Vladimir Krasilenko - Academia.edu (original) (raw)

Papers by Vladimir Krasilenko

Vìsnik Hmelʹnicʹkogo nacìonalʹnogo unìversitetu, Feb 23, 2022

Анотація-У статті розглядаються аспекти застосування матричних модифікацій криптосистеми RSA для ... more Анотація-У статті розглядаються аспекти застосування матричних модифікацій криптосистеми RSA для створення на основі матричних моделей та алгоритмів криптоперетворень зображень сліпих електронних цифрових підписів (СЕЦП). Перевагою запропонованих матричних моделей RSA є враховування специфіки зображень та простота адаптації до різних типів та форматів зображень. Наведені формули та алгоритмічні кроки процедур створення СЕЦП та проміжних кроків закриття, зашифрування та розшифрування зображень. Модельними експериментами у програмному середовищі Mathcad Professional, скрінами зі створених програмних модулів продемонстровані функціональні можливості та переваги процедур та алгоритмів створення покращених сліпих ЕЦП матричного типу на текстографічні документи (ТГД) конфіденційного характеру. Наведені результати моделювання процесів створення таких підписів для великоформатних документів у програмному середовищі Mathcad підтвердили адекватність запропонованих моделей перетворень та правильність функціонування та верифікації СЕЦП та дозволили визначити час та обмеження відповідних криптоперетворень. Ключові слова: криптографія, матричні моделі, сліпі електронні цифрові підписи, Mathcad Professional, моделювання, текстографічний документ, зашифрування-розшифрування зображень, криптографічні перетворення інтенсивності зображення, нелінійна обробка.

Herald of Khmelnytskyi National University

The perspective of neural networks equivalental models (EM) base on vector-matrix procedure with ... more The perspective of neural networks equivalental models (EM) base on vector-matrix procedure with basic operations of continuous and neuro-fuzzy logic (equivalence, absolute difference) are shown. Capacity on base EMs exceeded the amount of neurons in 4-10 times. This is larger than others neural networks paradigms. Amount neurons of this neural networks on base EMs may be 10 – 100 thousand. The base operations in EMs are normalized equivalence operations. The family of new operations “equivalence” and “non-equivalence” of neuro-fuzzy logic’s, which we have elaborated on the based of such generalized operations of fuzzy-logic’s as fuzzy negation, t-norm and s-norm are shown. Generalized rules of construction of new functions (operations) “equivalence” which uses operations of t-norm and s-norm to fuzzy negation are proposed. Despite the wide variety of types of operations on fuzzy sets and fuzzy relations and the related variety of new synthesized equivalence operations based on them...

Third International Conference on Applications of Optics and Photonics, 2017

Self-learning equivalent-convolutional neural structures (SLECNS) for auto-coding-decoding and im... more Self-learning equivalent-convolutional neural structures (SLECNS) for auto-coding-decoding and image clustering are discussed. The SLECNS architectures and their spatially invariant equivalent models (SI EMs) using the corresponding matrix-matrix procedures with basic operations of continuous logic and non-linear processing are proposed. These SI EMs have several advantages, such as the ability to recognize image fragments with better efficiency and strong cross correlation. The proposed clustering method of fragments with regard to their structural features is suitable not only for binary, but also color images and combines self-learning and the formation of weight clustered matrix-patterns. Its model is constructed and designed on the basis of recursively processing algorithms and to k-average method. The experimental results confirmed that larger images and 2D binary fragments with a large numbers of elements may be clustered. For the first time the possibility of generalization of these models for space invariant case is shown. The experiment for an image with dimension of 256x256 (a reference array) and fragments with dimensions of 7x7 and 21x21 for clustering is carried out. The experiments, using the software environment Mathcad, showed that the proposed method is universal, has a significant convergence, the small number of iterations is easily, displayed on the matrix structure, and confirmed its prospects. Thus, to understand the mechanisms of self-learning equivalence-convolutional clustering, accompanying her to the competitive processes in neurons, and the neural auto-encoding-decoding and recognition principles with the use of self-learning cluster patterns is very important which used the algorithm and the principles of non-linear processing of two-dimensional spatial functions of images comparison. These SIEMs can simply describe the signals processing during the all training and recognition stages and they are suitable for unipolar-coding multilevel signals. We show that the implementation of SLECNS based on known equivalentors or traditional correlators is possible if they are based on proposed equivalental two-dimensional functions of image similarity. The clustering efficiency in such models and their implementation depends on the discriminant properties of neural elements of hidden layers. Therefore, the main models and architecture parameters and characteristics depends on the applied types of non-linear processing and function used for image comparison or for adaptive-equivalental weighing of input patterns. Real model experiments in Mathcad are demonstrated, which confirm that non-linear processing on equivalent functions allows you to determine the neuron winners and adjust the weight matrix. Experimental results have shown that such models can be successfully used for auto- and hetero-associative recognition. They can also be used to explain some mechanisms known as "focus" and "competing gain-inhibition concept". The SLECNS architecture and hardware implementations of its basic nodes based on multi-channel convolvers and correlators with time integration are proposed. The parameters and performance of such architectures are estimated.

Machine Vision and Navigation, 2019

The urgent need to create video sensors and processors for parallel (simultaneous by pixel) image... more The urgent need to create video sensors and processors for parallel (simultaneous by pixel) image processing with advanced functionality and multichannel picture outputs is shown in the chapter. We consider perspective spheres and areas of application of such sensor processors, in particular, for hardware high-performance architectures of neural networks, convolutional neural structures, parallel matrix-matrix multipliers, and special processor systems. We show and analyze the theoretical foundations, the mathematical apparatus of the matrix and continuous logic, and their basic operations, show their functional completeness, and evaluate their advantages and prospects for application in the design of biologically inspired devices and systems for processing and analysis of array signals. We show that some functions of continuous logic, including operations of normalized equivalence of vector and matrix signals, the operation of a limited difference in continuous logic, are a powerfu...

