Visual Scheme Research Articles

Design and Optimal Pose‐Constrained Visual Servoing of a Novel Active Flexible Endoscope Holder System for Solo Laparoscopic Surgery

Laparoscopic surgery (LS) has become an effective and widely accepted therapy for patients. However, performing LS requires advanced training and skills, as it demands high precision and dexterity due to visual feedback depending on the collaboration between the surgeon and endoscope assistant, restricted field of view, and limited dexterity inside the body. Thus, a flexible endoscope holder system with an automatic control scheme is proposed to assist LS performance, allowing the surgeon to perform solo LS. A DNA‐inspired helix‐based structure with wide‐angle and constant curvature bending is presented and made of printable nylon with selective laser sintering. The proposed flexible mechanism reduces the cost of manufacturing and assembling. Also, it has a hollow design for embedded sensing and actuation. Then, an optimization method is proposed, which provides a reference for the endoscope installation, robot placement, and pose selection. In addition, aiming at the misorientation problem, a pose‐constrained visual servoing control scheme for the endoscope system is developed. Both simulation and physical experiments are conducted to verify the effectiveness and feasibility of the proposed control scheme. Experimental results demonstrate that the proposed visual servoing scheme can maintain the optimized pose and reduce misorientation during automatic view steering.

Which factors affect the Italian consumer’s intention to insect-eating? An application of an integrated attitude-intention-eating model

This research explored the role of key socio-psychological dimensions on individuals’ intention to eat insect-based foods (IEIBF). In particular, the theoretical framework included biospheric values and social norms, whose impact on pro-environmental choices has been largely evidenced, but also those factors related to animal welfare and neophobia/neophilia attitudes. An online survey was carried out in different Italian regions intercepting 1402 individuals to verify the relationships predicted by our proposed model, i.e. the Integrated Sustainable Neophilic Insect-based Eating Model (ISNIEM), for predicting IEIBF. Participants completed a survey containing measures of IEIBF, biospheric values, general pro-environmental beliefs, attitude toward sustainability, food neophilia, concern for insect welfare, and social norms (both injunctive and descriptive). Structural Equation Modelling (SEM) technique was used for testing the ISNIEM. The main findings show that individuals having higher concern about environmental and ethical sustainability are more open to eating insects, especially if the latter are treated ethically. Overall, these results suggest that a typical consumer of insect foods is likely to be driven to their consumption from one side by curiosity (related to the neophilia dimension), and from the other side either by the sense of responsibility towards the environment or by the concern for ethical issues (i.e., animal welfare). The integrated attitude-food-intention model used to explore intention to eat insect foods (IEIBF) with a visual scheme represents an innovative approach in the study of consumer behaviour.

Exploring visual representations of computer mouse movements for bot detection using deep learning approaches

Nowadays, bots can be seen everywhere on the Internet and are responsible for a large percentage of website traffic. The problem of bot detection has increasingly gained attention since more and more bots have been abused from click fraud in online advertisements to launching credential stuffing attacks for harvesting user accounts at a large scale. In this work, we present an end-to-end deep framework for bot detection based on computer mouse movements. Specifically, we propose a novel visual representation scheme that can simultaneously encode spatial and kinematic information in mouse movements into an image which can then be used as the input to Convolutional Neural Networks (CNN). Various strategies to encode kinematic features into images are investigated to obtain a better scheme of visual representation. Experimental results show that the proposed representation scheme in combination with CNN outperforms several baseline models with a TPR of 99.34% in detecting known bots and can be generalized to unknown bots with a highest accuracy of 99.20%. We also demonstrate that the proposed approach can reach acceptable performance levels even for models trained with a small number of training samples. This makes the deployment of our approach easier in real-world scenarios.

