Radio Electronics, Computer Science, Control

RISK ASSESSMENT MODELING OF ERP-SYSTEMS

A. D. Kozhukhivskyi, O. A. Kozhukhivska — Tue, 13 Dec 2022 00:00:00 +0200

Context. Because assessing security risks is a complex and complete uncertainty process, and uncertainties are a major factor influencing valuation performance, it is advisable to use fuzzy methods and models that are adaptive to noncomputed data. The formation of vague assessments of risk factors is subjective, and risk assessment depends on the practical results obtained in the process of processing the risks of threats that have already arisen during the functioning of the organization and experience of security professionals. Therefore, it will be advisable to use models that can ade-quately assess fuzzy factors and have the ability to adjust their impact on risk assessment. The greatest performance indicators for solving such problems are neuro-fuzzy models that combine methods of fuzzy logic and artificial neural networks and systems, i.e. “human-like” style of considerations of fuzzy systems with training and simulation of mental phenomena of neural networks. To build a model for calculating the risk assessment of security, it is proposed to use a fuzzy product model. Fuzzy product models (Rule-Based Fuzzy Models/Systems) this is a common type of fuzzy models used to describe, analyze and simulate complex systems and processes that are poorly formalized.

Objective. Development of a fuzzy model of quality of security risk assessment and protection of ERP systems through the use of fuzzy neural models.

Method. To build a model for calculating the risk assessment of security, it is proposed to use a fuzzy product model. Fuzzy product models are a common kind of fuzzy models used to describe, analyze and model complex systems and processes that are poorly formalized.

Results. Identified factors influencing risk assessment suggest the use of linguistic variables to describe them and use fuzzy variables to assess their qualities, as well as a system of qualitative assessments. The choice of parameters was substantiated and a fuzzy product model of risk assessment and a database of rules of fuzzy logical conclusion using the MATLAB application package and the Fuzzy Logic Toolbox extension package was implemented, as well as improved by introducing the adaptability of the model to experimental data by introducing neuro-fuzzy components into the model. The use of fuzzy models to solve the problems of security risk assessment, as well as the concept and construction of ERP systems and the analyzed problems of their security and vulnerabilities are considered.

Conclusions. A fuzzy model has been developed risk assessment of the ERP system. Selected a list of factors affecting the risk of security. Methods of risk assessment of information resources and ERP-systems in general, assessment of financial losses from the implementation of threats, determination of the type of risk according to its assessment for the formation of recommendations on their processing in order to maintain the level of protection of the ERP-system are proposed. The list of linguistic variables of the model is defined. The structure of the database of fuzzy product rules – MISO-structure is chosen. The structure of the fuzzy model was built. Fuzzy variable models have been identified.

METHODS FOR TABULAR IMPLEMENTATION OF ARITHMETIC OPERATIONS OF THE RESIDUES OF TWO NUMBERS REPRESENTED IN THE SYSTEM OF RESIDUAL CLASSES

V. A. Krasnobayev, A. S. Yanko, D. M. Kovalchuk — Sat, 03 Dec 2022 00:00:00 +0200

Context. Implementation of modular arithmetic operations of addition, subtraction and multiplication by a tabular method based on the use of the tabular multiplication code. The object of the study is the process of tabular implementation of basic arithmetic operations on the residues of numbers represented in the system of residual classes.

Objective. The goal of the work is to develop methods for the tabular implementation of the arithmetic operations of multiplication, addition and subtraction of the residues of two numbers based on the use of the tabular multiplication code.

Method. Tabular methods for implementing integer arithmetic modular operations of addition, subtraction and multiplication are proposed for consideration. In order to reduce the amount of equipment for a tabular operating unit of computer systems that implements modular operations of addition, subtraction and multiplication by reducing the coincidence circuits AND in the nodes of the tables for implementing arithmetic operations based on the code of table multiplication, two methods for performing arithmetic modular operations of addition and subtraction have been developed. These methods are based on the code of tabular multiplication, the use of which will reduce the amount of equipment of the tabular operating unit. Thus, despite the difference in the digital structure of the tables of modular operations of addition, subtraction and multiplication based on the use of the tabular multiplication code, two new tabular methods for implementing arithmetic modular operations of addition and subtraction have been created. Based on them, algorithms for tabular execution of modular arithmetic operations of addition and subtraction have been developed. Using these algorithms, it is possible to synthesize a structurally simple, highly reliable and fast table operating unit that operates in a system of residual classes, which is based on three separate permanent storage devices (read-only memory), each of which implements only one fourth of the corresponding complete table of values of the modular operation, what is earlier in the theory tabular arithmetic was supposed to be impossible.

