1,720,992 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Multi-Sensor Data Fusion and Performance Bounds
No full textModern autonomous systems, such as unmanned aerial vehicles and self-driving cars, rely heavily on multi-sensor data fusion for robust localization and tracking. While integrating diverse sensing modalities enhances observability, it can also introduce redundancy, increasing computational complexity without proportional gains in performance. This underscores the need for principled frameworks that evaluate sensor information under uncertainty and resource constraints.
This thesis adopts a performance-based perspective using the Posterior Cramér-Rao Lower Bound (PCRLB) to assess estimation accuracy across diverse sensing modalities. By combining PCRLB analysis with tailored measurement models and fusion strategies, the work presents a unified methodology to guide sensor selection, configuration, and deployment in complex operational environments.
Three interrelated investigations are presented. First, in airborne angle-only tracking over uncertain terrain, a biased PCRLB formulation incorporates terrain elevation uncertainty and sensor bias. An estimation algorithm is developed to leverage opportunistic ground targets, supported by a joint filtering framework. This investigation is extended in a later chapter by introducing range-only measurements, terrain-informed CRLB formulations, and trajectory optimization strategies under terrain and bias uncertainty. Second, in GPS-denied scenarios, a decentralized cooperative localization framework is proposed using asynchronous inertial measurements. This system integrates pseudo-measurement fusion and rotation-aware uncertainty propagation, anchored by a tailored analytical PCRLB. Third, for radar-camera fusion in autonomous driving, novel measurement models and asynchronous CRLB formulations are introduced to support performance evaluation, fusion algorithm design, and sensor placement optimization.
Collectively, these contributions illustrate how performance bounds, when integrated into sensor data fusion workflows, can inform the design of efficient, reliable localization and tracking systems. The proposed methods are validated through comprehensive simulations and offer insights for deploying autonomous platforms in complex, real-world environments.DissertationDoctor of Philosophy (PhD)Modern autonomous technologies, such as drones and self-driving cars, rely on combining data from multiple sensors to accurately track their surroundings and navigate. But using too many sensors can overwhelm systems without always improving performance. This research develops smarter ways to select and combine sensors by using a mathematical tool called the Posterior Cramér-Rao Lower Bound (PCRLB), which sets a limit on estimation accuracy.
The thesis presents three connected studies. First, for aerial tracking over uneven terrain, new methods account for terrain uncertainty and sensor bias, and are later extended to include range-only data and trajectory optimization. Second, in GPS-denied settings, a cooperative system uses inertial data to maintain localization. Third, for autonomous vehicles, radar and camera data are fused using proposed models that improve performance and help determine where to place sensors.
Together, these results form a unified framework for building efficient, reliable sensor data fusion systems in real-world autonomous applications
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Investigation of Kronecker-based Recovery in Compressive Sensing
No full textIn this thesis, modified Kronecker-based CS 1-D and 2-D recovery techniques with random and deterministic measurement matrices are investigated to improve signal quality despite resource restricted acquisition. For regular recovery of individual segments of the compressed signal, the measurement and sparsifying matrices are required. While the regular Kronecker-based CS recovery technique uses expanded Kronecker measurement and basis matrices to achieve one-time recovery of a collection of compressively acquired segmented signal, in the proposed modified Kronecker-based CS recovery, a new basis matrix which is an expanded version of the basis matrix is used. The reduction of mutual coherence between the expanded Kronecker measurement and the expanded basis matrix leads to improvement in the recovery of the signal. Deterministic sensing further improves the recovery and preserves the structure of the acquired signal in the compressed domain which can be exploited for compressed domain signal processing algorithms
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