Herald of Khmelnytskyi National University, 2021

The paper considers results of design, simulation of continuously logical pixel cells (CLPC) base... more 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...

Optical Sensors 2013, 2013

The paper considers results of design and modeling of continuously logical analog-to-digital conv... more The paper considers results of design and modeling of continuously logical analog-to-digital converters (ADC) based on current mirrors for image processor and multichannel optical sensor systems with parallel inputs-outputs. For such multichannel serial-parallel analog-to-digital converters (SP ADC) it is needed base photoelectron cells, which are considered in paper. Its have a number of advantages: high speed and reliability, simplicity, small power consumption, high integration level for linear and matrix structures. We show design of the continuously logical ADC of photocurrents and its base digit cells (ABC) and its simulations. We consider CL ADC for Gray and binary codes. Each channel of the structure consists of several base digit cells (ABC) on 20-30 CMOS FETs and one photodiode. The supply voltage of the ABC is 1-3.3V, the range of an input photocurrent is 0.1 – 10μA, the transformation time is 30ns at 5-8 bit binary or Gray codes, power consumption is about 1mW. One channel of ADC with iteration is based on one ABC-3(G) and SHD, and it has only 40 CMOS transistors. The general power consumption of the ADC, in this case, is only 50-100μW, if the maximum input current is 1μA. The CL ADC opens new prospects for realization of linear and matrix image processor and photo-electronic structures with picture operands, which are necessary for neural networks, digital optoelectronic processors, neural-fuzzy controllers, and so forth.

Neuro-inspired Photonic Computing, 2018

In the paper, we consider the urgent need to create highly efficient hardware accelerators for ma... more In the paper, we consider the urgent need to create highly efficient hardware accelerators for machine learning algorithms, including convolutional and deep neural networks (CNN and DNNS), for associative memory models, clustering, and pattern recognition. These algorithms usually include a large number of multiply-accumulate (and the like) operations. We show a brief overview of our related works the advantages of the equivalent models (EM) for describing and designing neural networks and recognizing bio-inspired systems. The capacity of NN on the basis of EM and of its modifications, including auto-and hetero-associative memories for 2D images, is in several times quantity of neurons. Such neuroparadigms are very perspective for processing, clustering, recognition, storing large size and strongly correlated and highly noised images. They are also very promising for solving the problem of creating machine uncontrolled learning. And since the basic operational functional nodes of EM are such vector-matrix or matrix-tensor procedures with continuous-logical operations as: normalized vector operations "equivalence", "nonequivalence", "autoequivalence", "auto-nonequivalence", we consider in this paper new conceptual approaches to the design of full-scale arrays of such neuron-equivalentors (NEs) with extended functionality, including different activation functions. Our approach is based on the use of analog and mixed (with special coding) methods for implementing the required operations, building NEs (with number of synapsis from 8 up to 128 and more) and their base cells, nodes based on photosensitive elements and CMOS current mirrors. We show the results of modeling the proposed new modularscalable implementations of NEs, we estimates and compare them. Simulation results show that processing time in such circuits does not exceed units of micro seconds, and for some variants 50-100 nanoseconds. Circuits are simple, have low supply voltage (1.5 – 3.3 V), low power consumption (milliwatts), low levels of input signals (microwatts), integrated construction, satisfy the problem of interconnections and cascading. Signals at the output of such neurons can be both digital and analog, or hybrid, and also with two complement outputs. They realize principle of dualism which gives a number of advantages of such complement dual NEs.

Emerging Imaging and Sensing Technologies for Security and Defence IV, 2019

Optics and Photonics for Information Processing XII, 2018

The paper considers results of design and simulation of continuously logical cells (CLC) based on... more The paper considers results of design and simulation of continuously logical cells (CLC) 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). For such IP and NN with vector or matrix parallel inputsoutputs, it is needed active basic photosensitive cells with an extended electronic circuit, which are considered in paper. 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 coding and their various possible implementations and simulations. The basic element of such cells is a scheme that implements the operation of a bounded difference of continuous logic. Using a set of such circuits implemented on traditional CMOS technology, we consider generalized decomposition and other methods for designing cells for nonlinear conversion of the photocurrent intensity, which makes it easy to realize the required nonlinear conversion function. Selection of the appropriate parameters, which can be specified as constructive constants or as parameters for external control, allows changing type of synthesized functions. We also consider the applications of such parallel matrix arrays for the creation of advanced IP and NN. We show the need for various types of converting and coding the photocurrents intensity in such parallel systems and sensory devices, especially for the implementation of various types of activation functions in the hardware implementations of neural networks. Such cells consist of several dozen CMOS transistors, have low power supply voltage (1.8 ÷ 3.3V), the range of an input photocurrent is 0.1÷24μA, the transformation time is less than 1 μs, low power consumption (microwatts). We also consider the cells for ADC after the intensity conversion. Each channel consists of several digital-analog cells (DC). The amount of DC is not exclusive to the number of digits of the formed code, and for an iteration type, only one cell of DC, complemented by the device of selection and holding (SHD), is required. One channel of ADC with iteration is based on one DC and SHD, and it has only 35 CMOS transistors. The general power consumption of the ADC with iteration is only 50÷100μW, if the maximum input current is 4μA. The ADCs with 6-8 bit binary or Gray codes have good dynamic characteristics (frequency of digitization even for 1.5μm CMOS-technologies is 10MHz and more). In such ADCs, easily parallel code can be realized. The circuits and the simulation results of their design with OrCAD are shown. The CLC and ADC on current mirrors open new prospects for realization of linear and matrix IP and NN with MIMO-operands.

