1,720,969 research outputs found
PERFORMANCE ANALYSES OF OPPORTUNISTIC SPECTRUM ACCESS IN COGNITIVE RADIO NETWORK
No full text[[abstract]]This thesis studies the performance analysis of opportunistic spectrum access (OSA) with imperfect sensing, spectrum backup channels (BCs), and prediction scheme for cognitive radio networks (CRN). The studies have organized in to three works. At first work we study the performance of an opportunistic spectrum access (OSA) system with a general number of channels and imperfect spectrum sensing. The system is modeled as a two-dimensional continuous-time Markov chain. We specify the state-dependent transition rates due to imperfect sensing for the general channel number case by using simple recursive functions. For performance metrics, we consider blocking probability, termination probability and success probability for each type of users. On second work, we study the performance of cognitive radio networks with imperfect spectrum sensing and backup channels (BCs). In case an SU is blocked from PCs, it then checks the BCs to find a free one. The system is modeled as a three-dimensional continuous-time Markov chain. We specify the state-dependent transition rates due to imperfect sensing by simple recursive functions. On the last work we propose a hidden Markov model (HMM) with state prediction for opportunistic spectrum access (OSA) in cognitive radio (CR) networks. In contrast to the traditional scheme relying only on channel sensing for exploring spectrum opportunities, the proposed prediction scheme takes advantage of state prediction, channel sensing, and acknowledgments (ACKs) from the receiver in an attempt to maximize the utility. We impose some constraints on the system parameters and derive thresholds by which we can specify the optimal action. We then conduct simulations to compare the performance of the prediction scheme to that of the traditional scheme.
Keyword: Opportunistic Spectrum Access, cognitive radio network, imperfect sensing, backup channel, prediction scheme, recursive function, two dimensional Markov, Three dimensional Markov, hidden Markov model
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
Short-Term Electricity Load Forecasting Using Bayesian Regularization-Based Neural Network: A Case Study in Langsa City
No full textThis study focuses on enhancing the accuracy of artificial neural network (ANN) methods in electricity load prediction for intelligent energy systems. Various optimization techniques, such as Bayesian regularization, have been introduced to improve model performance and generalization capability. A major challenge in ANN-based prediction models is overfitting, which occurs when the network topology fails to generalize input–output relationships, leading to poor prediction accuracy on unseen data. The research aims to develop an improved electricity load prediction model for Langsa City by applying a Bayesian regularization algorithm to minimize overfitting in the neural network topology. A quantitative experimental approach was used, which multiple ANN architectures with historical electricity load datasets. The Bayesian regularization algorithm optimized weight adjustments and minimized mean squared error during training. Results indicate that the proposed model effectively reduces overfitting and enhances predictive accuracy, achieving a Mean Absolute Percentage Error (MAPE) of 2.45%. These findings demonstrate that Bayesian regularization significantly enhances ANN reliability, stability, and forecasting capability for future intelligent energy management application
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
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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