Exploring the path of environmental visual design based on the perspective of new media

Abstract This paper first constructs a convolutional neural network model by convolutional layer, activation layer and normalization layer and then uses the style migration algorithm and image semantic segmentation algorithm based on the convolutional neural network to construct an environmental visual design model based on the style migration algorithm. The experimental analysis of the effective application of the environmental visual design scheme under the new media perspective is conducted after data pre-processing. The results show that from the total loss function graph after weighting, the function decreases from 0.03 to 0.01 after a small fluctuation, and the style migration imaging tends to a stable state, and the model is optimal now. This demonstrates the exploration of environmental visual design and encourages the innovative development of visual design in the new media environment.

Role of speckle-tracking echocardiography in diagnosis and further prognosis of coronary heart disease

Echocardiography is one of the first-line methods of detection and evaluation of coronary artery disease, which helps clinicians in the diagnostic and prognostic evaluation of this disease. However, visual assessment of abnormal left ventricular myocardial wall motion is not always appropriate in detecting less severe or transient myocardial ischemia and in making an accurate differential diagnosis. Speckle-tracking echocardiography is a widely available non-invasive tool that can easily and quickly provide clarifying information and significantly extend the standard echocardiographic study, as the method allows you to identify subtle myocardial damage and local areas of ischemia that correspond to the pattern of coronary disease, and obtain an objective report in the form of simple visual scheme, very easy to use for differential diagnosis and treatment. Therefore, this method, which is increasingly used in various manifestations of coronary insufficiency and is convenient to use both at rest and with stress echocardiography, has proved efficient results in terms of predicting coronary heart disease, quantitative interpretation of emerging or persistent ischemia, assessment of the clinical course and outcome, and left ventricular remodeling. In this review, we have attempted to reflect the currently available findings that demonstrate the significance of speckle-tracking echocardiography for the evaluation and follow-up of coronary heart disease, including some congenital anomalies of the coronary arteries, and to touch on the main achievements and discoveries in the use of two-dimensional and three-dimensional deformation parameters and their potential application for a wider introduction into clinical practice.

Modern approaches to digital product development in the conditions of market variability

Currently, the interest in design thinking in the world has made it possible to add many techniques and visual schemes that can solve various kinds of problems, including for building a strategy to achieve the company’s goals, its corporate spirit and the development of creative skills of its employees. Already today, it is possible to call the merit of design thinking the shift of emphasis in corporate goals to customer orientation, to the mobilization of collective intelligence to solve problems, to understanding the importance of empathy, creativity and entrepreneurship. A comparative analysis of existing design thinking frameworks is given, including from researchers of companies and design centers such as Hasso Plattner Institute of Design at Stanford University (d.school), The HPI-Stanford Design Thinking Research Program, Stanford d.school’s Design Thinking framework (IDEO), Google, Intel. Frameworks structure the process of design thinking in 5-6 phases: from empathy, when the needs and requests of consumers are studied, to testing an innovative product or service being created. The place of “design thinking” approach in the general concept of human-centered design is shown. The task of design thinking, the development of innovative products or services focused on human needs, is to provide a solution to a specific problem of a certain target audience. The paper outlines the principles of designing a digital innovative product. The five steps of designing a new product are described and the features of using various visualization techniques are given. The study uses such tools as the “Persona” model, Job Stories and User Stories frameworks. The creating process features of an innovative product based on the integration of design thinking, Lean Startup methodology and the theory of Jobs to Be Done are described. The life cycle of the innovation development “Creation – Evaluation – Training” is presented which is based on the process of validated learning. Changes in the outline of the business model in the lean startup methodology are presented. New segments have been added to the Lean Canvas business model – “Key Partners”, “Key Activities”, “Key Resources”, “Consumer Relations”.

