Visual Research Articles

Suitability Study of Optical Coordinate Measuring Machine for Quality Assessment and Wear Phenomena Identification of Blade Edge and Surface of Planer Technical Knives.

This article discusses a comparative analysis of the wear and quality of planer knife blades used in wood planers. The novelty in this work is the use of a simple coordinate measuring machine with a vision system to assess the wear of the cutting edges of planer knives. The primary objective of the research described in this paper was to verify whether the wear of the cutting edge of planer knives can be measured quickly and accurately using an optical coordinate measuring machine with a vision system. To date, contact profilometry methods have been used for this purpose, which require a specialist apparatus and qualified measuring equipment operators and are expensive and time-consuming. The research presented in this work was conducted on twelve planer knives. The condition and wear of the working surfaces of the tested knives were assessed using an optical digital microscope. The wear of the cutting edge of the knives was measured using two methods: the contact profilometry method and an optical coordinate measuring machine equipped with a vision system. The edge profiles and their parameters obtained by the optical method were compared to the results of measurements with a stylus profilometer. Based on the research and analyses conducted, it was found that the optical method used in this research significantly shortens the time of measuring the wear of the cutting edges of planer knives. In addition, this method has a wider measurement range, and the obtained measurement results are characterized by lower measurement uncertainty.

Extensive Optic Tracts Involvement in the Acute Phase of Leber Hereditary Optic Neuropathy.

Extensive Optic Tracts Involvement in the Acute Phase of Leber Hereditary Optic Neuropathy.

AI-Based Visual Early Warning System

Facial expressions are a universally recognised means of conveying internal emotional states across diverse human cultural and ethnic groups. Recent advances in understanding people’s emotions expressed through verbal and non-verbal communication are particularly noteworthy in the clinical context for the assessment of patients’ health and well-being. Facial expression recognition (FER) plays an important and vital role in health care, providing communication with a patient’s feelings and allowing the assessment and monitoring of mental and physical health conditions. This paper shows that automatic machine learning methods can predict health deterioration accurately and robustly, independent of human subjective assessment. The prior work of this paper is to discover the early signs of deteriorating health that align with the principles of preventive reactions, improving health outcomes and human survival, and promoting overall health and well-being. Therefore, methods are developed to create a facial database mimicking the underlying muscular structure of the face, whose Action Unit motions can then be transferred to human face images, thus displaying animated expressions of interest. Then, building and developing an automatic system based on convolution neural networks (CNN) and long short-term memory (LSTM) to recognise patterns of facial expressions with a focus on patients at risk of deterioration in hospital wards. This research presents state-of-the-art results on generating and modelling synthetic database and automated deterioration prediction through FEs with 99.89% accuracy. The main contributions to knowledge from this paper can be summarized as (1) the generation of visual datasets mimicking real-life samples of facial expressions indicating health deterioration, (2) improvement of the understanding and communication with patients at risk of deterioration through facial expression analysis, and (3) development of a state-of-the-art model to recognize such facial expressions using a ConvLSTM model.

Rapid Color Quality Evaluation of Needle-Shaped Green Tea Using Computer Vision System and Machine Learning Models.

Color characteristics are a crucial indicator of green tea quality, particularly in needle-shaped green tea, and are predominantly evaluated through subjective sensory analysis. Thus, the necessity arises for an objective, precise, and efficient assessment methodology. In this study, 885 images from 157 samples, obtained through computer vision technology, were used to predict sensory evaluation results based on the color features of the images. Three machine learning methods, Random Forest (RF), Support Vector Machine (SVM) and Decision Tree-based AdaBoost (DT-AdaBoost), were carried out to construct the color quality evaluation model. Notably, the DT-Adaboost model shows significant potential for application in evaluating tea quality, with a correct discrimination rate (CDR) of 98.50% and a relative percent deviation (RPD) of 14.827 in the 266 samples used to verify the accuracy of the model. This result indicates that the integration of computer vision with machine learning models presents an effective approach for assessing the color quality of needle-shaped green tea.