The algorithms to tracking of movement dynamics of various biological objects now it is actually ... more The algorithms to tracking of movement dynamics of various biological objects now it is actually to studying. Features and characteristic of objects, conditions of their visualization strongly influence the choice of optimal methods and algorithms for a specific task it they tracking. Therefore, to automate the processes of adaptation of recognition-tracking algorithms, several Labview project trackers are considered in the article. Specificity of these objects, conditions of their visualization and model parameters strongly influence the choice of methods and algorithms, which are optimal for a specific task. Therefore, in this article, in order to automate the processes of adaptation algorithms of recognition - tracking, we suggest several frames pre-processing algorithms using NI Labview tools and Vision Assistant. Preprocessing included equalization of general background luminance of the image, elimination of high-frequency noise and different artifacts (highlighted areas, gaps,...

First, in the introduction, we will show the urgent need to create neuron-calculators (NCs) for t... more First, in the introduction, we will show the urgent need to create neuron-calculators (NCs) for the normalized equivalence of two matrix arrays for self-learning equivalently-convolutional neural networks (SLE_CNNs), video processors for parallel image processing with enhanced functionality. Consider promising areas of application of such single and multichannel neuron-calculators as high-precision, high-speed and high-performance accelerators for hardware systems and architectures for recognition, classification, image categorization, in particular for 2D-image space-invariant associative memory structures, SLE_CNNs based on the equivalence paradigm. Next, we will consider and analyze the theoretical foundations, the mathematical apparatus of matrix and continuous logic, and their basic operations, show their functional completeness, evaluate their advantages and prospects for application in the design of biologically inspired devices and systems for processing and analyzing signal...

Rolling backup rolls require high fracture toughness, particularly in the shaft portion, and high... more Rolling backup rolls require high fracture toughness, particularly in the shaft portion, and high-hardness in the sleeve portion. The rolls are classified into two types; one is an integrated type and the other is a shrink-fitted type consisting of a sleeve and a shaft. The shrink-fitted roll has several advantages, for example, suitable materials can be chosen and the shaft can be reused by replacing the damaged sleeve. However, during use if the residual permanent deflection occurs, the roll cannot be used anymore. In this paper, an elastic-contact finite element method FEM analysis is performed to explain the residual permanent deflection mechanism. It is found that the quasi-equilibrium stress zone with the residual displacement causes the permanent slippage in the axial direction. In a similar way, the interface creep in the circumferential direction can be also explained from the quasi-equilibrium stress zone with the residual displacement.

Advances in Computational Intelligence and Robotics, 2020

In this chapter, the authors consider the need and relevance of cryptographic transformation of i... more In this chapter, the authors consider the need and relevance of cryptographic transformation of images and video files that are transmitted from unmanned aircraft, airborne robots. The authors propose and consider new multifunctional matrix-algebraic models of cryptographic image transformations, the variety of matrix models, including block parametrical and matrix affine permutation ciphers. The authors show the advantages of the cryptographic models, such as adaptability to various formats, multi-functionality, ease of implementation on matrix parallel structures, interchangeability of iterative procedures and matrix exponentiation modulo, ease of selection, and control of cryptographic transformation parameters. The simulation results of the proposed algorithms and procedures for the direct and inverse transformation of images with the aim of masking them during transmission are demonstrated and discussed in this chapter. The authors evaluate the effectiveness and implementation ...

Information Technology and Computer Engineering, 2018

Анотація. Дослідження алгоритмів для відстеження динаміки руху різних біологічних об'єктів є акту... more Анотація. Дослідження алгоритмів для відстеження динаміки руху різних біологічних об'єктів є актуальним. На вибір оптимальних методів і алгоритмів відстеження об'єктів, для конкретного завдання, сильно впливають особливості і характеристики цих об'єктів і умови їх візуалізації. Тому, щоб автоматизувати процеси адаптації алгоритмів розпізнавання-відстеження, в статті розглядаються кілька варіантів алгоритмів для стеження за об'єктами реалізованими в проектах Labview. Специфіка цих об'єктів, умови їх візуалізації і параметри моделі сильно впливають на вибір методів і алгоритмів, які є оптимальними для конкретного завдання. Тому в цій статті для автоматизації процесів адаптаційних алгоритмів розпізнавання-відстеження запропоновано кілька алгоритмів попередньої обробки кадрів з використанням інструментів NI Labview і Vision Assistant. Попередня обробка включала вирівнювання загальної фонової яскравості зображення, усунення високочастотного шуму і різних артефактів (виділені області, проміжки, переломи) з вихідного зображення, контрастності, порогового значення, бінаризації і інших функціональних перетворень. Проекти дозволяють швидко змінювати шаблони для навчання і перепідготовки системи. Вони адаптуються до швидкості об'єктів і статистичних характеристик шуму в зображеннях. У статті обговорюються нові методи попередньої обробки зображень для алгоритмів, які відстежують динаміку руху різних біологічних об'єктів. Будуть представлені і проаналізовані експерименти, проведені для тестування трекерів на реальних відеофайлах. Ключові слова: класифікація, процес відстеження, безліч біологічних об'єктів, алгоритми попередньої обробки зображень, порівняння зображень. Аннотация. Исследование алгоритмов для отслеживания динамики движения различных биологических объектов является актуальным. На выбор оптимальных методов и алгоритмов отслеживания объектов, для конкретной задачи, сильно влияют особенности и характеристики этих объектов и условия их визуализации. Поэтому, чтобы автоматизировать процессы адаптации алгоритмов распознавания-отслеживания, в статье рассматриваются несколько вариантов алгоритмов для слежения за объектами, реализованными в проектах Labview. Специфика этих объектов, условия их визуализации и параметры модели сильно влияют на выбор методов и алгоритмов, которые являются оптимальными для конкретной задачи. Поэтому в этой статье для автоматизации процессов адаптационных алгоритмов распознавания-отслеживания предложено несколько алгоритмов предварительной обработки кадров с использованием инструментов NI Labview и Vision Assistant. Предварительная обработка включала выравнивание общей фоновой яркости изображения, устранение высокочастотного шума и различных артефактов (выделенные области, промежутки, переломы) из исходного изображения, контрастности, порогового значения, бинаризации и других функциональных преобразований. Проекты позволяют быстро менять шаблоны для обучения и переподготовки системы. Они адаптируются к скорости объектов и статистическим характеристикам шума в изображениях. В статье обсуждаются новые методы предварительной обработки изображений для алгоритмов, отслеживающих динамику движения различных биологических объектов. Будут представлены и проанализованы эксперименты, проведенные для тестирования трекеров на реальных видеофайлах. Ключевые слова: классификация, процесс отслеживания, множество биологических объектов, алгоритмы предварительной обработки изображений, сравнение изображений. Abstract. The algorithms to tracking of movement dynamics of various biological objects now it is actually to studying. Features and characteristic of objects, conditions of their visualization strongly influence the choice of optimal methods and algorithms for a specific task it they tracking. Therefore, to automate the processes of adaptation of recognition-tracking algorithms, several Labview project trackers are considered in the article. Specificity of these objects, conditions of their visualization and model parameters strongly influence the choice of methods and algorithms, which are optimal for a specific task. Therefore, in this article, in order to automate the processes of adaptation algorithms of recognition-tracking, we suggest several frames pre-processing algorithms using NI Labview tools and Vision Assistant. Preprocessing included equalization of general background luminance of the image, elimination of high-frequency noise and different artifacts (highlighted areas, gaps, fractures, etc.) from the original image, contrasting, thresholding, binarization and other functional transformations. Projects allow changing templates for training and retraining the system quickly. They adapt to the speed of objects and statistical characteristics of noise in images. New pre-processing methods image for algorithms tracking of movement dynamics of various biological objects will be discussed. The experiments carried out to test the trackers on real video files will be presented and analyzed.