Reform of college students’ teaching management informatization under the background of big data and IoT

With the continuous layout of intelligent processing technology in the teaching field, it has become an important trend to apply big data and Internet of Things technology to the teaching management of college students. Big data technology can comprehensively analyze the teaching situation of students through massive data, and then provide the best solution, the current big data technology already has a strong technical foundation, which can provide assistance for students’ teaching. The Internet of Things technology can collect personal information of students through sensors and miniature portable devices, and provide the data to the background server for big data analysis. In order to analyze the current status of the informationization of teaching management for college students, this paper first surveys some colleges and institutions through questionnaire surveys, and conducts data analysis on the collected questionnaires, and puts forward a solution based on big data and the Internet of Things based on the data analysis results. Finally, the combination of big data and Internet of Things technology for student teaching data collection, effect evaluation scheme, intelligent arrangement of integrated courses, intelligent recommendation scheme for students’ teaching needs, and teaching management information visualization scheme are analyzed, and it is found that the combination of big data and Internet of Things related technologies It can effectively improve the efficiency of teaching management of college students.

Hybrid vision/strain-based control strategy for a parallel manipulator with flexible links

Parallel manipulators may present higher speed/acceleration ratios and energy efficiency when compared with serial manipulators. In addition, reducing their components’ inertia can further improve performance. However, this design alternative might yield vibrations requiring novel joint and task space control strategies. While the former involves precise models, the latter might require adequate computation vision schemes. These requirements impose critical challenges. This manuscript proposes a hybrid control strategy and experimentally investigates using a 3RRR prototype with flexible links. This hybrid strategy comprises two control loops: position-based visual servo and strain-based feedback control schemes. The strain-based loop uses a sliding mode control and the time derivative of the signals obtained by strain gauges installed in the flexible links. Compared with the position-based visual servo scheme, the hybrid strategy can considerably attenuate the vibrations since the approach reduced the overshoot response by 36% and the maximum value of the Euclidean error on position by 38%, on average.

Visual-Impedance-Based Human–Robot Cotransportation With a Tethered Aerial Vehicle

Physical human-robot interaction (pHRI) in the field of aerial vehicles has received more research attention in recent years. In this work, a visual impedance control strategy for human-aerial robot cooperative transportation with a tethered vehicle is presented. Without a positioning system, the aerial vehicle is controlled to follow the human partner by using cable force and visual features of the object as feedback. Furthermore, being aware of human motion is important to improve efficiency and smoothness of the cooperation. Without measuring velocities of the aerial vehicle and the human, we propose to directly estimate relative velocity of them by a vision-based velocity observer. This estimated velocity is then integrated into a visual impedance scheme. The stability of the system is rigorously proved by Lyapunov analysis and passivity analysis. Indoor experiments where a human participant transports a long bar with a tethered aerial vehicle are conducted. Results of experiments under different human velocities and intentions demonstrate the effectiveness and reliability of the proposed method.

Visual Research on the Design of Immersive Rice Paper Cultural Theme Space

As an intangible cultural heritage of human beings, rice paper technology can be used as a design theme in the environment to improve the cultural quality of the environment. To spread the culture of rice paper, it is necessary to export the cultural content naturally. An immersive environment makes this easier to achieve. In the environment construction of rice paper culture, the atmosphere of rice paper culture is reflected as a whole through space layout and lighting atmosphere. The immersive environment construction with the help of virtual reality technology and interactive technology is a more detailed and vivid visual expression form. Through the overall and detailed visualization scheme design, the effect of inheriting the intangible cultural heritage of Rice paper in the state of deep addiction is realized.

Towards Visual Inspection of Distributed and Irregular Structures: A Unified Autonomy Approach

This paper highlights the significance of maintaining and enhancing situational awareness in Urban Search and Rescue (USAR) missions. It focuses specifically on investigating the capabilities of Unmanned Aerial Vehicles (UAV) equipped with limited sensing capabilities and onboard computational resources to perform visual inspections of apriori unknown fractured and collapsed structures in unfamiliar environments. The proposed approach, referred to as First Look Inspect-Explore (FLIE), employs a flexible bifurcated behavior tree that leverages real-time RGB image and depth cloud data. By employing a recursive and reactive synthesis of safe view pose within the inspection module, FLIE incorporates a novel active visual guidance scheme for identifying previously inspected surfaces. Furthermore, the integration of a tiered hierarchical exploration module with the visual guidance system enables the UAV to navigate towards new and unexplored structures without relying on a map. This decoupling reduces memory overhead and computational effort by eliminating the need to plan based on an incrementally built, error-prone global map. The proposed autonomy is extensively evaluated through simulation and experimental verification under various scenarios and compared against state-of-art approaches, demonstrating its performance and effectiveness.

