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• DEVELOPMENT AND RESEARCH OF THE METHOD OF VECTOR ANALYSIS OF EMG OF THE FOREARM FOR CONSTRUCTION OF HUMAN-MACHINE INTERFACES

  N. A. Budko, M. Y. Medvedev , A.Y. Budko

  2021-07-18

  Abstract ▼

  The paper deals with the problems of increasing the depth and increasing the long-term stability
  of communication channels in human-machine interfaces, built on the basis of data on the
  electrical activity of the forearm muscles. A possible solution is to use the method of analysis of
  electromyogram (EMG) signals, which combines vector and command control. In view of the possibility
  of random displacement of the position of the electrodes during operation, a mathematical
  model was built for vector analysis of EMG in spherical coordinates, which is invariant to the
  spatial arrangement of the electrodes on the forearm. Command control is based on gesture
  recognition by means of a pretrained artificial neural network (ANN). Vector control consists in
  solving the problem of calibrating the channels of EMG sensors according to the spatial arrangement
  of the electrodes and calculating the resulting vector of muscle forces used as an additional
  information channel to set the direction of movement of the operating point of the control object.
  The proposed method has been tested on actually recorded EMG signals. The influence of the
  duration of the processed signal fragments on the process of extracting information about the
  rotational movement of the hand was investigated. Since the change in the position of the electrodes
  between operating sessions is different, an algorithm for reassigning and calibrating the
  amplification of the EMG channels is presented, which makes it possible to use a once trained
  ANN for recognition and classification of gestures in the future. Practical application of the results
  of the work is possible in the development of algorithms for calibration, gesture recognition
  and control of technical objects based on electromyographic human-machine interfaces.

• SUBTRACTION OF BACKPROPAGATION INTERFERENCE BASED ON POLARIZATION IN UNDERWATER VISION SYSTEMS FOR OPERATION IN TURBID WATER

  N.А. Budko, А.Y. Budko, М.Y. Medvedev

  2022-08-09

  Abstract ▼

  The study of the sea depths in order to ensure safety, the effective use of underwater resources
  is an urgent task. The first part of the article briefly considers the physical phenomena and
  limitations that arise during the propagation of electromagnetic waves in the visible range in the
  underwater environment. It is shown that underwater vision systems (as a class of specialized
  technical vision systems - TVS) based on conventional CCD matrices face a number of fundamental
  limitations in terms of improving the efficiency of functioning in natural water of low transparency.
  In particular, the use of artificial light sources as part of underwater vision systems in turbid
  water leads to the occurrence of backpropagation interference (BPR), which leads to spurious
  illumination of the optical device matrix. As a promising direction in the development of underwater
  vision systems, it is proposed to use methods for subtracting POR based on information about
  the polarization of light. In the review part of the article, the latest achievements in this field are
  considered. The main part of the article presents the methodology for studying the proposed method
  for subtracting the POR based on a comparison of the results obtained by processing images
  with known methods for estimating the Stokes vector parameters DoLP and AoLP, which allow
  obtaining information about the degree of polarization and the prevailing polarization angles of
  the scene, respectively. The experimentally obtained results of processing an underwater scene in
  water of varying degrees of turbidity using the DoLP, AoLP algorithms and the proposed methods
  for subtracting the POR are presented. Distinctive features are the use of four rather than two
  polarization directions in calculations, as well as the original mathematical apparatus for processing
  signals from the machine vision camera matrix.

