OCP Based Online Multisensor Data Fusion for Autonomous Ground Vehicle

In this paper, online multisensor data fusion algorithm using CORBA event channel is proposed, in order to deal with simplifying problem in sensor registration and fusion for vehicle’s state estimation. The networked based navigation concept for Autonomous Ground Vehicle (AGV) using several sensors is presented. A simulation of various application scenarios are considered by choosing several parameters of UKF, i.e. weighting constant for sigma points and square root matrix. Normalized mean-square error (MSE) of Monte Carlo simulations are computed and reported in the simulation results. Furthermore, the middleware infrastructure based on Open Control Platform (OCP) to support the interconnection between the whole filter structures also reported

Investigation of complete and incomplete fusion in 7^{7}Li+124^{124}Sn reaction around Coulomb barrier energies

The complete and incomplete fusion cross sections for $^{7}$Li+$^{124}$Sn reaction were measured using online and offline characteristic $\gamma$-ray detection techniques. The complete fusion (CF) cross sections at energies above the Coulomb barrier were found to be suppressed by $\sim$ 26 \% compared to the coupled channel calculations. This suppression observed in complete fusion cross sections is found to be commensurate with the measured total incomplete fusion (ICF) cross sections. There is a distinct feature observed in the ICF cross sections, i.e., $\textit{t}$-capture is found to be dominant than $\alpha$-capture at all the measured energies. A simultaneous explanation of complete, incomplete and total fusion (TF) data was also obtained from the calculations based on Continuum Discretized Coupled Channel method with short range imaginary potentials. The cross section ratios of CF/TF and ICF/TF obtained from the data as well as the calculations showed the dominance of ICF at below barrier energies and CF at above barrier energies.Comment: 9 pages, 8 figure

Network of the Day: Aggregating and Visualizing Entity Networks from Online Sources

This software demonstration paper presents a project on the interactive visualization of social media data. The data presentation fuses German Twitter data and a social relation network extracted from German online news. Such fusion allows for comparative analysis of the two types of media. Our system will additionally enable users to explore relationships between named entities, and to investigate events as they develop over time. Cooperative tagging of relationships is enabled through the active involvement of users. The system is available online for a broad user audience

Online forum thread retrieval using data fusion

Online forums empower people to seek and share information via discussion threads. However, finding threads satisfying a user information need is a daunting task due to information overload. In addition, traditional retrieval techniques do not suit the unique structure of threads because thread retrieval returns threads, whereas traditional retrieval techniques return text messages. A few representations have been proposed to address this problem; and, in some representations aggregating query relevance evidence is an essential step. This thesis proposes several data fusion techniques to aggregate evidence of relevance within and across thread representations. In that regard, this thesis has three contributions. Firstly, this work adapts the Voting Model from the expert finding task to thread retrieval. The adapted Voting Model approaches thread retrieval as a voting process. It ranks a list of messages, then it groups messages based on their parent threads; also, it treats each ranked message as a vote supporting the relevance of its parent thread. To rank parent threads, a data fusion technique aggregates evidence from threads’ ranked messages. Secondly, this study proposes two extensions of the voting model: Top K and Balanced Top K voting models. The Top K model aggregates evidence from only the top K ranked messages from each thread. The Balanced Top K model adds a number of artificial ranked messages to compensate the difference if a thread has less than K ranked messages (a padding step). Experiments with these voting models and thirteen data fusion methods reveal that summing relevance scores of the top K ranked messages from each thread with the padding step outperforms the state of the art on all measures on two datasets. The third contribution of this thesis is a multi-representation thread retrieval using data fusion techniques. In contrast to the Voting Model, data fusion methods were used to fuse several ranked lists of threads instead of a single ranked list of messages. The thread lists were generated by five retrieval methods based on various thread representations; the Voting Model is one of them. The first three methods assume a message to be the unit of indexing, while the latter two assume the title and the concatenation of the thread message texts to be the units of indexing respectively. A thorough evaluation of the performance of data fusion techniques in fusing various combinations of thread representations was conducted. The experimental results show that using the sum of relevance scores or the sum of relevance scores multiplied by the number of retrieving methods to develop multi-representation thread retrieval improves performance and outperforms all individual representation

CaloriNet: From silhouettes to calorie estimation in private environments

We propose a novel deep fusion architecture, CaloriNet, for the online estimation of energy expenditure for free living monitoring in private environments, where RGB data is discarded and replaced by silhouettes. Our fused convolutional neural network architecture is trainable end-to-end, to estimate calorie expenditure, using temporal foreground silhouettes alongside accelerometer data. The network is trained and cross-validated on a publicly available dataset, SPHERE_RGBD + Inertial_calorie. Results show state-of-the-art minimum error on the estimation of energy expenditure (calories per minute), outperforming alternative, standard and single-modal techniques.Comment: 11 pages, 7 figure

Disparity between Fusion Center Web Content and Self-Reported Activity

The fusion center literature is limited and lacks consensus regarding operational focus and strategic priorities. Perhaps the lone consistent finding in this literature is the lack of awareness among outsiders regarding what fusion centers do and the capabilities they provide. Contemporary communication research indicates the Internet serves as the primary source of information to inform what they do not understand. The present study employs a mixed methods approach that combines a content analysis of fusion center web content with fusion center self-report data gleaned from a federally funded project. This study encompasses 74 of the 77 primary and officially recognized fusion centers in the United States. Results indicate that centers provide limited information online about their organization and significantly underreport their activities and capabilities online in comparison to self-reported tasks. Information available online through official fusion centers websites is poor at best. Fusion centers self-report to engage in tasks consistent with their information sharing and analytic mission. A context for the findings is provided in addition to recommendations and study limitations

Remote Sensing Data Visualization, Fusion and Analysis via Giovanni

We describe Giovanni, the NASA Goddard developed online visualization and analysis tool that allows users explore various phenomena without learning remote sensing data formats and downloading voluminous data. Using MODIS aerosol data as an example, we formulate an approach to the data fusion for Giovanni to further enrich online multi-sensor remote sensing data comparison and analysis