Safe-LBP: A visually meaningful image encryption scheme based on LBP and compressive sensing

Recently, some visually meaningful image encryption schemes have been proposed to protect the visual security of images. However, much of the existing literature on visual image encryption ignores the features of cover images. To address the above issues, this paper proposes a content-adaptive visual image encryption scheme based on piecewise linear chaotic map (PWLCM), compressive sensing (CS), and safe local binary pattern (Safe-LBP). First of all, the plain image are sparse by applying discrete wavelet transform (DWT). Secondly, the cipher data is obtained by encrypting and compressing the sparse matrix using Arnold scrambling and CS. Next, we design a new LBP operator to discover appropriate embedding place in the cover image, which tremendously improves the imperceptibility of the cipher image. Finally, the visually meaningful steganographic image is obtained by embedded all cipher data through the least significant bit (LSB) method. Simulations and comparisons show that the proposed algorithm has excellent visual security and decryption quality and proves its robustness against various types of noises and data loss.

High-Impedance Fault Detection Methodology Using Time–Frequency Spectrum and Transfer Convolutional Neural Network in Distribution Networks

High-impedance fault (HIF) detection has always been difficult in distribution networks due to the lack of field data and the large difference between field and simulation waveforms. Based on the characteristics of zero-sequence currents, a novel HIF detection methodology is proposed, which combines time–frequency spectrum (TFS) and transfer convolutional neural network (TCNN). First, the TFSs are acquired by applying continuous wavelet transform (CWT) to the collected zero-sequence currents. Then, the TFSs of simulated zero-sequence currents are utilized for training source-domain convolutional neural network (SCNN). Next, the SCNN is transfer learned with very few TFSs of field zero-sequence currents to obtain TCNN. The performance of the proposed method is verified by simulation samples and field samples. The results show that the proposed method can effectively extract fault features from small-scale training samples under different fault circumstances. Besides, TCNN can adaptively extract the effective features of field HIF and detect field HIF more accurately than SCNN. Finally, this article provides a visualization scheme for interpretability of the neural network, which offers visual explanations for the decision-making basis of the neural network.

Exochorion in the tribe Nymphalini (Lepidoptera: Nymphalidae): the genus Hypanartia Hbner, [1821] and comparison with related genera.

We describe and compare the exochorion of six species of Hypanartia (H. paullus, H. bella, H. lethe, H. godmanii, H. dione disjuncta, and H. trimaculata autumna) using specimens collected in the field and deposited in entomological collections. We used a standard staining technique and scanning electron microscopy to visualize and describe the main exochorionic characters, especially the ridges and the differentiation in the micropylar, perimicropylar, and transition zones in the apical region. We included plates with photographs, drawings, and schemes for clearer visualization of these structures, which are tabulated for comparison. For our characterization, we considered the ridges in colonnade as the main exochorionic feature in the Nymphalini. Thus, we made brief comparisons of these features among several species of the tribe, particularly with the genus Antanartia. The exochorionic characters agree with the separation of groups (paullus group and dione group). By examining the characteristics of H. paullus, the basal species of the paullus group, we found that ridges with conspicuous columns (e.g., in one section only) appears to be the plesiomorphic state, while those with elongated shafts and walls only in the intercolumn represent the apomorphic state.