Evaluation of visual quality and refractive outcomes of myopia with presbyopia after laser blended vision surgery

Objective: To investigate the postoperative outcomes and visual quality following laser blended vision (LBV) surgery for the correction of myopia with presbyopia. Methods: This is a prospective study. A total of 50 patients (100 eyes) who underwent LBV surgery for myopia with presbyopia at Beijing Tongren Hospital, Capital Medical University, between August 2021 and March 2022 were included. Uncorrected distance visual acuity (UDVA), uncorrected near visual acuity (UNVA), best-corrected distance visual acuity (BCDVA), manifest refraction spherical equivalent (MRSE) were measured preoperatively and at 1, 3, 6, 12, and 24 months postoperatively for the dominant eye, non-dominant eye, and both eyes. Effectiveness Index (EI) and Safety Index (SI) were calculated to evaluate refractive outcomes. Accommodation function and contrast sensitivity were assessed to evaluate functional vision. Objective visual quality was assessed using higher-order aberrations (HOAs) and a dual-channel visual quality analysis system. Subjective visual quality was evaluated using a questionnaire. Results: At 24 months postoperatively, the EI and SI for the dominant eye were 1.04±0.23 and 1.14±0.14, respectively, while for the non-dominant eye, they were 0.85±0.21 and 1.06±0.08, respectively. Although there were statistically significant differences in UNVA between the eyes at 1 and 3 months postoperatively (all P<0.05), no significant differences were observed from 6 months onward (all P>0.05). At 6 months postoperatively, the UNVA for the dominant and non-dominant eyes were 0.07±0.13 and 0.03±0.13, respectively, with no significant difference (P>0.05). MRSE showed no significant differences at any follow-up time points up to 24 months. Accommodation function did not decrease compared to preoperative levels at any postoperative follow-up. Increases in HOAs were primarily due to spherical aberrations, and there were no significant differences in objective visual quality before and after surgery. Contrast sensitivity improved significantly at 24 months postoperatively compared to preoperative levels. At 24 months postoperatively, 11 out of 12 patients reported good visual quality in the questionnaire. Conclusions: LBV surgery gradually achieves good binocular blended vision within 6 months postoperatively and demonstrates good safety, efficacy, and stability at 24 months. It maintains good functional vision and visual quality with high postoperative patient satisfaction.

A Novel Method for Heat Haze-Induced Error Mitigation in Vision-Based Bridge Displacement Measurement.

Vision-based techniques have become widely applied in structural displacement monitoring. However, heat haze poses a great threat to the precision of vision systems by creating distortions in the images. This paper proposes a vision-based bridge displacement measurement technique with heat haze mitigation capability. The properties of heat haze-induced errors are illustrated. A dual-tree complex wavelet transform (DT-CWT) is used to mitigate the heat haze in images, and the speeded-up robust features (SURF) algorithm is employed to extract the displacement. The proposed method is validated through indoor experiments on a bridge model. The designed vision system achieves high measurement accuracy in a heat haze-free condition. The proposed mitigation method successfully corrects 61.05% of heat haze-induced errors in static experiments and 95.31% in dynamic experiments.

Saccade size predicts onset time of object processing during visual search of an open world virtual environment

ObjectiveTo date the vast majority of research in the visual neurosciences have been forced to adopt a highly constrained perspective of the vision system in which stimuli are processed in an open-loop reactive fashion (i.e., abrupt stimulus presentation followed by an evoked neural response). While such constraints enable high construct validity for neuroscientific investigation, the primary outcomes have been a reductionistic approach to isolate the component processes of visual perception. In electrophysiology, of the many neural processes studied under this rubric, the most well-known is, arguably, the P300 evoked response. There is, however, relatively little known about the real-world corollary of this component in free-viewing paradigms where visual stimuli are connected to neural function in a closed-loop. While growing evidence suggests that neural activity analogous to the P300 does occur in such paradigms, it is an open question when this response occurs and what behavioral or environmental factors could be used to isolate this component. ApproachThe current work uses convolutional networks to decode neural signals during a free-viewing visual search task in a closed-loop paradigm within an open-world virtual environment. From the decoded activity we construct fixation-locked response profiles that enable estimations of the variable latency of any P300 analogue around the moment of fixation. We then use these estimates to investigate which factors best reduce variable latency and, thus, predict the onset time of the response. We consider measurable, search-related factors encompassing top-down (i.e., goal driven) and bottom-up (i.e., stimulus driven) processes, such as fixation duration and salience. We also consider saccade size as an intermediate factor reflecting the integration of these two systems. Main resultsThe results show that of these factors only saccade size reliably determines the onset time of P300 analogous activity for this task. Specifically, we find that for large saccades the variability in response onset is small enough to enable analysis using traditional ensemble averaging methods. SignificanceThe results show that P300 analogous activity does occur during closed-loop, free-viewing visual search while highlighting distinct differences between the open-loop version of this response and its real-world analogue. The results also further establish saccades, and saccade size, as a key factor in real-world visual processing.