Adaptive Computing: Mathematical and Physical Methods for Complex Environments, 1996

The theory and equivalental models of neural networks based on equivalence operation of continuou... more The theory and equivalental models of neural networks based on equivalence operation of continuous and multivalued neural logic are considered. Their connection with metric of metric-address spaces are shown. Normalized equivalences of vectors with multilevel components are determined. Equivalental models for simple network with weighted correlation coefficients, for network with adapted weighing and double weighing are suggested. It is shown, that the network model with double weighing being most generalized can also conduct the recalculation process of networks to two-step algorithms without calculation of connections matrix. Equivalent models require calculations based on vector-matrix procedures with equivalence operation and can be realized on vector-matrix calculations based on vector- matrix procedures with equivalence operation and can be realized on vector-matrix equivalentors with space and time integration. The apparatus implementations of models with productivity of 108 divided by 109 connections/sec and neuron number 256 and more are suggested.

International Conference on Correlation Optics, 1997

Mathematical fundamentals of neurobiologic, equivalence algebra and equivalence models of neural ... more Mathematical fundamentals of neurobiologic, equivalence algebra and equivalence models of neural networks are considered. Modified equivalence models of neural networks and associative memory for space-invariant 2D pattern recognition are proposed. They are based on the use of equivalence functions, including normalized ones, characterizing the similarity equivalence degree of two images, depending on their mutual space displacement. Relations between the equivalence functions and correlation functions are found out. Simulation results, demonstrating efficiency of the models on the example of 8.8 pixels patterns recognition with number of etalons, equaled to 4. Possible variants of the models implementations are considered. Neural networks architecture for invariant 2D pattern recognition consists of equivalentors, every of which replace two correlators.

International Conference on Holography and Correlation Optics, 1995

Principles of picture elements (PE) construction are proposed. These PE fulfill the operations of... more Principles of picture elements (PE) construction are proposed. These PE fulfill the operations of array continuous (neuron) logic (ACL or ANL) and are characterized by multifunctionality (up to universality), high performance (105 divided by 107 operations of ACL per sec.), and cascadability. Three schemes ACL PE are proposed: (1) on the basis of pulse-width conversion and universal PE of array two-level logic (ATL); (2) on the basis of comparators of images and operations of array hybrid logic (AHL); (3) on the basis of pulse-position conversion and operations of AHL in the time region. Proposed PE are generalizations of array discrete logic and will be able to become the macroelement basis for computers of future generations.

International Conference on Holography and Correlation Optics, 1995

The necessity of multichannel (1D or 2D) neural modules with optical inputs/outputs and completel... more The necessity of multichannel (1D or 2D) neural modules with optical inputs/outputs and completely parallel information processing, carrying out the operations over continuous variables, is shown. These neuromodules have to be multifunctional with programmable tuning. The realization of a definite function tuning is carried out by means of control signals. It is suitable to the functioning description of neural networks based on frequency-dynamic models to use the apparatus of continuous (neuron) logics. The operations of array neuron logic are discussed and it is shown that all these operations can be expressed by means of 2 basic operations: limited difference and addition. The most promising neuron models are the frequency-dynamic ones, because their functioning principles correspond to natural neuron ones. The circuit of optoelectronic cell, which fulfills the limited difference operation, storage, and threshold control operations is proposed. Experimental results for this cell are given. Circuit and experimental parameters for comparator of optical signals are also given. Creation opportunity and promising application of such circuits and lines on its base for optical neural nets are shown.

International Conference on Holography and Correlation Optics, 1995

Simulation results for information processing models and algorithms in optical neural networks (O... more Simulation results for information processing models and algorithms in optical neural networks (ONN) in the teaching and recognition modes are presented. They are based on equivalent operations of continuous logic and Boolean operations of coincidence, as well as vector-matrix procedures with normalization and threshold operation. It is proved that models based on these operations are united and common for various methods of coding.

