1,720,961 research outputs found
Radar Target Recognition via 2-D Sparse Linear Prediction In Missing Data Case
No full textClassical linear prediction methods based on least-square estimation yields radar images with high side lobes and many spurious scattering centers while singular value decomposition (SVD) truncation used to address these issues decreases the dynamic range of the image. So, radar images provided by these methods are not appropriate for classification purposes. In this work, sparsity constraints are induced on the prediction coefficients. The classification results demonstrate that the proposed sparse linear prediction methods give better accuracy rates compared to Multiple Signal Classification (MUSIC) method conventionally used for limited bandwidth-observation angle data. Classification performances of proposed methods are also investigated in case of the missing backscattered data. It is shown that the proposed methods are not affected from the missing data unlike the MUSIC method whose performance decreases with the increase in the percentage of the missing data
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
Enhancing Deep Learning Networks Performance By Using B-Spline Activation Functions for Clutter Removal in GPR
No full text32nd Telecommunications Forum, TELFOR 2024 -- 26 November 2024 through 27 November 2024 -- Belgrade -- 205822Traditional clutter removal methods struggle with complex clutter and multiple targets in Ground-Penetrating Radar images. This study proposes using B-spline activation functions in the deep-learning models to improve clutter removal in GPR. Experimental results demonstrate that B-spline-enhanced models outperform their ReLU-based counterparts, with improvements of up to 2.20% in PSNR, 0.035% in MS-SSIM, and 15.83% in SCR, showcasing their potential for real-world GPR applications. © 2024 IEEE.MEKTAM; TUBITAK BILGEM Mine and Handmade Explosive Countermeasures Test and Research Center; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK, (120E234
Autoencoder Guided Low-Rank Approximation Approach for Clutter Removal in GPR Images
Get PDF47th International Conference on Telecommunications and Signal Processing, TSP 2024 -- 10 July 2024 through 12 July 2024 -- Virtual, Online -- 201450The performance of low-rank and sparse decomposition (LRSD) based clutter removal methods which are widely used in GPR systems depends heavily on the regularization parameter. This study proposes a A parameter-free low-rank approach. The low-rank component recovered by an autoencoder (AE) network is subtracted from the raw image to provide a clutter-free image. Simulation and experimental results validate the superiority of the proposed method compared to the low-rank approach Nonnegative Matrix Factorization (NMF) as well as other LRSD methods: Robust Principal Component Analysis (RPCA), Robust NMF (RNMF), and Robust Autoencoder (RAE). © 2024 IEEE.Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK, (120E234
A Vision-Transformer-Based Approach to Clutter Removal in GPR: DC-ViT
Get PDFSince clutter encountered in ground-penetrating radar (GPR) systems deteriorates the performance of target detection algorithms, clutter removal is an active research area in the GPR community. In this letter, instead of convolutional neural network (CNN) architectures used in the recently proposed deep-learning-based clutter removal methods, we introduce declutter vision transformers (DC-ViTs) to remove the clutter. Transformer encoders in DC-ViT provide an alternative to CNNs which has limitations to capture long-range dependencies due to its local operations. In addition, the implementation of a convolutional layer instead of multilayer perceptron (MLP) in the transformer encoder increases the capturing ability of local dependencies. While deep features are extracted with blocks consisting of transformer encoders arranged sequentially, losses during information flow are reduced using dense connections between these blocks. Our proposed DC-ViT was compared with low-rank and sparse methods such as robust principle component analysis (RPCA), robust nonnegative matrix factorization (RNMF), and CNN-based deep networks such as convolutional autoencoder (CAE) and CR-NET. In comparisons made with the hybrid dataset, DC-ViT is 2.5% better in peak signal-to-noise ratio (PSNR) results than its closest competitor. As a result of the tests, we conducted using our experimental GPR data, and the proposed model provided an improvement of up to 20%, compared with its closest competitor in terms of signal-to-clutter ratio (SCR).Scientific and Technological Research Council of Trkiye (TUBITAK)No Statement Availabl
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