Audio–video collaborative JND estimation model for multimedia applications

With the rapid development of the Internet and multimedia technologies, multimedia applications integrating audio and video are becoming increasingly prevalent in both everyday life and professional environments. A critical challenge is to significantly enhance compression efficiency and bandwidth utilization while maintaining high-quality user experiences. To address this challenge, the Just Noticeable Distortion (JND) estimation model, which leverages the perceptual characteristics of the Human Visual System (HVS), is widely used in image and video coding for improved data compression. However, human visual perception is an integrative process that involves both visual and auditory stimuli. Therefore, this paper investigates the influence of audio signals on visual perception and presents a collaborative audio–video JND estimation model tailored for multimedia applications. Specifically, we characterize audio loudness, duration, and energy as temporal perceptual features, while assigning the audio saliency superimposed on the image plane as the spatial perceptual feature. An audio JND adjustment factor is then designed using a segmentation function. Finally, the proposed model combines the video-based JND model with the audio JND adjustment factor to form the audio–video collaborative JND estimation model. Compared with existing JND models, the model presented in this paper achieves the best subjective quality, with an average PSNR value of 26.97 dB. The experimental results confirm that audio significantly impacts human visual perception. The proposed audio–video collaborative JND model effectively enhances the accuracy of JND estimation for multimedia data, thereby improving compression efficiency and maintaining high-quality user experiences.

Evaluating the impact of tubular retractors in glioma surgery: A systematic review and meta-analysis

BackgroundAchieving safe, maximal tumor resection in gliomas can be challenging due to the tumor’s intricate relationship with surrounding structures. Tubular retractors offer a minimally invasive approach, preserving functional pathways and reducing complications. To assess their efficacy and safety, we conducted a systematic review and meta-analysis. MethodsA search across databases identified 26 studies meeting inclusion criteria, encompassing 106 patients with various glioma types and tumor locations. ResultsAmong 26 eligible studies, 15 provided sufficient data on 106 patients (median age: 50.5 years). Glioblastoma multiforme constituted 52.4 % of tumors, followed by IDH-mutant astrocytomas at 31.0 %. Tumor locations varied, with intraventricular and thalamic involvement in 16.3 % (16/98) of cases, followed by temporal (12.2 %), frontal and occipital (each 8.16 %), basal ganglia (8.16 %), parietal (7.14 %), optic pathway (2.04 %), and caudate nucleus (1.02 %) involvement. VyCor and Brainpath retractors were most used (22.6 % and 21.7 %, respectively). Tubular retractors were often combined with the exoscope (35.9 %). Gross total resection (GTR) was achieved in 69.4 % of cases, near-total resection (NTR) in 5.1 %, and subtotal resection/partial resection (STR/PR) in 25.5 %. Mean extent of resection (EOR) significantly differed between GTR and STR/NTR/PR groups (p<0.001). Postoperative complications included visual deficits (6.38 %), hemiparesis or weakness (2.13 %), multiple complications (1.06 %), and other unspecified complications (3.19 %). ConclusionTubular retractors are a valuable intraoperative adjunct and component of the surgical armamentarium for glioma surgery allowing bimanual operative techniques to manage hemostasis directly with excellent surgical outcomes and an acceptable complication profile.

Light-Adapted Optoelectronic-Memristive Device for the Artificial Visual System.

With the development of artificial intelligence systems, it is necessary to develop optoelectronic devices with photoresponse and storage capacity to simulate human visual perception systems. The key to an artificial visual perception system is to integrate components with both sensing and storage capabilities of illumination information. Although module integration components have made useful progress, they still face challenges such as multispectral response and high energy consumption. Here, we developed a light-adapted optoelectronic-memristive device integrated by an organic photodetector and ferroelectric-based memristor to simulate human visual perception. ITO/P3HT:PC71BM/Au as the light sensor unit shows a high on/off ratio (Iph/Id) reaching ∼5 × 104 at 0 V. The memristor unit, consisting of ITO/CBI@P(VDF-TrFE)/Cu, has a RON/ROFF ratio window of ∼106 under 0.05 V read voltage and ultralow power consumption of ∼1 pW. Moreover, the artificial visual perception unit shows stable light-adapted memory windows under different wavelengths of irradiation light (400, 500, and 600 nm; they meet the spectral range of human visual recognition) and can clearly identify the target image ("T" shape) because of the apparent contrast, which results from the high ROFF/RON ratio values. These results provide a potential design strategy for the development of intelligent artificial vision systems.