Vìsnik Hmelʹnicʹkogo nacìonalʹnogo unìversitetu, Feb 23, 2022

Анотація-У статті розглядаються аспекти застосування матричних модифікацій криптосистеми RSA для ... more Анотація-У статті розглядаються аспекти застосування матричних модифікацій криптосистеми RSA для створення на основі матричних моделей та алгоритмів криптоперетворень зображень сліпих електронних цифрових підписів (СЕЦП). Перевагою запропонованих матричних моделей RSA є враховування специфіки зображень та простота адаптації до різних типів та форматів зображень. Наведені формули та алгоритмічні кроки процедур створення СЕЦП та проміжних кроків закриття, зашифрування та розшифрування зображень. Модельними експериментами у програмному середовищі Mathcad Professional, скрінами зі створених програмних модулів продемонстровані функціональні можливості та переваги процедур та алгоритмів створення покращених сліпих ЕЦП матричного типу на текстографічні документи (ТГД) конфіденційного характеру. Наведені результати моделювання процесів створення таких підписів для великоформатних документів у програмному середовищі Mathcad підтвердили адекватність запропонованих моделей перетворень та правильність функціонування та верифікації СЕЦП та дозволили визначити час та обмеження відповідних криптоперетворень. Ключові слова: криптографія, матричні моделі, сліпі електронні цифрові підписи, Mathcad Professional, моделювання, текстографічний документ, зашифрування-розшифрування зображень, криптографічні перетворення інтенсивності зображення, нелінійна обробка.

Herald of Khmelnytskyi National University

The perspective of neural networks equivalental models (EM) base on vector-matrix procedure with ... more The perspective of neural networks equivalental models (EM) base on vector-matrix procedure with basic operations of continuous and neuro-fuzzy logic (equivalence, absolute difference) are shown. Capacity on base EMs exceeded the amount of neurons in 4-10 times. This is larger than others neural networks paradigms. Amount neurons of this neural networks on base EMs may be 10 – 100 thousand. The base operations in EMs are normalized equivalence operations. The family of new operations “equivalence” and “non-equivalence” of neuro-fuzzy logic’s, which we have elaborated on the based of such generalized operations of fuzzy-logic’s as fuzzy negation, t-norm and s-norm are shown. Generalized rules of construction of new functions (operations) “equivalence” which uses operations of t-norm and s-norm to fuzzy negation are proposed. Despite the wide variety of types of operations on fuzzy sets and fuzzy relations and the related variety of new synthesized equivalence operations based on them...

Third International Conference on Applications of Optics and Photonics, 2017

Self-learning equivalent-convolutional neural structures (SLECNS) for auto-coding-decoding and im... more Self-learning equivalent-convolutional neural structures (SLECNS) for auto-coding-decoding and image clustering are discussed. The SLECNS architectures and their spatially invariant equivalent models (SI EMs) using the corresponding matrix-matrix procedures with basic operations of continuous logic and non-linear processing are proposed. These SI EMs have several advantages, such as the ability to recognize image fragments with better efficiency and strong cross correlation. The proposed clustering method of fragments with regard to their structural features is suitable not only for binary, but also color images and combines self-learning and the formation of weight clustered matrix-patterns. Its model is constructed and designed on the basis of recursively processing algorithms and to k-average method. The experimental results confirmed that larger images and 2D binary fragments with a large numbers of elements may be clustered. For the first time the possibility of generalization of these models for space invariant case is shown. The experiment for an image with dimension of 256x256 (a reference array) and fragments with dimensions of 7x7 and 21x21 for clustering is carried out. The experiments, using the software environment Mathcad, showed that the proposed method is universal, has a significant convergence, the small number of iterations is easily, displayed on the matrix structure, and confirmed its prospects. Thus, to understand the mechanisms of self-learning equivalence-convolutional clustering, accompanying her to the competitive processes in neurons, and the neural auto-encoding-decoding and recognition principles with the use of self-learning cluster patterns is very important which used the algorithm and the principles of non-linear processing of two-dimensional spatial functions of images comparison. These SIEMs can simply describe the signals processing during the all training and recognition stages and they are suitable for unipolar-coding multilevel signals. We show that the implementation of SLECNS based on known equivalentors or traditional correlators is possible if they are based on proposed equivalental two-dimensional functions of image similarity. The clustering efficiency in such models and their implementation depends on the discriminant properties of neural elements of hidden layers. Therefore, the main models and architecture parameters and characteristics depends on the applied types of non-linear processing and function used for image comparison or for adaptive-equivalental weighing of input patterns. Real model experiments in Mathcad are demonstrated, which confirm that non-linear processing on equivalent functions allows you to determine the neuron winners and adjust the weight matrix. Experimental results have shown that such models can be successfully used for auto- and hetero-associative recognition. They can also be used to explain some mechanisms known as "focus" and "competing gain-inhibition concept". The SLECNS architecture and hardware implementations of its basic nodes based on multi-channel convolvers and correlators with time integration are proposed. The parameters and performance of such architectures are estimated.

Machine Vision and Navigation, 2019

The urgent need to create video sensors and processors for parallel (simultaneous by pixel) image... more The urgent need to create video sensors and processors for parallel (simultaneous by pixel) image processing with advanced functionality and multichannel picture outputs is shown in the chapter. We consider perspective spheres and areas of application of such sensor processors, in particular, for hardware high-performance architectures of neural networks, convolutional neural structures, parallel matrix-matrix multipliers, and special processor systems. We show and analyze the theoretical foundations, the mathematical apparatus of the matrix and continuous logic, and their basic operations, show their functional completeness, and evaluate their advantages and prospects for application in the design of biologically inspired devices and systems for processing and analysis of array signals. We show that some functions of continuous logic, including operations of normalized equivalence of vector and matrix signals, the operation of a limited difference in continuous logic, are a powerfu...

