Gene-finding programs available over the Internet today are shown to be nothing more than guides to possible coding regions in the DNA. The programs often do incorrect predictions. The idea of combining a number of different gene-finding programs arised a couple of years ago. Murakami and Takagi (1998) published one of the first attempts to combine results from gene-finding programs built on different techniques (e.g. artificial neural networks and hidden Markov models). The simple combinations methods used by Murakami and Takagi (1998) indicated that the prediction accuracy could be improved by a combination of programs. In this project artificial neural networks are used to combine the results of the three well-known gene-finding programs GRAILII, FEXH, and GENSCAN. The results show a considerable increase in prediction accuracy compared to the best performing single program GENSCAN

We propose a novel 3D navigation system for autonomous vehicle path-planning. The system processes a point-cloud data from an rgb-d camera and creates a 3D occupancy grid with adaptable cell size. Occupied grid cells contain normal distribution characterizing the data measured in the area of the cell. The normal distributions are then used for cell classification, traversability and collision checking. The space of traversable cells is then used for path-planning. The ability to work in three-dimensional space allows the usage of autonomous robots in highly structured environments with multiple levels, uneven surface or various elevated and underground crossings. That is important for the usage of robots in real- world scenarios, in urban areas or for disaster rescue missions. Powered by TCPDF (www.tcpdf.org)

In this work we present the problem of multi-agent path-finding with dynamic obstacles, a generalisation of multi-agent path-finding (MAPF) in which the environment contains randomly-moving dynamic obstacles. This generalisation can be though of as an abstraction of incomplete knowledge of the environment or as a simplification of the multi-agent path-finding where we do not include all agents in the cooperative planner. We adapt three planning algorithms for MAPF to work in an environment with dy- namic obstacles: Local-Repair A* (LRA*), Windowed Hierarchical Cooper- ative A* (WHCA*) and Operator Decomposition with Independence Detec- tion (OD/ID). In addition, we propose two heuristics for these algorithms in dynamic environments: Path Rejoining and Obstacle Predictor. In our experiments, we find that LRA* and WHCA* are best suited for the dy- namic environment. The Path Rejoining heuristic is successful in improving run-times at a small cost in makespan. Obstacle Prediction is capable of lowering the number of times a plan has to be found, but the overhead of our implementation outweighs any performance benefits in most cases. 1

Diploma thesis describes direction finding principles for passive localization of emitting source. Direction finding system with one rotating antenna was chosen with triangulation calculation of emitter position. Input parameters are position of measurement point and direction of maximum signal receiving level. A suitable digital compass module and GPS receiver module for this measurement was chosen. For this purpose was created a graphic user interface, which allow user to communicate with external devices and control spectrum analyzer. Results of measurements are displayed at map, together with area of possible positions of transmitter. Receiving Yagi antenna is placed on rotator and whole system is placed on roof of car. Whole system was construct and results of measurement are discuss here.

This dissertation investigates the manner in which facts are proven with specific emphasis on the role which the concept of ‘probabilities’ plays in achieving the ‘burden of proof. It is illustrated that the concept of ‘probabilities’ plays a central role throughout the process of determining the accountability of a litigant, including fact-finding during the evaluation of the adduced evidence and the application of the burden of proof. This study distinguishes between the findings of individual facts, as opposed to the finding of whether the case of a party, as reflected by the cumulative effect of the individually proven facts, has been proven. It is submitted that, despite traditionally perceived views, the concept of ‘probabilities’ is applied in exactly the same manner to both these aspects of a legal dispute, the only variable being the degree of probabilities as determined by a specific stage and nature of the litigation. The research focuses on both criminal and civil cases. The dissertation is based on current South African practices as reflected in judgments in different law reports and, to some extent, on English and American legal practices. Copyright / Dissertation (LLM)--University of Pretoria, 2007. / Procedural Law / unrestricted

This thesis is concerned with the problem of planning paths for autonomous cars through a smart traffic intersection. In this thesis, we describe existing concepts for solving this problem and discuss the possibilities of approaching intersection problems theoretically. Then, we choose one specific approach and design a declarative model for solving the problem. We use that model to perform a series of theoretical experiments to test the throughput and the quality of intersection paths described by different graphs. After that, we translate theoretical plans to actions for real robots and run it. In these experiments, we measure the degree of robots desynchronization and performance success of the plans based on the collision rate. We also describe how to improve action translation to achieve better performance than that for real robots following the straightforward plans.

