id: 30657
Назва: Simulation of cells for signals intensity transformation in mixed image processors and activation functions of neurons in neural networks
Автори: Krasilenko V., Lazarev A., Nikitovich D.
Ключові слова: self-learning equivalent-convolutional neural structures, equivalent models, continuous-logical operations, 2D spatial function, neuron-equivalentor, current mirror, image intensity transformation, nonlinear processing.
Дата публікації: 2022-02-08 21:00:45
Останні зміни: 2022-02-08 21:00:45
Рік видання: 2021
Аннотація: Abstract - The paper considers results of design, simulation of continuously logical pixel cells (CLPC) based on current mirrors (CM) with functions of preliminary analogue processing for image intensity transformation and coding for construction of mixed image processors (IP) and neural networks (NN). The methodology and principles of construction of such cells are based on the use of piecewise-linear approximation of functions for nonlinear transformation of analog signals. It is shown that for the realization of generalized arbitrary functions by such gamma correctors, it is possible to apply basic step functions with controlled parameters. To implement the basic step functions, it is proposed to use nodes that perform a continuous-logical operation of a limited current difference and are quite simply implemented on current reflectors (VDS). The design and modeling of continuous-logical pixel cells (CLPC) based on VDS in different modes and for different conversion functions. Such CLC has a number of advantages: high speed and reliability, simplicity, small power consumption, high integration level for linear and matrix structures. We show design of CLC variants for photocurrents transformation and their simulations. The basic element of such cells is a scheme that implements the operation of a bounded difference of continuous logic.
URI: http://repository.vsau.org/repository/getfile.php/30657.pdf
Тип виданя: Статті у наукових фахових виданнях України (Copernicus та інші)
Видавництво: Вісник Хмельницького національного університету. Серія: технічні науки. 2021. № 5 (301). С.127-135.
Розташовується в колекціях :
Ким внесений: Адміністратор
Файл : 30657.pdf Розмір : 2017946 байт Формат : Adobe PDF Доступ : Загально доступний
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