Herald of Khmelnytskyi National University, 2021

The paper considers results of design, simulation of continuously logical pixel cells (CLPC) base... more 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...

Optical Sensors 2013, 2013

The paper considers results of design and modeling of continuously logical analog-to-digital conv... more The paper considers results of design and modeling of continuously logical analog-to-digital converters (ADC) based on current mirrors for image processor and multichannel optical sensor systems with parallel inputs-outputs. For such multichannel serial-parallel analog-to-digital converters (SP ADC) it is needed base photoelectron cells, which are considered in paper. Its have a number of advantages: high speed and reliability, simplicity, small power consumption, high integration level for linear and matrix structures. We show design of the continuously logical ADC of photocurrents and its base digit cells (ABC) and its simulations. We consider CL ADC for Gray and binary codes. Each channel of the structure consists of several base digit cells (ABC) on 20-30 CMOS FETs and one photodiode. The supply voltage of the ABC is 1-3.3V, the range of an input photocurrent is 0.1 – 10μA, the transformation time is 30ns at 5-8 bit binary or Gray codes, power consumption is about 1mW. One channel of ADC with iteration is based on one ABC-3(G) and SHD, and it has only 40 CMOS transistors. The general power consumption of the ADC, in this case, is only 50-100μW, if the maximum input current is 1μA. The CL ADC opens new prospects for realization of linear and matrix image processor and photo-electronic structures with picture operands, which are necessary for neural networks, digital optoelectronic processors, neural-fuzzy controllers, and so forth.

Neuro-inspired Photonic Computing, 2018

In the paper, we consider the urgent need to create highly efficient hardware accelerators for ma... more In the paper, we consider the urgent need to create highly efficient hardware accelerators for machine learning algorithms, including convolutional and deep neural networks (CNN and DNNS), for associative memory models, clustering, and pattern recognition. These algorithms usually include a large number of multiply-accumulate (and the like) operations. We show a brief overview of our related works the advantages of the equivalent models (EM) for describing and designing neural networks and recognizing bio-inspired systems. The capacity of NN on the basis of EM and of its modifications, including auto-and hetero-associative memories for 2D images, is in several times quantity of neurons. Such neuroparadigms are very perspective for processing, clustering, recognition, storing large size and strongly correlated and highly noised images. They are also very promising for solving the problem of creating machine uncontrolled learning. And since the basic operational functional nodes of EM are such vector-matrix or matrix-tensor procedures with continuous-logical operations as: normalized vector operations "equivalence", "nonequivalence", "autoequivalence", "auto-nonequivalence", we consider in this paper new conceptual approaches to the design of full-scale arrays of such neuron-equivalentors (NEs) with extended functionality, including different activation functions. Our approach is based on the use of analog and mixed (with special coding) methods for implementing the required operations, building NEs (with number of synapsis from 8 up to 128 and more) and their base cells, nodes based on photosensitive elements and CMOS current mirrors. We show the results of modeling the proposed new modularscalable implementations of NEs, we estimates and compare them. Simulation results show that processing time in such circuits does not exceed units of micro seconds, and for some variants 50-100 nanoseconds. Circuits are simple, have low supply voltage (1.5 – 3.3 V), low power consumption (milliwatts), low levels of input signals (microwatts), integrated construction, satisfy the problem of interconnections and cascading. Signals at the output of such neurons can be both digital and analog, or hybrid, and also with two complement outputs. They realize principle of dualism which gives a number of advantages of such complement dual NEs.

Emerging Imaging and Sensing Technologies for Security and Defence IV, 2019

Optics and Photonics for Information Processing XII, 2018

The paper considers results of design and simulation of continuously logical cells (CLC) based on... more The paper considers results of design and simulation of continuously logical cells (CLC) 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). For such IP and NN with vector or matrix parallel inputsoutputs, it is needed active basic photosensitive cells with an extended electronic circuit, which are considered in paper. 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 coding and their various possible implementations and simulations. The basic element of such cells is a scheme that implements the operation of a bounded difference of continuous logic. Using a set of such circuits implemented on traditional CMOS technology, we consider generalized decomposition and other methods for designing cells for nonlinear conversion of the photocurrent intensity, which makes it easy to realize the required nonlinear conversion function. Selection of the appropriate parameters, which can be specified as constructive constants or as parameters for external control, allows changing type of synthesized functions. We also consider the applications of such parallel matrix arrays for the creation of advanced IP and NN. We show the need for various types of converting and coding the photocurrents intensity in such parallel systems and sensory devices, especially for the implementation of various types of activation functions in the hardware implementations of neural networks. Such cells consist of several dozen CMOS transistors, have low power supply voltage (1.8 ÷ 3.3V), the range of an input photocurrent is 0.1÷24μA, the transformation time is less than 1 μs, low power consumption (microwatts). We also consider the cells for ADC after the intensity conversion. Each channel consists of several digital-analog cells (DC). The amount of DC is not exclusive to the number of digits of the formed code, and for an iteration type, only one cell of DC, complemented by the device of selection and holding (SHD), is required. One channel of ADC with iteration is based on one DC and SHD, and it has only 35 CMOS transistors. The general power consumption of the ADC with iteration is only 50÷100μW, if the maximum input current is 4μA. The ADCs with 6-8 bit binary or Gray codes have good dynamic characteristics (frequency of digitization even for 1.5μm CMOS-technologies is 10MHz and more). In such ADCs, easily parallel code can be realized. The circuits and the simulation results of their design with OrCAD are shown. The CLC and ADC on current mirrors open new prospects for realization of linear and matrix IP and NN with MIMO-operands.