Carboplatin desensitization in the era of target therapies: still worthwhile?

Unresectable pediatric low-grade gliomas (LGG) usually need adjuvant therapy, and carboplatin hypersensitivity reaction (HR) commonly leads to premature treatment cessation of a standard chemotherapy regimen. In the molecular era, advances in understanding tumor genetic characteristics allowed the development of targeted therapies for this group of tumors; however, cost-effectiveness assessment of treatments, especially in low-income countries, is crucial. The aim is to describe the results of carboplatin desensitization protocol in a single center in a middle-income country. Prospective analysis of children with LGG submitted to carboplatin desensitization from December 2017 to June 2020 with follow-up until April 2024. Nine patients were included. The mean age was 11years. Five patients were male. Seven had optic pathway and two cervicomedullary location. Six had histologic diagnosis and four molecular analyses. The incidence of carboplatin reactions during the study period was 39.1%. Six patients underwent skin prick test, three with positive results. The first HR occurred, on average, around the 9th cycle of treatment. All patients had cutaneous symptoms, and five out of nine had anaphylaxis as the first reaction. 77.7% of the patients completed the protocol, and the clinical benefit rate (stable disease and partial response) was 88.8%. Six patients further required other lines of therapy. Monthly, the total cost for carboplatin was $409.09, and for target therapies (dabrafenib plus trametinib), $4929.28 to $5548.57. Our study presented an interesting and cost-effective option where desensitization allowed children with HR to be treated with first-line therapy, avoiding the discontinuation of an effective treatment.

Barrier Lyapunov Function-based Backstepping Controller Design for Path Tracking of Autonomous Vehicles

This research proposes a novel BLF-based backstepping controller for path tracking of Autonomous Vehicles (AVs) with unknown dynamics and unmeasurable states. The proposed framework includes: (1) forming geometric-dynamic model of the vehicle by combining the dynamics of the vehicle with the kinematics of the visual measurement system, (2) designing a fixed-time Extended-State Observer (ESO) to estimate the unknown dynamics and unmeasurable states, and (3) introducing a BLF-based controller for faster response and more accurate path tracking compared to previous BLF-based controllers. Besides the novelty of the BLF-based controller, by transforming the closed-loop error dynamics into a unified proportional-derivative (PD)-type structure, an intuitive criterion is proposed to provide a systematic procedure for comparing BLF-based controllers. A combined BLF is further proposed based on this performance criterion to eliminate the sensitivity of BLF-based controllers to the magnitude of the constraint. The stability analysis is performed for the fixed-time ESO and the closed-loop control system. MATLAB/CarSim co-simulation is conducted to evaluate the performance of the proposed control system. The outcomes of the work show that the closed-loop control system is exponentially stable. In addition, it can provide a faster response and result in more accurate path tracking compared to previous BLF-based control systems.

Presentation of multiple task instructional sets impacts auditory Stroop performance during dual task locomotion.

The auditory Stroop is a modification of the classic Stroop paradigm commonly used in dual-task research when the motor task requires the visual system. Despite its use, there are gaps in our understanding of this tool. For example, in visual/auditory Stroop paradigms, neutral cues irrelevant to the required response, which theoretically cause less interference/facilitation, are used to elucidate effects of visual/auditory demands on neural processes. Specifically, in auditory Stroop paradigms the use and choice of neutral cue words is inconsistent. To address these gaps, we instrumented participants with kinematic markers and a digital microphone and asked them to respond to auditory Stroop cues and neutral cue words consisting of either one or two syllables, while simultaneously performing an unobstructed locomotor task. Two blocks of trials were collected. In one block, participants had prior knowledge that either an auditory Stroop or a neutral word stimulus would be presented (Known); a second block presented both types of cognitive cues in a random order to participants (Mixed). We observed main effects of cognitive task (neutral, incongruent, congruent) and instructional set (Known, Mixed) on response times, but not on center of mass velocity. Also, more time was required to verbally respond to an incongruent compared to congruent or neutral task across all conditions, and neutral task words with one syllable resulted in longer response times compared to two syllable neutral words. We recommend that researchers include neutral cues when using the auditory Stroop test and to carefully consider their neutral word choice.

