1,720,997 research outputs found
Increasing accuracy in image-guided robotic surgery through tip tracking and model-based flexion correction
No full textRobot assistance can enhance minimally invasive image-guided surgery, but flexion of the thin surgical instrument shaft impairs accurate control by creating errors in the kinematic model. Two controller enhancements that can mitigate these errors are improved kinematic models that account for flexing and direct measurement of the instrument tip's position. This paper presents an experiment quantifying the benefits of these enhancements in an effort to inform development of an image-guided robot control system accurate in the presence of quasi-static instrument flexion. The study measured a controller's ability to guide a flexing instrument along user-commanded motions while preventing incursions into a forbidden region virtual fixture. Compared with the controller using neither enhancement, improved kinematics and reduced maximum incursion depth into the forbidden region by 28%, tip tracking by 67%, and both enhancements together by 83%.National Science Foundation (Grant EEC- 9731748
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Get PDFWe 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
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
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Wearable Neuromotor Sensing
No full textMovement is essential to human function and life. Wearable neuromotor sensing enables accessible and non-invasive study of the systems that govern human movement. In this thesis, I present three approaches in materials science, in electronics, and in multimodal sensor fusion for biomechanics, to enhance wearable neuromotor sensing.
This thesis begins by improving signal-to-noise of surface electromyography (EMG). I develop a novel preclinical ex-vivo model to experimentally isolate the bioelectrochemical features of single skin-electrode contact. In this model, soft conductive polymer hydrogels made of PEDOT: PSS present nearly an order of magnitude decrease in the skin- electrode contact impedance (88%, 82%, and 77% at 10Hz, 100Hz, and 1kHz, respectively) when compared to clinical electrodes. Integrating these soft conductive polymer blocks into an adhesive wearable sensor increases the EMG signal-to-noise ratio (average 2.1dB increase, max 3.4dB increase) when compared to clinical electrodes across all human subjects. I demonstrate the utility of this higher fidelity EMG in a neural interface system: EMG-based velocity-control of a robotic arm to complete a pick and place task.
This thesis then expands what can be sensed with EMG electrodes. Changes in electrode-skin conditions due to contact pressure variation, sweat, and dehydration lead to variation in bioimpedance across skin locations and thus variation in the EMG voltages measured at skin electrodes. By combining analog circuits, digital signal processing, and analytic calculations using bioimpedance principles, a novel system enables the decoupling of EMG and bioimpedance signals by simultaneously measuring both signals with the same electrodes already used for EMG. Design rationales for the system are explicitly defined and benchtop characterizations show accurate bioimpedance measurements (R^2 ~ 0.96) under carefully controlled EMG-like signals from a function generator. I demonstrate system utility in vivo during controlled force generation tasks where controlled alteration to subjects’ skin-electrode conditions produce changes in both EMG and bioimpedance.
Finally, this thesis leverages multimodal sensor fusion machine learning to fuse EMG and muscle ultrasound imaging for a critical movement application: balance. Elderly non-fatal falls from balance loss cost American society $50 billion in direct healthcare costs. Ultrasound enables muscle state tracking, especially of deep musculature not accessible to surface EMG. I design a novel multi-stage machine learning architecture with kinematics, ultrasound, and EMG sensing to forecast and to estimate the ankle torques subjects generate in single leg balance when perturbed anteriorly, medially, and laterally. The pipeline results in 6% normalized RMSE in both the sagittal and the frontal plane torque estimation. Furthermore, the pipeline forecasts torques 74 milliseconds in the future even under the influence of perturbations, opening new avenues for balance assistance and diagnosis where future human intent may be useful or essential.
I present preliminary work for a stretchable adhesive EMG array embedded with a flexible ultrasound probe. Such a device would benefit from the bioelectronic performance of PEDOT:PSS. Data from the device could be directly leveraged by the multimodal machine learning pipeline for balance control or other applications in human intent.
Ultimately, my thesis aims to build accessible tools for researchers interested in neuromotor sensing. I build from basic preclinical characterization to applied machine learning forecasting on human subject data. I hope this thesis helps future researchers in their works and encourages readers to mix fields to improve their science
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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