Development of a convolutional neural network joint detector for non-orthogonal multiple access uplink receivers
Raed S. H. AL-Musawi, Ali Hilal Ali, Kadhum Al-Majdi, Sarmad Al Gayar We present a novel approach to signal detection for Non-Orthogonal Multiple Access (NOMA) uplink receivers using Convolutional Neural Networks (CNNs) in a single-shot fashion. The defacto NOMA detection method is the so-called Successive Interference Cancellation which requires precise channel estimation and accurate successive […]
METHODS FOR INCREASING THE CLASSIFICATION ACCURACY BASED ON MODIFICATIONS OF THE BASIC ARCHITECTURE OF CONVOLUTIONAL NEURAL NETWORKS
Svitlana Shapovalova, Yurii Moskalenko Object of research: basic architectures of deep learning neural networks. Investigated problem: insufficient accuracy of solving the classification problem based on the basic architectures of deep learning neural networks. An increase in accuracy requires a significant complication of the architecture, which, in turn, leads to an increase in the required computing resources, as […]
APPLICATION OF A CONVOLUTIONAL NEURAL NETWORK AND A KOHONEN NETWORK FOR ACCELERATED DETECTION AND RECOGNITION OF OBJECTS IN IMAGES
Victor Skuratov, Konstantin Kuzmin , Igor Nelin , Mikhail Sedankin One of the most effective ways to improve the accuracy and speed of algorithms for searching and recognizing objects in images is to pre-select areas of interest in which it is likely to detect objects of interest. To determine areas of interest in a […]
APPLICATION OF A CONVOLUTIONAL NEURAL NETWORK TO CREATE A DETECTOR OF TECHNICAL ANALYSIS FIGURES ON EXCHANGE QUOTES CHARTS
Victor Skuratov, Konstantin Kuzmin, Igor Nelin, Mikhail Sedankin Today, the use of artificial intelligence based on neural networks is the most effective approach to solving image recognition problems. The possibility of using a convolutional neural network to create a pattern detector for technical analysis based on stock chart data has been investigated. The found figures […]
CREATION OF A NEURAL NETWORK ALGORITHM FOR AUTOMATED COLLECTION AND ANALYSIS OF STATISTICS OF EXCHANGE QUOTES GRAPHICS
Victor Skuratov, Konstantin Kuzmin, Igor Nelin, Mikhail Sedankin Currently, the problem of automated data analysis and statistics collection from stock quotation charts has not been fully resolved. Most of the analysis of visual data falls on the physical work of the analyst, or on obsolete software solutions. The process of summarizing the information received from financial markets still […]