The algorithms to tracking of movement dynamics of various biological objects now it is actually ... more The algorithms to tracking of movement dynamics of various biological objects now it is actually to studying. Features and characteristic of objects, conditions of their visualization strongly influence the choice of optimal methods and algorithms for a specific task it they tracking. Therefore, to automate the processes of adaptation of recognition-tracking algorithms, several Labview project trackers are considered in the article. Specificity of these objects, conditions of their visualization and model parameters strongly influence the choice of methods and algorithms, which are optimal for a specific task. Therefore, in this article, in order to automate the processes of adaptation algorithms of recognition - tracking, we suggest several frames pre-processing algorithms using NI Labview tools and Vision Assistant. Preprocessing included equalization of general background luminance of the image, elimination of high-frequency noise and different artifacts (highlighted areas, gaps,...

First, in the introduction, we will show the urgent need to create neuron-calculators (NCs) for t... more First, in the introduction, we will show the urgent need to create neuron-calculators (NCs) for the normalized equivalence of two matrix arrays for self-learning equivalently-convolutional neural networks (SLE_CNNs), video processors for parallel image processing with enhanced functionality. Consider promising areas of application of such single and multichannel neuron-calculators as high-precision, high-speed and high-performance accelerators for hardware systems and architectures for recognition, classification, image categorization, in particular for 2D-image space-invariant associative memory structures, SLE_CNNs based on the equivalence paradigm. Next, we will consider and analyze the theoretical foundations, the mathematical apparatus of matrix and continuous logic, and their basic operations, show their functional completeness, evaluate their advantages and prospects for application in the design of biologically inspired devices and systems for processing and analyzing signal...

Rolling backup rolls require high fracture toughness, particularly in the shaft portion, and high... more Rolling backup rolls require high fracture toughness, particularly in the shaft portion, and high-hardness in the sleeve portion. The rolls are classified into two types; one is an integrated type and the other is a shrink-fitted type consisting of a sleeve and a shaft. The shrink-fitted roll has several advantages, for example, suitable materials can be chosen and the shaft can be reused by replacing the damaged sleeve. However, during use if the residual permanent deflection occurs, the roll cannot be used anymore. In this paper, an elastic-contact finite element method FEM analysis is performed to explain the residual permanent deflection mechanism. It is found that the quasi-equilibrium stress zone with the residual displacement causes the permanent slippage in the axial direction. In a similar way, the interface creep in the circumferential direction can be also explained from the quasi-equilibrium stress zone with the residual displacement.

Advances in Computational Intelligence and Robotics, 2020

In this chapter, the authors consider the need and relevance of cryptographic transformation of i... more In this chapter, the authors consider the need and relevance of cryptographic transformation of images and video files that are transmitted from unmanned aircraft, airborne robots. The authors propose and consider new multifunctional matrix-algebraic models of cryptographic image transformations, the variety of matrix models, including block parametrical and matrix affine permutation ciphers. The authors show the advantages of the cryptographic models, such as adaptability to various formats, multi-functionality, ease of implementation on matrix parallel structures, interchangeability of iterative procedures and matrix exponentiation modulo, ease of selection, and control of cryptographic transformation parameters. The simulation results of the proposed algorithms and procedures for the direct and inverse transformation of images with the aim of masking them during transmission are demonstrated and discussed in this chapter. The authors evaluate the effectiveness and implementation ...

Information Technology and Computer Engineering, 2018

Анотація. Дослідження алгоритмів для відстеження динаміки руху різних біологічних об'єктів є акту... more Анотація. Дослідження алгоритмів для відстеження динаміки руху різних біологічних об'єктів є актуальним. На вибір оптимальних методів і алгоритмів відстеження об'єктів, для конкретного завдання, сильно впливають особливості і характеристики цих об'єктів і умови їх візуалізації. Тому, щоб автоматизувати процеси адаптації алгоритмів розпізнавання-відстеження, в статті розглядаються кілька варіантів алгоритмів для стеження за об'єктами реалізованими в проектах Labview. Специфіка цих об'єктів, умови їх візуалізації і параметри моделі сильно впливають на вибір методів і алгоритмів, які є оптимальними для конкретного завдання. Тому в цій статті для автоматизації процесів адаптаційних алгоритмів розпізнавання-відстеження запропоновано кілька алгоритмів попередньої обробки кадрів з використанням інструментів NI Labview і Vision Assistant. Попередня обробка включала вирівнювання загальної фонової яскравості зображення, усунення високочастотного шуму і різних артефактів (виділені області, проміжки, переломи) з вихідного зображення, контрастності, порогового значення, бінаризації і інших функціональних перетворень. Проекти дозволяють швидко змінювати шаблони для навчання і перепідготовки системи. Вони адаптуються до швидкості об'єктів і статистичних характеристик шуму в зображеннях. У статті обговорюються нові методи попередньої обробки зображень для алгоритмів, які відстежують динаміку руху різних біологічних об'єктів. Будуть представлені і проаналізовані експерименти, проведені для тестування трекерів на реальних відеофайлах. Ключові слова: класифікація, процес відстеження, безліч біологічних об'єктів, алгоритми попередньої обробки зображень, порівняння зображень. Аннотация. Исследование алгоритмов для отслеживания динамики движения различных биологических объектов является актуальным. На выбор оптимальных методов и алгоритмов отслеживания объектов, для конкретной задачи, сильно влияют особенности и характеристики этих объектов и условия их визуализации. Поэтому, чтобы автоматизировать процессы адаптации алгоритмов распознавания-отслеживания, в статье рассматриваются несколько вариантов алгоритмов для слежения за объектами, реализованными в проектах Labview. Специфика этих объектов, условия их визуализации и параметры модели сильно влияют на выбор методов и алгоритмов, которые являются оптимальными для конкретной задачи. Поэтому в этой статье для автоматизации процессов адаптационных алгоритмов распознавания-отслеживания предложено несколько алгоритмов предварительной обработки кадров с использованием инструментов NI Labview и Vision Assistant. Предварительная обработка включала выравнивание общей фоновой яркости изображения, устранение высокочастотного шума и различных артефактов (выделенные области, промежутки, переломы) из исходного изображения, контрастности, порогового значения, бинаризации и других функциональных преобразований. Проекты позволяют быстро менять шаблоны для обучения и переподготовки системы. Они адаптируются к скорости объектов и статистическим характеристикам шума в изображениях. В статье обсуждаются новые методы предварительной обработки изображений для алгоритмов, отслеживающих динамику движения различных биологических объектов. Будут представлены и проанализованы эксперименты, проведенные для тестирования трекеров на реальных видеофайлах. Ключевые слова: классификация, процесс отслеживания, множество биологических объектов, алгоритмы предварительной обработки изображений, сравнение изображений. Abstract. The algorithms to tracking of movement dynamics of various biological objects now it is actually to studying. Features and characteristic of objects, conditions of their visualization strongly influence the choice of optimal methods and algorithms for a specific task it they tracking. Therefore, to automate the processes of adaptation of recognition-tracking algorithms, several Labview project trackers are considered in the article. Specificity of these objects, conditions of their visualization and model parameters strongly influence the choice of methods and algorithms, which are optimal for a specific task. Therefore, in this article, in order to automate the processes of adaptation algorithms of recognition-tracking, we suggest several frames pre-processing algorithms using NI Labview tools and Vision Assistant. Preprocessing included equalization of general background luminance of the image, elimination of high-frequency noise and different artifacts (highlighted areas, gaps, fractures, etc.) from the original image, contrasting, thresholding, binarization and other functional transformations. Projects allow changing templates for training and retraining the system quickly. They adapt to the speed of objects and statistical characteristics of noise in images. New pre-processing methods image for algorithms tracking of movement dynamics of various biological objects will be discussed. The experiments carried out to test the trackers on real video files will be presented and analyzed.