Interspecies-chimera machine vision with polarimetry for real-time navigation and anti-glare pattern recognition

Cutting-edge humanoid machine vision merely mimics human systems and lacks polarimetric functionalities that convey the information of navigation and authentic images. Interspecies-chimera vision reserving multiple hosts’ capacities will lead to advanced machine vision. However, implementing the visual functions of multiple species (human and non-human) in one optoelectronic device is still elusive. Here, we develop an optically-controlled polarimetry memtransistor based on a van der Waals heterostructure (ReS2/GeSe2). The device provides polarization sensitivity, nonvolatility, and positive/negative photoconductance simultaneously. The polarimetric measurement can identify celestial polarizations for real-time navigation like a honeybee. Meanwhile, cognitive tasks can be completed like a human by sensing, memory, and synaptic functions. Particularly, the anti-glare recognition with polarimetry saves an order of magnitude energy compared to the traditional humanoid counterpart. This technique promotes the concept of interspecies-chimera visual systems that will leverage the advances of autonomous vehicles, medical diagnoses, intelligent robotics, etc.

Automated Inspection Approach for OCA Particles in Multi-layered Cover Glass of Display Module Assembly

Abstract CG(Cover Glass) is a critical component of electronic screens, and its manufacturing quality is closely relevant to the display effect of the screen. To satisfy the requirement of quality control, a machine vision system for real-time inspection of particles in the OCA(Optically Clear Adhesive) layer of CG is presented in this paper. With a brief description of the optical logic of particle imaging and the design of the vision system, our emphasis is put on the post-processing image analysis. To align the particles regions in the CG under multi-mode imaging, a spatial alignment calibration algorithm is proposed with perspective distortion correction to calculate the triaxial offset. Then, a CLAHE+PM filtering is adopted to enhance the contrast of the particle. Furthermore, a Meanshift method combined with adaptive local thresholding is proposed to extract the contours of tiny particles. Finally, to distinguish between multiple layers of particles in the CG and detect OCA particles, a combination of fast background reconstruction and ASGD-SVM(Averaged Stochastic Gradient Descent-Support Vector Machine) is used. According to in-line experiments and tests, our system can find out a majority of the OCA particles with a PR(over-detection rate) of 1.31% and a PM(miss detection rate) of 0.33% for over 10,000 CG samples.

Application of computer vision and machine learning in morphological characterization of Adansonia digitata fruits

Measuring fruit mass and volume is a time-consuming and tedious task that can affect production planning. This study developed a computer vision system to estimate the volume and mass of baobab fruits from single-view images captured from inexpensive and readily available cameras such as those in smartphones. The baobab fruits were collected from two study fields within the savanna ecological zone. Their images were captured, and subsequently, they were detected and segmented with over 97 % accuracy. The segmented images were binarized, and two-dimensional (2D) features such as the segmented area, centroid, bounding box, equivalent diameter, and major diameter were extracted from them. The volumes of the fruits were estimated from the 2D features using random forest, linear, polynomial, and radial support vector machine models. All the models achieved high goodness of fit; however, the random forest model delivered the best performance, with an R2 value of 99.8 %. The relationship between mass and volume was a quadratic equation (mass = 38.23 + 0.25 × volume + 4.49e−05 × volume2) and had an R2 value of 92 %. Correlations were validated via plots and statistical tests, and credible intervals of point estimates were determined from the posterior distributions of their samples. This highlights the potential of artificial intelligence methods to be applied in a less constrained environmental setting for ecological research and agricultural management. Commercial companies producing baobab powder and seed oil should apply these models for effective production planning. To enhance the model, it would be beneficial to gain a better understanding of how climate gradients affect the morphological characteristics of baobab fruits.

Affordable 3D Orientation Visualization Solution for Working Class Remotely Operated Vehicles (ROV).

ROV operators often encounter challenges with orientation awareness while operating underwater, primarily due to relying solely on 2D camera feeds to manually control the ROV robot arm. This limitation in underwater visibility and orientation awareness, as observed among Malaysian ROV operators, can compromise the accuracy of arm placement, and pose a risk of tool damage if not handle with care. To address this, a 3D orientation monitoring system for ROVs has been developed, leveraging measurement sensors with nine degrees of freedom (DOF). These sensors capture crucial parameters such as roll, pitch, yaw, and heading, providing real-time data on the ROV's position along the X, Y, and Z axes to ensure precise orientation. These data are then utilized to generate and process 3D imaging and develop a corresponding 3D model of the operational ROV underwater, accurately reflecting its orientation in a visual representation by using an open-source platform. Due to constraints set by an agreement with the working class ROV operators, only short-term tests (up to 1 min) could be performed at the dockyard. A video demonstration of a working class ROV replica moving and reflecting in a 3D simulation in real-time was also presented. Despite these limitations, our findings demonstrate the feasibility and potential of a cost-effective 3D orientation visualization system for working class ROVs. With mean absolute error (MAE) error less than 2%, the results align with the performance expectations of the actual working ROV.

