This work discusses the algorithms and implementation of a multi-robot system for locating radioactive sources. The estimation algorithm presented in this work is able to fuse measurements collected by γ-ray spectrometers carried by an unmanned aerial and unmanned ground vehicle into a single consistent estimate of the probability distribution over the position of a point source in an environment. By constructing a set of hypotheses on the position of the point source, this method converts a non-linear problem into many independent linear ones. Since the underlying model is probabilistic, candidate paths may be evaluated by their expected reduction in uncertainty, allowing the algorithm to select good paths for vehicles to take. An initial hardware test conducted at Savannah River National Laboratory served as a proof of concept and demonstrated that the algorithm successfully locates a radioactive source in the environment, and moves the vehicle to that location. This approach also demonstrated the capability to utilize radiation data collected from an unmanned aerial vehicle to aid the ground vehicle’s exploration. Subsequent numerical experiments characterized the performance of several reward functions and different exploration algorithms in scenarios covering a range of source strengths and region sizes. These experiments demonstrated the improved performance of planning-based algorithms over the myopic method initially tested in the hardware experiments. / Doctor of Philosophy / This work discusses the use of unmanned aerial and ground vehicles to autonomously locate radioactive materials. Using radiation detectors onboard each vehicle, they are commanded to search the environment using a method that incorporates measurements as they are collected. A mathematical model allows measurements taken from different vehicles in different positions to be combined together. This approach decreases the time required to locate sources by using previously collected measurements to improve the quality of later measurements. This approach also provides a best estimate of the location of a source as data is collected. This algorithm was tested in an experiment conducted at Savannah River National Laboratory. Further numerical experiments were conducted testing different reward functions and exploration algorithms.

Previous research has shown that simultaneous auditory identification of the target in a visual search task can lead to more efficient (i. e. ?flatter?) search functions (Spivey et al. , 2001). Experiment 1 replicates the paradigm of Spivey et al. , providing subjects with auditory identification of the search target either before (<em>Consecutive</em> condition) or simultaneously with (<em>Concurrent</em> condition) the onset of the search task. RT x Set Size slopes in the <em>Concurrent</em> condition are approximately 1/2 as steep as those in the <em>Consecutive</em> condition. Experiment 2 employs a distractor ratio manipulation to test the notion that subjects are using the simultaneous auditory target identification to ?parse? the search set by colour, thus reducing the search set by 1/2. The results of Experiment 2 do not support the notion that subjects are parsing the search set by colour. Experiment 3 addresses the same question as Experiment 2, but obtains the desired distractor ratios by holding the amount of relevantly-coloured items constant while letting overall set size vary. Unlike Experiment 2, Experiment 3 supports the interpretation that subjects are using the auditory target identification to parse the search set.

An outline of supply chains and differences in the problem types is given. The motivation for a generic framework is discussed and explored. A conceptual model is presented along with it application to real world situations; and from this a database model is developed. A MIP and CP implementations are presented; along with alternative formulation which can be use to solve the problems. A local search solution algorithm is presented and shown to have significant benefits. Problem instances are presented which are used to validate the generic models, including a large manufacture and distribution problem. This larger problem instance is not only used to explore the implementation of the models presented, but also to explore the practically of the use of alternative formulation and solving techniques within the generic framework and the effectiveness of such methods including the neighbourhood search solving method. A stochastic dimension to the generic framework is explored, and solution techniques for this extension are explored, demonstrating the use of solution analysis to allow problem simplification and better solutions to be found. Finally the local search algorithm is applied to the larger models that arise from inclusion of scenarios, and the methods is demonstrated to be powerful for finding solutions for these large model that were insoluble using the MIP on the same hardware.

