Method of indicators forecasting of biomedical images using a parallel-hierarchical network

Timchenko, Leonid I.; Kokriatskaia, Natalia I.; Pavlov, Sergii; Stepaniuk, Dmytro S.; Січко, Тетяна; Ivasyuk, Igor D.; Dzierżak, Róża; Amirgaliyeva, Saltanat

doi:10.1117/12.2536808

Cited by 2 publications

(4 citation statements)

References 8 publications

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“…The structure of data processing is identified with a directed graph, where nodes correspond to data elements, and directed edges connecting nodes describe various dependencies between elements and are marked accordingly 9 . The concept of a parallel-hierarchical network was considered for the development of a structural-functional model.…”

Section: Research Materials and Methodsmentioning

confidence: 99%

“…The matrix was then transformed based on the proposed "general part" criterion F* 9,10 . An example of such a criterion could be the selection of the smallest (largest) element of the matrix A, selection based on a functional law, associative selection, and so on.…”

Section: 𝑀 𝑖mentioning

confidence: 99%

“…Arrays located at different levels change horizontally over time. This process is the evolution of the array 9 , and the Q* transformation in this case is defined as the operator of array evolution.…”

Section: 𝑌 = (mentioning

confidence: 99%

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Structural-functional model of a parallel-hierarchical optical network as a systematic tool for artificial intelligence methods

Tymchenko,

Kokriatska,

Tverdomed

et al. 2023

Optical Fibers and Their Applications 2023

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The article discusses the challenges of real-time data processing and analyzes various methods used to solve them, with a focus on image processing. It points out the limitations of existing methods and argues for the need to use more effective and modern technologies, proposing parallel-hierarchical networks as a promising solution. The article provides a detailed description of the structural-functional model of this type of network, which involves cyclically transforming the input data matrix using a "common part" criterion and an array evolution operator until a set of individual elements is formed. The proposed model is expected to improve real-time image recognition and can potentially be applied to other fields by using the "common part" criterion.

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Section: Research Materials and Methodsmentioning

confidence: 99%

Section: 𝑀 𝑖mentioning

confidence: 99%

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Structural-functional model of a parallel-hierarchical optical network as a systematic tool for artificial intelligence methods

Tymchenko,

Kokriatska,

Tverdomed

et al. 2023

Optical Fibers and Their Applications 2023

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“…In such a case, additional information about the location of masks within the level needs to be stored in the mask RAM. The arrangement of information in the mask RAM cells for one array packet is presented in figure 4 [13,22,29].…”

Section: Fig 3 Functional Scheme Of Pi Memory Address Blockmentioning

confidence: 99%

Architectural and Structural and Functional Features of the Organization of Parallel-Hierarchical Memory

Timchenko,

Kokriatska,

Tverdomed

et al. 2024

IAPGOS

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Parallel hierarchical memory (PI memory) is a new type of memory that is designed to improve the performance of parallel computing systems. PI memory is composed of two blocks: a mask RAM and a tail element RAM. The mask RAM stores the masks that are used to encode the information, while the tail element RAM stores the actual information. The address block of the PI memory is responsible for generating the physical addresses of the cells where the tail elements and their masks are stored. The address block also stores the field of addresses where the array was written and associates this field of addresses with the corresponding external address used to write the array. The proposed address block structure is able to efficiently generate the physical addresses of the cells where the tail elements and their masks are stored. The address block is also able to store the field of addresses where the array was written and associate this field of addresses with the corresponding external address used to write the array. The proposed address block structure has been implemented in a prototype PI memory. The prototype PI memory has been shown to be able to achieve significant performance improvements over traditional memory architectures. The paper will present a detailed description of the PI transformation algorithm, a description of the different modes of addressing organization that can be used in PI memory, an analysis of the efficiency of parallel-hierarchical memory structures, and a discussion of the challenges and future research directions in the field of PI memory.

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Method of indicators forecasting of biomedical images using a parallel-hierarchical network

Cited by 2 publications

References 8 publications

Structural-functional model of a parallel-hierarchical optical network as a systematic tool for artificial intelligence methods

Structural-functional model of a parallel-hierarchical optical network as a systematic tool for artificial intelligence methods

Architectural and Structural and Functional Features of the Organization of Parallel-Hierarchical Memory

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