Results. The developed methods are justified theoretically and studied when performing arithmetic modular operations of addition, subtraction and multiplication using tabular procedures.

Conclusions. The conducted examples of the implementation of integer arithmetic modular operations of addition and subtraction can be considered as presented experiments. The results obtained make it possible to recommend them for use in practice in the design of computer systems operating in a non-positional number system in residual classes. Prospects for further research may be to create a tabular method for implementing integer arithmetic modular division operations based on the use of the tabular multiplication code.

RESTORATION OF DISCONTINUOUS FUNCTIONS BY DISCONTINUOUS INTERLINATION SPLINES

I. I. Pershyna — Sun, 04 Dec 2022 00:00:00 +0200

Context. The problem of development and research of methods for approximation of discontinuous functions by discontinuous interlination splines and its further application to problems of computed tomography. The object of the study was the modeling of objects with a discontinuous internal structure.

Objective. The aim of this study is to develop a general method for constructing discontinuous interlining polynomial splines, which, as a special case, include discontinuous and continuously differentiated splines.

Method. Modern methods of restoring functions are characterized by new approaches to obtaining, processing and analyzing information. There is a need to build mathematical models in which information can be represented not only by function values at points, but also in the form of a set of function traces on planes or straight lines.

At the same time, practice shows that among the multidimensional objects that need to be investigated, more problems are described by a discontinuous functions.

The paper develops a general method for constructing discontinuous interlining polynomial splines, which, as a special case, include discontinuous and continuously differentiable splines. It is considered that the domain of the definition of the required twodimensional function is divided into rectangular elements. Theorems on interlination and approximation properties of such discontinuous constructions are formulated and proved. The method is developed for approximating discontinuous functions of two variables based on the constructed discontinuous splines. The input data are the traces of an unknown function along a given system of mutually perpendicular straight lines. The proposed method has not only theoretical significance but also practical application in the IT domain, especially in computing tomography, allowing more accurately restore the internal structure of the body.

Results. The discontinuous interlination operator from known traces of the function of two variables on a system of mutually perpendicular straight lines is researched.

Conclusions. The functions of two variables that are discontinuous at some points or on some lines are better approximated by discontinuous spline interlinants. At the same time, equally high approximation estimates can be obtained. The results obtained have significant advantages over existing methods of interpolation and approximation of discontinuous functions. In further research, the authors plan to develop a theory of discontinuous splines on areas of complex shape bounded by arcs of known curves.

QUANTUM DIGITAL-ANALOGUE COMPUTING

A. Khakhanova, S. Chumachenko, D. Rakhlis, І. Hahanov, V. Hahanov — Sun, 04 Dec 2022 00:00:00 +0200

Context. Nature is the relation among processes and phenomena. Nothing exists in the universe without relations. Computer is transactions of relations between data with the help of control and execution mechanisms. Quantum relations are a superposition of particles and their states. Superposition and entanglement are equivalent concepts. Entanglement is a non-local superposition of deterministic states. A quantum computer is unconditional transactions of relations between qubit data. Quantum computer is an analog device for parallel solution of combinatorial problems. Practically oriented definitions of the quantum computer concepts are the path to development of scalable quantum parallel algorithms for combinatorial problems solving. Any algorithm can be reduced to a sequence of operations without conditions, because any truth table is a collection of a complete system of conditions-states. Any sequence of actions can always be reduced to one parallel operation. Conditions and sequences arise only when the developer wants to use previously created primitive constructs to build an always non-optimal computing unit. The paradigm of quantum computer creation is determined through the use of photonic transactions on the electrons of an atom may exclude the use of quantum logic. The evolutionary path of a quantum computer from the classical one: “memory-address-transaction” (MAT) → “electron-addresstransaction” → “electron-address-quantaction” (EAQ) → state-superposition-logic. The meeting point of classical and quantum computers is photon transactions on the structure of electrons. Everything that is calculated on a quantum computer can be calculated in parallel on a classical one on account of memory redundancy. The given example is a memory-driven algorithm for modeling digital products based on qubit-vector forms of functionality description for significant performance boost of computing processes by parallel execution of logical operations.