Passive machine vision-based defect classification in tungsten inert gas welding on SS304 using AI-based gradient descent algorithm

In modern digitization, safety industries demand flaw-free and high-integrity welds, due to part localization on high uncertainty makes automation a challenging task. Integrating robotic welding with high-value manufacturing sector makes volume rise through pre-programmed repetitive performance on desired welding space. Further, the establishment of fully automated robotic-based welding operations with different configurations on controlled localization is the least available due to unviable cost. Hence, the proposed work concentrates on internet of things (IoT)-driven robotic tungsten inert gas (TIG) welding on stainless steel (SS) 304 by incorporating online programming (OP) with visual control schemes to classify the nature of weld quality using an artificial neural network. Continuous sensory-guided techniques with IoT high-level operator interface affords automated welding planning by using feature matching strategies. Area scan-based complementary metal oxide semiconductor (CMOS) cameras have been used to capture passive vision real-time images of the weld species for defect classification. In real-time, discrete reference workpiece and image feature extraction processes seem to be complicated in an unstructured welding environment. Hence, the present idea will enhance the efficiency of high product variance, but the accuracy of the automation relies on the weld image database. The proposed method (a) adaptively regulates its welding parameter variation on the welding trajectory path, (b) adapts and produces precise good weld workpieces with a 92% production rate with flaw-free welds and (c) initiates the automatic tuning of robot kinematics through IoT closed-loop online external control strategy. The proposed research experimental results confirm that fully automated IoT-driven robotic TIG welding affords good welding with an 88% improved quality rate through online passive vision-based image feature analysis.

Visual image encryption algorithm based on compressed sensing and 2D cosine -type logistic map

A new two-dimensional cosine-type Logistic map (2D-CTLM) is proposed in this paper. Performance analysis shows that 2D-CTLM has good ergodicity, complex behavior, and a wide range of chaotic regions. To study its application, an adaptive embedded high visual security image encryption scheme is proposed by combining the system with two-dimensional compression sensing to address the shortcomings of fixed embedding positions and low transmission efficiency in existing schemes. The embedding position is fixed when the ciphertext image is embedded into the carrier image. When embedded in an inappropriate location, it will affect the decryption quality and the imperceptibility of the steganographic image. Firstly, the initial value of 2D-CTLM system is generated by using the information entropy of plaintext image and counter to generate random measurement matrix, which is used to compress and encrypt plaintext image from two directions at the same time. Secondly, the compressed cipher image is scrambled by the initial matrix generated by the GOL (game of life) rules in two-dimensional cellular automata to obtain the ciphertext image. Finally, the most suitable embedding position in the carrier image is found by combining information entropy and edge entropy. Then the unquantized ciphertext image is embedded into it by ST (slant transform) embedding method to obtain a high visual security image. To improve the transmission efficiency, the color carrier image can be selected to transmit three gray plaintext images at the same time, maintain high-quality reconstruction effect. SHA-512 and the information entropy of plaintext image are used to generate the initial value of encryption and measurement matrix, enhance the correlation between the algorithm and plaintext image, and effectively resist known plaintext and selective plaintext attacks. Simulation results and performance analysis show that compared with the existing encryption schemes, the encryption scheme has excellent visual effect, good decryption quality and good robustness.

Spherical bispectrum: a novel visualization scheme for facilitating comparisons

Recent developments of Perturbation Theory (PT), specifically the Effective Field Theory of Large Scale Structure (EFTofLSS) and its equivalents, have proven powerful in analyzing galaxy clustering statistics such as the galaxy power spectrum and bispectrum. To further this pursuit, we have devised a novel spherical-bispectrum visualization scheme that collapses configuration dependencies to highlight the scale dependence of the bispectrum. The resulting one-dimensional curves facilitate the comparison between different bispectra, for example, from simulation and PT calculation. Using the new scheme, we present a quantitative analysis of the accuracy of PT modeling by comparing PT's analytical prediction to the result from a suite of Quijote simulations. Specifically, we determine k NL, the wavenunmber below which the analytical prediction matches well with the N-body result by inspectingboth leading order (LO) and next-to-leading order (NLO) power spectrum and bispectrum at redshifts z = 0, 0.5, 1, 2, 3. We also quantify the binning effect in Fourier space and show that an appropriate correction must be applied to the analytic predictions in order to compare them with the discrete Fourier transform results obtained from N-body-simulation or real data.