• METHOD OF MOVING OBJECT POSITIONING WITHOUT USING GLOBAL GEO-REFERENCED DATA

  Е. V. Lishchenko, E.V. Melnik, А. S. Matvienko, А.Y. Budko

  2025-01-09

  Abstract ▼

  The paper considers the problem of determining the current coordinates of moving object in the
  conditions of unstable signal from the global navigation satellite system (GNSS). The relevance of the
  work is due to the fact that in recent years moving object are increasingly used in virtually all sectors of
  industry, agriculture, transportation, solving a variety of tasks of surveillance, reconnaissance, monitoring
  the state of controlled objects, search and rescue operations, cargo delivery and much more. At the same
  time, the success of flight missions largely depends on how accurately and efficiently its onboard navigation
  system works in real time. The existing solutions for creating onboard positioning systems involve the
  use of inertial and GNSS. However, they have the disadvantage of partial or complete absence of data
  from the GNSS (Global Positioning System). This paper describes a method for maintaining a given accuracy
  of moving object spatial positioning under conditions of partial or complete absence of data from the
  object's GSP. This approach is based on a combination of computer vision methods for processing video
  stream frames from the moving object on-board vision system (OVS) in order to ensure positioning accuracy
  under conditions of partial or complete absence of data from satellite navigation systems. Based on
  the advanced method, an algorithm has been developed for automated determination of moving object
  coordinates in the absence of georeferencing data from global positioning systems (GPS). Experiments
  have been carried out, which demonstrated the reduction of time costs for description and matching of key
  points and improvement of the accuracy of image matching. The developed algorithm was used to solve
  the problem of satellite image matching, which is an important step in the moving object positioning problem
  without the use of global geo-referencing data.

• ESTIMATION OF THE SPATIAL POSITION OF AN ON-BOARD CAMERA BY COMPARING AERIAL IMAGES AND SATELLITE IMAGE DATA

  А.Y. Budko , Т.А. Gaida , Z.А. Ponimash

  2026-02-27

  Abstract ▼

  The article describes a method for estimating the spatial position of an onboard camera of an aircraft. This method involves comparing aerial photographs and georeferenced remote sensing (RSS) data by using a neural network detector to detect stable spatiotemporal reference points in both datasets. This method then solves the well-known Perspective-n-Point (PnP) problem for estimating rotation and translation matrices that minimize the reprojection error based on the correspondences between 3D world points and 2D points of their projections onto the onboard camera matrix. This approach can be used to solve the pressing problem of aircraft localization in the absence of global navigation satellite system signals. Road intersections are selected as stable spatiotemporal reference points that are clearly visible in RSS data and aerial photographs. Other local semantic image patterns characteristic of a particular area may serve as an alternative. Since direct comparison of remote sensing and airborne images is difficult due to significant differences in shooting conditions, the use of robust landmark detectors based on artificial neural network (ANN) algorithms is proposed. To train the robust detector, a mixed dataset was created using satellite and airborne imagery. The mixed dataset was labeled using a 3D Gaussian function normalized to unity with a apex at the intersection center, the graph of which is projected onto a 2D mask of the training set. The parameters of the Gaussian function are calculated based on the radius of the circle enclosing the intersection. Using a normalized 3D Gaussian function with a apex at the geometric center of the intersection projection allows the network to predict the probability of each image pixel belonging to the intersection, with a maximum at the intersection center, which increases positioning accuracy due to more precise georeferencing of the landmark point in the global 3D dataset. A U-Net-type artificial neural network was trained as an intersection detector. A differentiable analog of the Dice metric was used as a training quality metric. AdamW, coupled with a CosineAnnealingLR cosine learning rate planner, was used as an optimizer. The final section of the paper presents the results of comparing satellite data and airborne imagery using the proposed method.

• OPTIMIZATION METHOD FOR GESTURE CLASSIFIER

  N.А. Budko

  2023-02-27

  Abstract ▼

  The work is devoted to the study of the possibility of optimizing the process of synthesis of
  gesture classifiers by selecting the most significant channels of electromyographic (EMG) activity
  of the muscles of the forearm. The first part of the study is devoted to the development and analysis
  of the performance of gesture classifiers with a different number of EMG channels, ranked by
  significance based on the Pearson criterion. The solution of the problem of classification of gestures
  by EMG signals was first implemented on the basis of ensembles of decision trees trained by
  the gradient boosting method. For this, software was developed that allows automatic synthesis
  and training of gesture classifiers. Next, a series of studies was carried out to find the optimal
  number of EMG channels based on three criteria: the classifier learning rate, the performance of
  the trained model, and the area under the ROC AUC error curve. To do this, a cycle of training
  and testing of the classifier was carried out for data sets recorded at different positions of the electrodes
  on the forearm. Then, range diagrams of the studied criteria were constructed for various
  numbers of EMG channels involved in the work from 1 to 8, ranked by significance in each of the
  samples. It was found that the optimal number of EMG channels involved under the experimental
  conditions was 3-6, since a further increase did not lead to a decrease in the classification error,
  while significantly degrading the performance. The proposed method allows you to automatically
  select the channels, the electrodes of which are located above the most informative areas of the
  forearm in case of an accidental change in the position of the sensors. The second part of the work
  contains the results of a full-scale experiment to demonstrate the possibility of controlling a
  wheeled robot through EMG analysis.