<p>For over 50 years, the Satisfaction of Search effect, and more recently known as the Subsequent Search Miss (SSM) effect, has plagued the field of radiology. Defined as a decrease in additional target accuracy after detecting a prior target in a visual search, SSM errors are known to underlie both real-world search errors (e.g., a radiologist is more likely to miss a tumor if a different tumor was previously detected) and more simplified, lab-based search errors (e.g., an observer is more likely to miss a target ‘T’ if a different target ‘T’ was previously detected). Unfortunately, little was known about this phenomenon’s cognitive underpinnings and SSM errors have proven difficult to eliminate. However, more recently, experimental research has provided evidence for three different theories of SSM errors: the Satisfaction account, the Perceptual Set account, and the Resource Depletion account. A series of studies examined performance in a multiple-target visual search and aimed to provide support for the Resource Depletion account—a first target consumes cognitive resources leaving less available to process additional targets. </p><p>To assess a potential mechanism underlying SSM errors, eye movements were recorded in a multiple-target visual search and were used to explore whether a first target may result in an immediate decrease in second-target accuracy, which is known as an attentional blink. To determine whether other known attentional distractions amplified the effects of finding a first target has on second-target detection, distractors within the immediate vicinity of the targets (i.e., clutter) were measured and compared to accuracy for a second target. To better understand which characteristics of attention were impacted by detecting a first target, individual differences within four characteristics of attention were compared to second-target misses in a multiple-target visual search. </p><p>The results demonstrated that an attentional blink underlies SSM errors with a decrease in second-target accuracy from 135ms-405ms after detection or re-fixating a first target. The effects of clutter were exacerbated after finding a first target causing a greater decrease in second-target accuracy as clutter increased around a second-target. The attentional characteristics of modulation and vigilance were correlated with second- target misses and suggest that worse attentional modulation and vigilance are predictive of more second-target misses. Taken together, these result are used as the foundation to support a new theory of SSM errors, the Flux Capacitor theory. The Flux Capacitor theory predicts that once a target is found, it is maintained as an attentional template in working memory, which consumes attentional resources that could otherwise be used to detect additional targets. This theory not only proposes why attentional resources are consumed by a first target, but encompasses the research in support of all three SSM theories in an effort to establish a grand, unified theory of SSM errors.</p> / Dissertation

In order to improve a user's query and help the user quickly satisfy his/her information need, most search engines provide query suggestions that are meant to be relevant alternatives to the user's query. This thesis builds on the query suggestion system and evaluation methodology described in Shen Jiang's Masters thesis (2008). Jiang's system constructs query suggestions by searching for lexical aliases of web documents and then applying query search to the lexical aliases. A lexical alias for a web document is a list of terms that return the web document in a top-ranked position. Query search is a search process that finds useful combinations of search terms. The main focus of this thesis is to supply alternatives for the components of Jiang's system. We suggest three term scoring mechanisms and generalize Jiang's lexical alias search to be a general search for terms that are useful for constructing good query suggestions. We also replace Jiang's top-down query search by a bottom-up beam search method. We experimentally show that our query suggestion method improves Jiang's system by 30% for short queries and 90% for long queries using Jiang's evaluation method. In addition, we add new evidence supporting Jiang's conclusion that terms in the user's initial query terms are important to include in the query suggestions. In addition, we explore the usefulness of document clustering in creating query suggestions. Our experimental results are the opposite of what we expected: query suggestion based on clustering does not perform nearly as well, in terms of the "coverage" scores we are using for evaluation, as our best method that is not based on document clustering.

After completing this tutorial you will be able to use multiple search terms and other advanced features in "Google." This flash tutorial requires a screen resolution of 1024 x 768 or higher.

The purpose of this paper has been to identify which modern search functions user of Smartphones are using, why just these functions are being used, and how they have affected the way users acquire information. Modern search functions in this paper means searches that are not based on text, i.e. ways to search for information by other means than writing something into a search field. To identify this, two studies were conducted, one with a questionnaire being distributed with the help of Google Docs through social networks, and one with four interviews with users of modern search functions for Smartphones. The study showed that even though 76 % of Smartphone users are aware of at least one modern search function, only 29% uses one on a regular basis. The reason that modern search functions are not more widespread is that they are not sufficiently developed yet, and serious usage is still in the future. Because of this no major impact on search behavior can yet be found.