A framework to improve causal inferences from visualizations using counterfactual operators

Exploratory data analysis of high-dimensional datasets is a crucial task for which visual analytics can be especially useful. However, the ad hoc nature of exploratory analysis can also lead users to draw incorrect causal inferences. Previous studies have demonstrated this risk and shown that integrating counterfactual concepts within visual analytics systems can improve users’ understanding of visualized data. However, effectively leveraging counterfactual concepts can be challenging, with only bespoke implementations found in prior work. Moreover, it can require expertise in both counterfactual subset analysis and visualization to implement the functionalities practically. This paper aims to help address these challenges in two ways. First, we propose an operator-based conceptual model for the use of counterfactuals that is informed by prior work in visualization research. Second, we contribute the Co-op library, an open and extensible reference implementation of this model that can support the integration of counterfactual-based subset computation with visualization systems. To evaluate the effectiveness and generalizability of Co-op, the library was used to construct two different visual analytics systems each supporting a distinct user workflow. In addition, expert interviews were conducted with professional visual analytics researchers and engineers to gain more insights regarding how Co-op could be leveraged. Finally, informed in part by these evaluation results, we distil a set of key design implications for effectively leveraging counterfactuals in future visualization systems.

Initial point positioning of weld seam and robot pose estimation based on binocular vision

Abstract In order to rapidly identify and locate the weld seam initial point in robotic automated welding, we established a binocular vision system and proposed a weld seam initial point localization algorithm named WIPL-Net. Built upon the Fully Convolutional One-Stage object detection network, WIPL-Net introduces a lightweight ResNext as its backbone network and incorporates channel attention and enhanced feature fusion mechanisms to enhance feature detection and extraction capabilities. Subsequently, WIPL-Net is utilized to obtain the weld seam’s initial point, and its three-dimensional coordinates are determined through trigonometric measurements. To further estimate the robot’s posture at the initial point, we performed sparse three-dimensional reconstruction of the local region centered on the weld seam initial point based on You Only Look At Coefficients of Tensors instance segmentation and feature point matching. Finally, we conducted comparative experiments on WIPL-Net and conducted weld seam initial point localization experiments in real welding scenarios. The results demonstrate that our proposed method achieves a positioning error of less than 1.2 mm for the weld seam’s initial point and a pose error of less than 10 degrees for the robot, meeting the requirement for real-time positioning of the weld seam’s initial point.

Digital technologies for signal radio vision and radio monitoring

Objectives. Radiophysical processes involving the electrodynamic formation of signal radio images diffusely scattered by the signature of small-sized objects or induced by the near field of radio devices are relevant for identifying radiogenomic (cumulant) features of objects in the microwave range in the development of neuroimaging ultra-short pulse (USP) signal radio vision systems, telemonitoring, and near-radio detection. The paper sets out to develop methods and algorithms for vector analysis of radio wave deformation of nonstationary fields forming a signal radio image based on radiophysical and topological characteristics of small-sized objects; to develop software and hardware for registration and neural network recognition of signal radio images, including methods for the synthesis and extraction of signal radiogenomes using digital twins of objects obtained through vector electrodynamic modeling; and to analyze signal radio images induced by elements of printed topology of electronic devices.Methods. The study is based on statistical radiophysics methods, time-frequency approaches for wavelet transformation of USP radio images, numerical electrodynamic methods for creating digital twins of small-sized objects, as well as neural network authentication algorithms based on the cumulant theory of pole-genetic and resonant physically unclonable functions used in identifying signal radio images.Results. The results of fundamental research on electrodynamic effects of vector-wave deformation of nonstationary fields of sub-nanosecond configuration are presented as a means of identifying and authenticating signal radio images. Neural network techniques for cumulant formation of radio genomes of signal radio images are proposed on the basis of pole-genetic and resonant functions.Conclusions. A radiogenome, representing the unique authenticator of a radio image, is shown to be formed on the basis of physically unclonable functions determined by the structure and set of radiophysical parameters of the image. Cumulant features of signal radio images identified on the basis of pole-genetic and physically unclonable resonant functions of small-sized objects are revealed.