Optimal Multisecret Image Sharing Using Lightweight Visual Sign-Cryptography Scheme With Optimal Key Generation for Gray/Color Images

Problem: Digital devices are becoming increasingly powerful and smart, which is improving quality of life, but presents new challenges to privacy protection. Visual cryptographic schemes provide data sharing privacy, but have drawbacks such as extra storage space, lossy secret images, and the need to store permutation keys. Aim: This paper proposes a light-weight visual sign-cryptography scheme based on optimal key generation to address the disadvantages of existing visual cryptographic schemes and improve the security, sharing quality, and time consumption of multisecret images. Methods: The proposed light-weight visual sign-cryptography (LW-VSC) scheme consists of three processes: band separation, shares generation, and signcryption/un-signcryption. The process of separation and shares generation is done by an existing method. The multiple shares of the secret images are then encrypted/decrypted using light-weight sign-cryptography. The proposed scheme uses a novel harpy eagle search optimization (HESO) algorithm to generate optimal keys for both the encrypt/decrypt processes. Results: Simulation results and comparative analysis showed the proposed scheme is more secure and requires less storage space, with faster encryption/decryption and improved key generation quality. Conclusion: The proposed light-weight visual sign-cryptography scheme based on optimal key generation is a promising approach to enhance security and improve data sharing quality. The HESO algorithm shows promise in improving the quality of key generation, providing better privacy protection in the face of increasingly powerful digital devices.

Mge-cluster: a reference-free approach for typing bacterial plasmids.

Extrachromosomal elements of bacterial cells such as plasmids are notorious for their importance in evolution and adaptation to changing ecology. However, high-resolution population-wide analysis of plasmids has only become accessible recently with the advent of scalable long-read sequencing technology. Current typing methods for the classification of plasmids remain limited in their scope which motivated us to develop a computationally efficient approach to simultaneously recognize novel types and classify plasmids into previously identified groups. Here, we introduce mge-cluster that can easily handle thousands of input sequences which are compressed using a unitig representation in a de Bruijn graph. Our approach offers a faster runtime than existing algorithms, with moderate memory usage, and enables an intuitive visualization, classification and clustering scheme that users can explore interactively within a single framework. Mge-cluster platform for plasmid analysis can be easily distributed and replicated, enabling a consistent labelling of plasmids across past, present, and future sequence collections. We underscore the advantages of our approach by analysing a population-wide plasmid data set obtained from the opportunistic pathogen Escherichia coli, studying the prevalence of the colistin resistance gene mcr-1.1 within the plasmid population, and describing an instance of resistance plasmid transmission within a hospital environment.

An Autonomous Surgical Instrument Tracking Framework With a Binocular Camera for a Robotic Flexible Laparoscope

In minimally invasive surgery (MIS), the field of view (FOV) plays a vital role. To enhance the stability of FOV and lighten the burden on surgeons, robot-assisted laparoscope systems have been developed and introduced into surgery. However, most of the existing automatic surgical tool tracking schemes with robotic laparoscopes either do not consider tracking of the depth direction or the remote center of motion (RCM) constraints. To tackle these problems, this paper presents an autonomous three-dimensional surgical instrument tracking framework for a robotic laparoscope based on a binocular camera. A 3-degree-of-freedom visual tracking scheme is derived from the kinematics of the binocular robotic laparoscope. A programmable RCM constraint is introduced due to the MIS requirement. To precisely locate the surgical instruments in FOV, a surgical instrument tip segmentation dataset with 5471 images is built and a modified real-time instance segmentation network is trained based on the dataset. Then, a semi-global matching (SGM) algorithm is utilized with the binocular camera to estimate the depth in FOV. To meet the joint limit constraint and maintain the dexterity of the robotic system, a task-priority-based control method is adopted. The simulation and experiment results demonstrate the feasibility of the proposed framework for instrument tracking with the robotic laparoscope.