Objective. Simulation of the correct SoC-component behavior based on vector representation of the logic. Formation of the triggering development of a computing based on the superposition of the classical, quantum and analog computing process, which in its development should be based on technological qubit, tabular and vector data structures for the parallel solution of combinatorial problems.

Method. MAT-computing implements any algorithms on account of transactions (read-write) in memory. Qubit-vector models for describing functionalities, which differ from known truth tables in compactness of description and manufacturability for the implementation of parallel algorithms of the synthesis and analysis of digital devices and SoC-components.

Results. 1) The metric of the technological data structures, focused on parallel troubleshooting in digital systems based on the usage of two logical vector operations, was proposed for the first time. 2) The metric of relations between the individual components of QC, allowing organizing a quantum deterministic computer, has been further developed. 3) Quantum architectural solutions, that allow solving coverage problems in a quasi-parallel mode, were proposed for the first time. 4) Architectural solutions based on an analog-to-digital computing, which can be used to solve the problems of the digital systems parallel analysis, have been further developed. 5) Vector-qubit structures of the logic data, that allow a quasi-parallel simulation of digital circuits, were proposed.

Conclusions. Qubit models, quantum methods and combinatorial algorithms for technical diagnostics of digital devices have been implemented, which can significantly (up to 25%) reduce the time of test synthesis, deductive modeling of faulty and correct behavior, search for defective states by introducing an innovative idea of using qubit-vector data structures for describing logical components. Comparative assessments of qubit models and methods usage show an increase in the efficiency of algorithms for modeling digital devices compared to tabular ones. The superposition of a classical, quantum and analog computer is integrally represented, which allows to find the best solutions for recognition and decision making.

INFORMATION TECHNOLOGY OF TRANSPORT INFRASTRUCTURE MONITORING BASED ON REMOTE SENSING DATA

S. Yu. Danshyna, A. S. Nechausov, S. M. Andrieiev — Thu, 15 Dec 2022 00:00:00 +0200

Context. In the light of current road network monitoring practices, this study aims to explore the capability of remote sensing technologies to solve the problems of increasing the objectivity of preliminary evaluations of the condition of the infrastructure as a whole. The object of the study was to process the monitoring of transport infrastructure (TI) to find ways to improve it in the implementation of development projects.

Objective. The goal of the work is to increase objectivity of decision-making on the evaluation, reconstruction, development of the transport network structure due to the visual presentation and disclosure of open data for monitoring the transport value.

Method. Existing approaches to TI monitoring and evaluating its condition are analyzed. The identified shortcomings, as well as the development of remote sensing technologies, open up prospects for the use of remote sensing data in the TI monitoring process. A set-theoretic model of the monitoring process information flows is proposed, the consistent refinement of the elements of which made it possible to develop information technology (IT). Formation of a set of input and output parameters of IT, the set of its operations, their representation with IDEFX-models set explains how a set of heterogeneous (graphic, text, digital, cartographic, etc.) data about TI elements coming from different sources are processed and presented to support decision-making on the survey of existing infrastructure and its improvement. The developed IT makes it possible to obtain complex indicators for analyzing the TI of a particular area, to solve the problems of inventorying objects, TI and its elements modeling, taking into account the physical and geographical location, which makes it possible to consider it as an auxiliary tool that complements existing methods of TI monitoring.

Results. The developed IT was studied in solving the problem of monitoring the TI section of the Kharkiv region using satellite imagery of medium (Sentinel–2) and high (SuperView-1) resolution and the results of laser survey of the road bridge across the river Mzha (as an element of infrastructure).

Conclusions. The conducted experiments confirmed the operability of the proposed information technology and showed expediency of its practical use in solving the problems of obtaining generalizing characteristics of the infrastructure, inventory of TI objects and their modeling. This opens up opportunities for substantiating project decisions for the reconstruction of the transport network and planning procedures for examining its condition. Prospects for further research may include: creating reference models of TI objects, expanding the table of decryption signs of road transport infrastructure objects, integrating remote data, survey results of TI sections and engineering surveys of objects to obtain evaluations of the condition of TI in general.

DETERMINATION OF INHERITANCE RELATIONS AND RESTRUCTURING OF SOFTWARE CLASS MODELS IN THE PROCESS OF DEVELOPING INFORMATION SYSTEMS

O. B. Kungurtsev, A. I. Vytnova — Sat, 10 Dec 2022 00:00:00 +0200

Context. The implementation of different use-cases may be performed by different development teams at different times. This results in a poorly structured code. The problem is exacerbated when developing medium and large projects in a short time.

