Results 1 to 10 of about 5,046,879 (188)
Few-Shot Symbol Detection in Engineering Drawings
Applied Artificial IntelligenceRecently, there has been significant interest in digitizing engineering drawings due to their complexity and practical benefits. Symbol digitization, a critical aspect in this field, is challenging as utilizing Deep Learning-based methods to recognize ...
Laura Jamieson +2 more
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Symbol Detection with Deep Learning
International Journal of Advanced Natural Sciences and Engineering Researches, 2023 This study shows the feasibility of detecting some symbols from a distance. Therefore, it will be possible to use these symbols as identification of things, e.g. people, vehicles, objects, information labels etc. There is such a necessity especially for construction sites to know the rough location of workers.
BECERİKLİ, YAŞAR, ASLAN, FATİH
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Learning for Detection: MIMO-OFDM Symbol Detection Through Downlink Pilots [PDF]
IEEE Transactions on Wireless Communications, 2020 Reservoir computing (RC) is a special recurrent neural network which consists of a fixed high dimensional feature mapping and trained readout weights. In this paper, we introduce a new RC structure for multiple-input, multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) symbol detection, namely windowed echo state network (WESN).
Zhou Zhou, Lingjia Liu, Hao-Hsuan Chang
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Enhancing Cyber Situational Awareness Through Dynamic Adaptive Symbology: The DASS Framework [PDF]
SensorsThe static nature of traditional military symbology, such as MIL-STD-2525D, hinders effective real-time threat detection and response in modern cybersecurity operations.
Nicholas Macrino +2 more
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2D-RC: Two-Dimensional Neural Network Approach for OTFS Symbol Detection [PDF]
IEEE Transactions on Wireless Communications, 2023 Orthogonal time frequency space (OTFS) is a promising modulation scheme for wireless communication in high-mobility scenarios. Recently, a reservoir computing (RC) based approach has been introduced for online subframe-based symbol detection in the OTFS ...
Jiarui Xu +3 more
semanticscholar +1 more source
IEEE Access, 2021 We study the problem of semi-blind channel estimation and symbol detection in the uplink of multi-cell massive MIMO (multi-input multi-output) systems with spatially correlated time-varying channels.
Mort Naraghi-Pour +2 more
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IEEE Access, 2021 In view of reducing the complexity of signal detection in massive multiple-input multiple-output (MIMO) receivers, the use of non-coherent detection is favored over usual coherent techniques that require complex channel estimation.
Omnia Mahmoud +2 more
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Low-Complexity Near-Optimum Symbol Detection Based on Neural Enhancement of Factor Graphs [PDF]
IEEE Transactions on Communications, 2022 We consider the application of the factor graph framework for symbol detection on linear inter-symbol interference channels. Based on the Ungerboeck observation model, a detection algorithm with appealing complexity properties can be derived.
Luca Schmid, L. Schmalen
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Optimal Sequence Detection and Optimal Symbol-by-Symbol Detection: Similar Algorithms [PDF]
IEEE Transactions on Communications, 1982 An algorithm is derived which performs optimal symbol-by-symbol detection of a pulse amplitude modulated sequence. The algorithm is similar to the Viterbi algorithm with the optimality criterion optimal symbol detection rather than optimal sequence detection.
J. Hayes, T. Cover, J. Riera
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Neural Enhancement of Factor Graph-based Symbol Detection [PDF]
International Workshop on Signal Processing Advances in Wireless Communications, 2022 We study the application of the factor graph framework for symbol detection on linear inter-symbol interference channels. Cyclic factor graphs have the potential to yield low-complexity symbol detectors, but are suboptimal if the ubiquitous sum-product ...
Luca Schmid, L. Schmalen
semanticscholar +1 more source