Previous research has shown that simultaneous auditory identification of the target in a visual search task can lead to more efficient (i. e. ?flatter?) search functions (Spivey et al. , 2001). Experiment 1 replicates the paradigm of Spivey et al. , providing subjects with auditory identification of the search target either before (<em>Consecutive</em> condition) or simultaneously with (<em>Concurrent</em> condition) the onset of the search task. RT x Set Size slopes in the <em>Concurrent</em> condition are approximately 1/2 as steep as those in the <em>Consecutive</em> condition. Experiment 2 employs a distractor ratio manipulation to test the notion that subjects are using the simultaneous auditory target identification to ?parse? the search set by colour, thus reducing the search set by 1/2. The results of Experiment 2 do not support the notion that subjects are parsing the search set by colour. Experiment 3 addresses the same question as Experiment 2, but obtains the desired distractor ratios by holding the amount of relevantly-coloured items constant while letting overall set size vary. Unlike Experiment 2, Experiment 3 supports the interpretation that subjects are using the auditory target identification to parse the search set.

Due to the progress of Internet technology and technical improvement, the growths of multimedia products and services ,such as Multimedia Message Service¡]MMS¡^, Multimedia on Demand¡]MoD¡^, Video Conferencing, and Digital TV, are very fast. All of these services need good video compression and audio compression standards to support. It is impossible to transmit source data of multimedia on networks. Motion Estimation needs the most computing complexity in the video compression. In our research, we focus on how to reduce candidate blocks and keep video quality. We study some fast motion estimation algorithms and architectures, and design a fast motion estimation architecture which supports resolution of 1280x720 at 30fps frame rate in HDTV specification based on hierarchical motion estimation algorithm. In the limit of hardware resources and the compressed video quality, the architecture can improve inter-coding performance. Two adjacent MacroBlocks have similar Motion Vector in our observation. We arrange a 16x8 processing element array to deal with two adjacent MacroBlocks together. The design can reduce a lot of clock cycles in the hierarchical motion estimation architecture, and keep high video quality. Furthermore, we propose a search range prediction method¡]called ASR¡^which reflect the motion behavior of video sequences into search range on MB-By-MB Basis. ASR can reduce the unnecessary operation of candidate blocks and keep very high video quality compared with Full Search Block Matching algorithm by the implementation in official software of the new video compression standard, Joint Model of H.264/AVC.

Due to the information explosion on the Internet, effective information search techniques are required to retrieve the desired information from the Web. Based on much analysis on users' search intention and the variant forms of Web content, we find that both the query and the indexed web content are often associated with various context information, which can provide much essential information to indicate the ranking relevance in Web search. This dissertation seeks to develop new search algorithms and techniques by taking advantage of rich context information to improve search quality and consists of two major parts. In the first part, we study the context of the query in terms of various ranking objectives of different queries. In order to improve the ranking relevance, we propose to incorporate such query context information into the ranking model. Two general approaches will be introduced in the following of this dissertation. The first one proposes to incorporate query difference into ranking by introducing query-dependent loss functions, by optimizing which we can obtain better ranking model. Then, we investigate another approach which applies a divide-and-conquer framework for ranking specialization. The second part of this dissertation investigates how to extract the context of specific Web content and explore them to build more effective search system. This study is based on the new emerging social media content. Unlike traditional Web content, social media content is inherently associated with much new context information, including content semantics and quality, user reputation, and user interactions, all of which provide useful information for acquiring knowledge from social media. In this dissertation, we seek to develop algorithms and techniques for effective knowledge acquisition from collaborative social media environments by using the dynamic context information. We first propose a new general framework for searching social media content, which integrates both the content features and the user interactions. Then, a semi-supervised framework is proposed to explicitly compute content quality and user reputation, which are incorporated into the search framework to improve the search quality. Furthermore, this dissertation also investigates techniques for extracting the structured semantics of social media content as new context information, which is essential for content retrieval and organization.