Objective. Since inheritance is one of the effective ways to structure and improve the quality of code, the aim of the study is to determine possible inheritance relationships for a variety of class models.

Method. It is proposed to select from the entire set of classes representing the class model at a certain design stage, subsets for which a common parent class (in a particular case, an abstract class) is possible. To solve the problem, signs of the generality of classes have been formulated. The mathematical model of the conceptual class has been improved by including information about the responsibilities of the class, its methods and attributes. The connection of each class with the script items for which it is used has been established. A system of data types for class model elements is proposed. Description of class method signatures has been extended. A method for restructuring the class model, which involves 3 stages, has been developed. At the first stage, the proximity coefficients of classes are determined. At the second, subsets of possible child classes are created. At the third stage, an automated transformation of the class structure is performed, considering the identified inheritance relationships.

Results. A software product for conducting experiments to identify possible inheritance relationships depending on the number of classes and the degree of their similarity has been developed. The results of the conducted tests showed the effectiveness of the decisions made.

Conclusions. The method uses an algorithm for forming subsets of classes that can have one parent and an algorithm for automatically creating and converting classes to build a two-level class hierarchy. An experiment showed a threefold reduction in errors in detecting inheritance and a multiple reduction in time in comparison with the existing technology.

SYNTHESIS OF THE SYMBOLOGIES OF MULTICOLOR INTERFERENCE-RESISTANT BAR CODES ON THE BASE OF MULTI-VALUED BCH CODES

Ye. S. Sulema, L. V. Drozdenko, A. I. Dychka — Thu, 15 Dec 2022 00:00:00 +0200

Context. The problem of constructing a set of barcode patterns for multicolor barcodes that are resistant to distortions of one or two elements within each pattern is considered.

Objective. The goal of the work is ensuring the reliability of the reading of multi-color barcode images.

Method. A multicolor barcode pattern has the property of interference immunity if its digital equivalent (vector) is a codeword of a multi-valued (non-binary) correcting code capable to correct errors (distortions of the pattern elements). It is shown that the construction of barcode patterns should be performed on the basis of a multi-valued correcting BCH code capable to correct two errors. A method is proposed for constructing a set of interference-resistant barcode patterns of a given capacity, which ensure reliable reproduction of data when they are read from a carrier. A procedure for encoding data with a multi-valued BCH code based on the generator matrix of the code using operations by the modulo of a prime number has been developed. A new method of constructing the check matrix of the multivalued BCH code based on the vector representation of the elements of the finite field is proposed. A generalized algorithm for generating symbologies of a multi-color barcode with the possibility of correcting double errors in barcode patterns has been developed. The method also makes it possible to build symbology of a given capacity based on shortened BCH codes. A method of reducing the generator and check matrices of a multi-valued full BCH code to obtain a shortened code of a given length is proposed. It is shown that, in addition to correction double errors, multi-valued BCH codes also make it possible to detect errors of higher multiplicity – this property is enhanced when using shortened BCH codes. The method provides for the construction of a family of multicolor noise-immune barcodes.

Results. On the basis of the developed software tools, statistical data were obtained that characterize the ability of multi-valued BCH codes to detect and correct errors, and on their basis to design multi-color interference-resistant bar codes.

Conclusions. The conducted experiments have confirmed the operability of the proposed algorithmic tools and allow to recommend it for use in practice for developing interference-resistant multi-color barcodes in automatic identification systems.

PERMANENT DECOMPOSITION ALGORITHM FOR THE COMBINATORIAL OBJECTS GENERATION

Y. V. Turbal, S. V. Babych, N. E. Kunanets — Sun, 11 Dec 2022 00:00:00 +0200

Context. The problem of generating vectors consisting of different representatives of a given set of sets is considered. Such problems arise, in particular, in scheduling theory, when scheduling appointments. A special case of this problem is the problem of generating permutations.

Objective. Problem is considered from the point of view of a permanent approach and a well-known one, based on the concept of lexicographic order.

Method. In many tasks, it becomes necessary to generate various combinatorial objects: permutations, combinations with and without repetitions, various subsets. In this paper we consider a new approach to the combinatorial objects generation, which is based on the procedure of the permanent decomposition. Permanent is built for the special matrix of incidence. The main idea of this approach is including to the process of the algebraic permanent decomposition by row additional function for the column identifiers writing into corresponding data structures. In this case, the algebraic permanent in not calculated, but we get a specific recursive algorithm for generating a combinatorial object. The computational complexity of this algorithm is analyzed.