In this thesis, strategies for navigating a crowded area using an autonomous holonomic robot are discussed and evaluated. The focus is set on path planning and the topic is therefore largely decoupled from the prediction (i.e. machine learning) and control theory techniques needed for a practical implementation outside of the simulated environment. Existing methods and algorithms for path planning in highly dynamic environments are compared using several measures via computer simulations in different environments. A new, effective, and yet simple, algorithm is introduced and proven to be useful in certain scenarios. This algorithm, ART, predicts the future states of the crowd and using these predictions finds better paths to the goal than traditional algorithms. / I detta examensarbete utvärderas och diskuteras strategier för navigering bland folk med hjälp av en självstyrd holonomisk robot. Fokus är satt på navigeringsproblemet i sig och närliggande ämnen som maskininlärning och reglerteknik behandlas ej även om en fördjupning på dessa områden vore nödvändigt för en praktisk implementation utanför den simulerade världen. Existerande strategier och algoritmer för navigering av dynamiska miljöer utvärderas genom datorsimuleringar i varierande miljöer. En ny algorithm presenteras och visar sig vara användbar i vissa situationer. Denna algoritm, ART, förutser folkmassans rörelser och använder denna information för att hitta bättre vägar till målet.

The thesis project aims to investigate a Form Finding process as a design tool as well as the generator of a structural system, in this case a process of intersecting cones to create surface network logic, for a sailing school at Lidingö, Stockholm. Specifically the research aims at the blurring of the cone shape and the diffusion of a cellular logic by a process that increases the complexity and definition of the whole system. And as a result, a design method that generates and shapes the space.

Experiencing growth after the diagnosis of a life threatening illness is commonly reported among people living with HIV (PLWH). The links between benefit finding and better adjustment in PLWH have been identified, but it is less clear whether these links vary by ethnicity. Minority stress theory suggests that individuals from minority populations may have unique stress experiences, which can have negative health implications but may also provide opportunity for growth. We hypothesized that the association between benefit finding and psychological adjustment would be stronger for Black (n = 80) than White (n = 87) PLWH. Contrary to predictions, the relationship between benefit finding and better adjustment was significant for White but not Black PLWH. Post-hoc analyses suggested that sexual orientation played role in this relationship. The relationship between benefit finding and psychological adjustment may be complex for Black PLWH, or they may achieve adjustment using other resources.

Indiana University-Purdue University Indianapolis (IUPUI) / The late-onset outcome issue is common in early phase dose- nding clinical trials. This problem becomes more intractable in phase I/II clinical trials because both toxicity and e cacy responses are subject to the late-onset outcome issue. The existing methods applying for the phase I trials cannot be used directly for the phase I/II trial due to a lack of capability to model the joint toxicity{e cacy distribution. We propose a conditional weighted likelihood (CWL) method to circumvent this issue. The key idea of the CWL method is to decompose the joint probability into the product of marginal and conditional probabilities and then weight each probability based on each patient's actual follow-up time. We further extend the proposed method to handle more complex situations where the late-onset outcomes are competing risks or semicompeting risks outcomes. We treat the late-onset competing risks/semi-competing risks outcomes as missing data and develop a series of Bayesian data-augmentation methods to e ciently impute the missing data and draw the posterior samples of the parameters of interest. We also propose adaptive dose- nding algorithms to allocate patients and identify the optimal biological dose during the trial. Simulation studies show that the proposed methods yield desirable operating characteristics and outperform the existing methods.