1,720,961 research outputs found
Fizik tabanlı derin öğrenme teknikleri ile üç boyutlu yakın alan MIMO radar görüntüleme
No full textNear-field radar imaging systems are used in a wide range of applications, such as medical diagnosis, through-wall imaging, concealed weapon detection, and nondestructive evaluation. In this thesis, we consider the inverse problem of reconstructing the three-dimensional (3D) complex-valued reflectivity distribution of the near-field scene from the sparse multiple-input multiple-output (MIMO) array measurements. Motivated by recent advances, we develop physics-informed deep learning techniques for the image reconstruction and array optimization tasks encountered in near-field MIMO radar imaging. Firstly, we develop a novel plug-and-play (PnP) reconstruction method that exploits deep priors and regularization on the magnitude. Our approach provides a unified general framework to effectively handle arbitrary regularization on the magnitude of a complex-valued unknown and is equally applicable to other radar image formation problems including SAR. Secondly, we focus on existing learned direct inversion methods that enable real-time imaging and perform modifications to improve these methods. We demonstrate the effectiveness of all developed approaches under various compressive and noisy observation scenarios using both simulated and experimental data. We also analyze the resolution achieved at compressive settings with sparse MIMO arrays. The developed methods enable not only state-of-the-art performance for 3D real-world targets but also fast computation. Lastly, we develop a novel method for joint optimization of MIMO arrays and reconstruction methods. We illustrate the performance of the jointly optimized imaging system by utilizing various reconstruction methods and different observation settings, and compare the performance with the commonly used MIMO arrays.Yakın alan radar görüntüleme sistemleri tıbbi teşhis ve gizli silah tespiti gibi çeşitli uygulamalarda kullanılmaktadır. Bu tezde, seyrek çok-girişli çok-çıkışlı (MIMO) radar anten dizisi ölçümlerinden yakın alan sahnesinin üç boyutlu karmaşık değerli yansıtırlık dağılımının geri çatılması ters problemine odaklanılmaktadır. Yakın alan MIMO radar görüntülemesinde karşılaşılan imgenin geri çatılması ve anten dizilimi eniyilemesi problemleri için, son gelişmelerden harekete geçerek, fizik tabanlı ve derin öğrenmeye dayalı teknikler geliştiriyoruz. İlk olarak, imgenin geri çatılması problemi için derin öğrenmeye dayalı önsel bilgilerden ve büyüklük üzerindeki düzenlileştirmelerden yararlanan yeni bir tak-çalıştır yöntemi geliştiriyoruz. Bu yaklaşımımız, karmaşık değerli bilinmeyenin büyüklüğü üzerinde herhangi bir düzenlileştirme uygulayabilmek için genel bir çerçeve sağlamakta ve sentetik açıklıklı radar dahil radar görüntülemede karşılaşılan diğer ters problemlere benzer bir şekilde uygulanabilmektedir. İkinci olarak, mevcut imge oluşturma yöntemlerinden gerçek zamanlı görüntülemeyi mümkün kılan öğrenilmiş direkt evirme yöntemlerine odaklanıyoruz ve bu yöntemleri iyileştiriyoruz. Geliştirilen tüm yaklaşımların başarımını çeşitli ölçüm senaryoları altında hem simüle edilmiş hem de deneysel verileri kullanarak gösteriyoruz. Ayrıca seyrek MIMO dizileriyle sıkıştırmalı örnekleme için elde edilen çözünürlüğü de analiz ediyoruz. Geliştirilen yöntemler, yalnızca üç boyutlu gerçek hedefler için en iyi başarımı sağlamakla kalmayıp aynı zamanda hızlı hesaplanabilmektedir. Son olarak, MIMO dizilimlerinin ve imge oluşturma yöntemlerinin aynı anda eniyilenmesi için yeni bir yöntem geliştiriyoruz. Çeşitli imge oluşturma yöntemleri ve farklı ölçüm senaryoları kullanarak eniyilenmiş görüntüleme sistemlerinin başarımını gösteriyor ve bu başarımı yaygın kullanılan MIMO dizileriyle karşılaştırıyoruz.M.S. - Master of ScienceThis work is supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant 120E505 (1001 Research Program)
Comparison of the Performance of K-Nearest Neighbours and Generalized Neural Network in Construction Crew Productivity Prediction
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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
Plug-and-Play Reconstruction with 3D Deep Prior for Complex-Valued Near-Field MIMO Imaging
No full textVariations 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
Plug-and-Play Reconstruction with 3D Deep Prior for Complex-Valued Near-Field MIMO Imaging
No full textNear-field radar imaging systems are used in a wide range of applications, such as medical diagnosis, through-wall imaging, concealed weapon detection, and nondestructive evaluation. In this paper, we consider the inverse problem of reconstructing the three-dimensional (3D) complex-valued reflectivity distribution of the near-field scene from the sparse multiple-input multiple-output (MIMO) array measurements. Using the alternating direction method of multipliers (ADMM) framework, we formulate this problem by exploiting regularization on the magnitude of the complex-valued reflectivity distribution. We then provide a general expression for the proximal mapping associated with such regularization functionals operating on the magnitude of the complex-valued unknown. By utilizing this expression, we develop a computationally efficient plug-and-play reconstruction method that involves simple update steps both with analytical and deep priors. We illustrate the reconstruction performance of our approach with a 3D deep prior on a synthetic dataset. We also compare the result with the classical back-projection method and magnitude-total variation. Our results demonstrate that significant performance improvement can be achieved with learned 3D priors
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