Results. It is investigated a new approach to the generation of complex combinatorial objects, based on the procedure of decomposition of the modified permanent of the incidence matrix by line with memorization of index elements.

Conclusions. The permanent algorithms of the combinatorial objects generation is investigated. The complexity of our approach in the case of permutation is compared with the lexicographic algorithm and the Johnson-Trotter algorithm.

The obtained results showed that our algorithm belongs to the same complexity class as the lexicographic algorithm and the Johnson-Trotter method. Numerical results confirmed the effectiveness of our approach.

REWRITING IDENTIFICATION TECHNOLOGY FOR TEXT CONTENT BASED ON MACHINE LEARNING METHODS

N. Kholodna, V. Vysotska — Tue, 13 Dec 2022 00:00:00 +0200

Context. Paraphrased textual content or rewriting is one of the difficult problems of detecting academic plagiarism. Most plagiarism detection systems are designed to detect common words, sequences of linguistic units, and minor changes, but are unable to detect significant semantic and structural changes. Therefore, most cases of plagiarism using paraphrasing remain unnoticed.

Objective of the study is to develop a technology for detecting paraphrasing in text based on a classification model and machine learning methods through the use of Siamese neural network based on recurrent and Transformer type – RoBERTa to analyze the level of similarity of sentences of text content.

Method. For this study, the following semantic similarity metrics or indicators were chosen as features: Jacquard coefficient for shared N-grams, cosine distance between vector representations of sentences, Word Mover’s Distance, distances according to WordNet dictionaries, prediction of two ML models: Siamese neural network based on recurrent and Transformer type - RoBERTa.

Results. An intelligent system for detecting paraphrasing in text based on a classification model and machine learning methods has been developed. The developed system uses the principle of model stacking and feature engineering. Additional features indicate the semantic affiliation of the sentences or the normalized number of common N-grams. An additional fine-tuned RoBERTa neural network (with additional fully connected layers) is less sensitive to pairs of sentences that are not paraphrases of each other. This specificity of the model may contribute to incorrect accusations of plagiarism or incorrect association of user-generated content. Additional features increase both the overall classification accuracy and the model’s sensitivity to pairs of sentences that are not paraphrases of each other.

Conclusions. The created model shows excellent classification results on PAWS test data: precision – 93%, recall – 92%, F1score – 92%, accuracy – 92%. The results of the study showed that Transformer-type NNs can be successfully applied to detect paraphrasing in a pair of texts with fairly high accuracy without the need for additional feature generation.

CLUSTERIZATION OF DATA ARRAYS BASED ON COMBINED OPTIMIZATION OF DISTRIBUTION DENSITY FUNCTIONS AND THE EVOLUTIONARY METHOD OF CAT SWARM

Ye. V. Bodyanskiy, I. P. Pliss, A. Yu. Shafronenko — Mon, 05 Dec 2022 00:00:00 +0200

Context. The task of clustering arrays of observations of an arbitrary nature is an integral part of Data Mining, and in the more general case of Data Science, a huge number of approaches have been proposed for its solution, which differ from each other both in a priori assumptions regarding the physical nature of the data and the problem, and in the mathematical apparatus. From a computational point of view, the clustering problem turns into a problem of finding local extrema of a multiextremal function of the vector density argument using gradient procedures that are repeatedly launched from different points of the initial data array. It is possible to speed up the process of searching for these extremes by using the ideas of evolutionary optimization, which includes algorithms inspired by nature, swarm algorithms, population algorithms, etc.

Objective. The purpose of the work is to introduce a data clustering procedure based on the peaks of the data distribution density and the evolutionary method of cat swarms, that combines the main advantages of methods for working with data in conditions of overlapping classes, is characterized by high-quality clustering, high speed and accuracy of the obtained results.

Method. The method for clustering data arrays based on the combined optimization of distribution density functions and the evolutionary method of cat swarms was proposed. The advantage of the proposed approach is to reduce the time for solving optimization problems in conditions where clusters are overlap.

Results. The results of the experiments confirm the effectiveness of the proposed approach in clustering problems under the condition of classes that overlap and allow us to recommend the proposed method for use in practice to solve problems of automatic clustering big data.

