LPV approaches are being published in the Czech Republic nowadays. Their usage is enabled by the EGNOS European satellite augmentation system. However, published decision heights do not allow equivalence with the ILS CAT I precision approach yet. This work presents the model of automated control of aircraft precision approach. Verification of its functionality shows that applicable airspace requirements can be fulfilled for lower values of decision heights than values already published. The model is developed using contemporary methods of model-based development in the tool supporting common processing of both continuous and discrete signals. Although model architecture follows the structure of commonly used ILS system in definition of coordinate system and in establishing control in two separate directions it allows curved approach. Usage of digital navigation data provided by satellite system opens further opportunities in its usage, expansion and improvements. Model functionality in control of flight course, position and height control is verified in the scenarios covering detailed thesis goals. The goals were defined based on definition of precision approach process and include navigation signal drop-out, impact of wind, various flight path angles and curved approach. Analysis of behavior of controlled aircraft dynamics was a stimulator for research of specific system modules up to the application level, i.e. specific simulations of successful precision approaches.