Adaptive Computing: Mathematical and Physical Methods for Complex Environments, 1996

The theory and equivalental models of neural networks based on equivalence operation of continuou... more The theory and equivalental models of neural networks based on equivalence operation of continuous and multivalued neural logic are considered. Their connection with metric of metric-address spaces are shown. Normalized equivalences of vectors with multilevel components are determined. Equivalental models for simple network with weighted correlation coefficients, for network with adapted weighing and double weighing are suggested. It is shown, that the network model with double weighing being most generalized can also conduct the recalculation process of networks to two-step algorithms without calculation of connections matrix. Equivalent models require calculations based on vector-matrix procedures with equivalence operation and can be realized on vector-matrix calculations based on vector- matrix procedures with equivalence operation and can be realized on vector-matrix equivalentors with space and time integration. The apparatus implementations of models with productivity of 108 divided by 109 connections/sec and neuron number 256 and more are suggested.

International Conference on Correlation Optics, 1997

Mathematical fundamentals of neurobiologic, equivalence algebra and equivalence models of neural ... more Mathematical fundamentals of neurobiologic, equivalence algebra and equivalence models of neural networks are considered. Modified equivalence models of neural networks and associative memory for space-invariant 2D pattern recognition are proposed. They are based on the use of equivalence functions, including normalized ones, characterizing the similarity equivalence degree of two images, depending on their mutual space displacement. Relations between the equivalence functions and correlation functions are found out. Simulation results, demonstrating efficiency of the models on the example of 8.8 pixels patterns recognition with number of etalons, equaled to 4. Possible variants of the models implementations are considered. Neural networks architecture for invariant 2D pattern recognition consists of equivalentors, every of which replace two correlators.

International Conference on Holography and Correlation Optics, 1995

Principles of picture elements (PE) construction are proposed. These PE fulfill the operations of... more Principles of picture elements (PE) construction are proposed. These PE fulfill the operations of array continuous (neuron) logic (ACL or ANL) and are characterized by multifunctionality (up to universality), high performance (105 divided by 107 operations of ACL per sec.), and cascadability. Three schemes ACL PE are proposed: (1) on the basis of pulse-width conversion and universal PE of array two-level logic (ATL); (2) on the basis of comparators of images and operations of array hybrid logic (AHL); (3) on the basis of pulse-position conversion and operations of AHL in the time region. Proposed PE are generalizations of array discrete logic and will be able to become the macroelement basis for computers of future generations.

International Conference on Holography and Correlation Optics, 1995

The necessity of multichannel (1D or 2D) neural modules with optical inputs/outputs and completel... more The necessity of multichannel (1D or 2D) neural modules with optical inputs/outputs and completely parallel information processing, carrying out the operations over continuous variables, is shown. These neuromodules have to be multifunctional with programmable tuning. The realization of a definite function tuning is carried out by means of control signals. It is suitable to the functioning description of neural networks based on frequency-dynamic models to use the apparatus of continuous (neuron) logics. The operations of array neuron logic are discussed and it is shown that all these operations can be expressed by means of 2 basic operations: limited difference and addition. The most promising neuron models are the frequency-dynamic ones, because their functioning principles correspond to natural neuron ones. The circuit of optoelectronic cell, which fulfills the limited difference operation, storage, and threshold control operations is proposed. Experimental results for this cell are given. Circuit and experimental parameters for comparator of optical signals are also given. Creation opportunity and promising application of such circuits and lines on its base for optical neural nets are shown.

International Conference on Holography and Correlation Optics, 1995

Simulation results for information processing models and algorithms in optical neural networks (O... more Simulation results for information processing models and algorithms in optical neural networks (ONN) in the teaching and recognition modes are presented. They are based on equivalent operations of continuous logic and Boolean operations of coincidence, as well as vector-matrix procedures with normalization and threshold operation. It is proved that models based on these operations are united and common for various methods of coding.