Conclusions. The method for clustering data arrays based on the combined optimization of distribution density functions and the evolutionary method of cat swarm was proposed. The advantage of the proposed approach is to reduce the time for solving optimization problems in conditions where clusters are overlap. The method is quite simple from the numerical implementation and is not critical for choosing an optimization procedure. The experimental results confirm the effectiveness of the proposed approach in clustering problems under conditions of overlapping clusters.

DATA CLUSTERING BASED ON INDUCTIVE LEARNING OF NEURO-FUZZY NETWORK WITH DISTANCE HASHING

S. A. Subbotin — Fri, 09 Dec 2022 00:00:00 +0200

Context. Cluster analysis is widely used to analyze data of various nature and dimensions. However, the known methods of cluster analysis are characterized by low speed and are demanding on computer memory resources due to the need to calculate pairwise distances between instances in a multidimensional feature space. In addition, the results of known methods of cluster analysis are difficult for human perception and analysis with a large number of features.

Objective. The purpose of the work is to increase the speed of cluster analysis, the interpretability of the resulting partition into clusters, as well as to reduce the requirements of cluster analysis to computer memory.

Method. A method for cluster analysis of multidimensional data is proposed, which for each instance calculates its hash based on the distance to the conditional center of coordinates, uses a one-dimensional coordinate along the hash axis to determine the distances between instances, considers the resulting hash as a pseudo-output feature, breaking it into intervals, which matches the labels pseudo-classes – clusters, having received a rough crisp partition of the feature space and sample instances, automatically generates a partition of input features into fuzzy terms, determines the rules for referring instances to clusters and, as a result, forms a fuzzy inference system of the Mamdani-Zadeh classifier type, which is further trained in the form of a neuro-fuzzy network to ensure acceptable values of the clustering quality functional. This makes it possible to reduce the number of terms and features used, to evaluate their contribution to making decisions about assigning instances to clusters, to increase the speed of data cluster analysis, and to increase the interpretability of the resulting data splitting into clusters.

Results. The mathematical support for solving the problem of cluster data analysis in conditions of large data dimensions has been developed. The experiments confirmed the operability of the developed mathematical support have been carried out.

Conclusions. . The developed method and its software implementation can be recommended for use in practice in the problems of analyzing data of various nature and dimensions.

IMPROVING THE EDDY CURRENT IDENTIFIER OF METALS BASED ON THE CORRELATION APPROACH

А. О. Abramovych — Sat, 03 Dec 2022 00:00:00 +0200

Context. The article considers the problem of improving the eddy current device for metal identification on the basis of the correlation approach. This approach is convenient for the accumulation of a database of known leading materials, as well as for the comparison of the signal from an unknown metal object with the available standards in the database. The proposed approach allows to determine the type of metal from which the sample is made, without damaging its surface.

Objective. The aim of the work is the identification of metals by type on the feedback signals from the eddy current converter, due to the identification of new informative features and the creation of a database of images of metals.

Method. The paper presents the results of an experimental study of the proposed approach to increase the reliability of identification of metal objects that are detected using an eddy current device. These studies are conducted on the basis of a radio system that operates on the eddy current principle and has low-frequency magnetic loop antennas. The eddy current method allows the identification of metals by type remotely and without damaging their surface, in contrast to X-ray fluorescence, optical emission or chemical methods. A correlation approach for processing the response signal spectrum from a hidden metal object is proposed. The correlation approach allows to increase the reliability of the identification of metals by type when processing the signal in the spectral region based on the Fourier transform. Studies have been conducted on the example of metals that have similar spectral characteristics (silver, gold, lead). The updated approach allowed to increase the percentage difference between the information parameters of signals – responses from 1.87% to 5.02% for silver and gold, from 2.24% to 4.34% for silver and lead and from 0.36% to 0.7% for gold and lead.

Results. The developed radio system is a laboratory model, which consists of an analog part and a digital one. The analog part is an antenna unit, a signal amplifier and a bandpass filter, a digital part – a microcontroller with an ADC for digitizing and transmitting data to a laptop, which software implements the proposed approaches to signal processing. The paper experimentally confirmed the possibility of using a radio engineering system to solve the problem of metal identification within a subset of nonmagnetic and magnetic materials.

Conclusions. The development of modern eddy current devices is aimed at increasing the reliability of the identification the hidden metal objects, which is relevant in geophysical exploration, archeology, and law enforcement agencies in the search for hidden non-ferrous metals. The development and improvement of such systems includes both the development the hardware and the discovery new information parameters in the feedback signals from metals. One such direction may be the correlation approach to signal processing in the spectral region.