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The digital media recording and broadcasting classroom using Internet real-time intelligent image positioning and opinion monitoring in communication teaching is researched and analyzed.

First, spatial grid positioning and monitoring and image intelligent recognition technologies were used to extract and analyze teaching images by mastering Internet of Things (IoT) technology and establishing an intelligent image positioning and opinion monitoring digital media recording and broadcasting system framework. Next, a positioning node algorithm was utilized to measure the image distance, and then a moving node location model under the IoT was established. In addition, a radial basis function (RBF) neural network was used to realize the signal transmission function. The experimental data of the adopted RBF based on the optimization of the adaptive cuckoo search (ACS-RBF) neural network, particle swarm algorithm neural network, and method of least squares optimization were compared and analyzed. In addition, a more efficient RBF neural network was adopted. Finally, the digital media recording and broadcasting classroom scheme of real-time intelligent image positioning and opinion monitoring was designed. In addition, the application environment of digital media actual teacher teaching was detected, and recording and broadcasting pictures were analyzed and researched.

The actual value, predicted value, and the number of predicted samples of the ACS-RBF model were all better than those of the two other neural networks. According to the analysis and comparison of the sampling optimization Monte Carlo localization (SOMCL), Monte Carlo, and genetic algorithm optimization-based Monte Carlo positioning algorithms, the SOMCL algorithm showed better robustness, and its positioning efficiency was superior to that of the two other algorithms. In addition, the SOMCL algorithm greatly reduced the positioning and monitoring energy consumption.

The application of real-time intelligent image positioning and monitoring technology in actual communication teaching was realized in the study.

This paper examines the acceptance, of a group of 79 students, of an educational forum, used for mobile and distance learning, that has been modified to include empathic characteristics and affective principles.

With this study is proposed that the introduction of empathic and affective principles in educational forums is a useful and effective way to increase students’ participation and motivation in educational contexts. After an analysis of existing literature and research on the subject of empathic technology, the unified theory of acceptance and use of technology (UTAUT) was used as a framework for the research model. The analysis of their acceptance is done via an extended version of the UTAUT that focuses on the use of the variable attitude toward technology and uses gender, age and experience as moderators. A partial least square technique has been used to test the nine hypotheses.

The results confirmed three of the nine hypotheses: performance expectancy and effort expectancy had a positive influence on the students attitudes towards empathic forums, while the effect of social influence and facilitating conditions was considered insignificant; social influence had a positive influence on the students’ behavioral intention to use emphatic forums, while attitude toward technology, performance expectancy, facilitating conditions and effort expectancy were considered not relevant.

The focus of this study was the influence of attitude toward empathic forums, used for mobile and distance learning, and the results establish the grounds for future research on attitude as an important determinant of technology acceptance.

In visual-based applications, lighting conditions have a considerable impact on quality of the acquired images. Extremely low or high illuminated environments are a real issue for a majority of cameras due to limitations in their dynamic range. Indeed, over or under exposure might result in loss of essential information because of pixel saturation or noise. This can be critical in computer vision applications. High dynamic range (HDR) imaging technology is known to improve image rendering in such conditions. The purpose of this paper is to investigate the level of performance that can be achieved for feature detection and tracking operations in images acquired with a HDR image sensor.

In this study, four different feature detection techniques are selected and tracking algorithm is based on the pyramidal implementation of Kanade-Lucas-Tomasi (KLT) feature tracker. Tracking algorithm is run over image sequences acquired with a HDR image sensor and with a high resolution 5 Megapixel image sensor to comparatively assess them.

The authors demonstrate that tracking performance is greatly improved on image sequences acquired with HDR sensor. Number and percentage of finally tracked features are several times higher than what can be achieved with a 5 Megapixel image sensor.

The specific interest of this work focuses on the evaluation of tracking persistence of a set of initial detected features over image sequences taken in different scenes. This includes extreme illumination indoor and outdoor environments subject to direct sunlight exposure, backlighting, as well as dim light and dark scenarios.

This paper aims to propose a set of six-axis robot arm welding seam tracking experiment platform based on Halcon machine vision library to resolve the curve seam tracking issue.

Robot-based and image coordinate systems are converted based on the mathematical model of the three-dimensional measurement of structured light vision and conversion relations between robot-based and camera coordinate systems. An object tracking algorithm via weighted local cosine similarity is adopted to detect the seam feature points to prevent effectively the interference from arc and spatter. This algorithm models the target state variable and corresponding observation vector within the Bayes framework and finds the optimal region with highest similarity to the image-selected modules using cosine similarity.

The paper tests the approach and the experimental results show that using metal inert-gas (MIG) welding with maximum welding current of 200A can achieve real-time accurate curve seam tracking under strong arc light and splash. Minimal distance between laser stripe and welding molten pool can reach 15 mm, and sensor sampling frequency can reach 50 Hz.

Designing a set of six-axis robot arm welding seam tracking experiment platform with a system of structured light sensor based on Halcon machine vision library; and adding an object tracking algorithm to seam tracking system to detect image feature points. By this technology, this system can track the curve seam while welding.

This paper aims to develop a method to achieve automatic robotic welding and seam tracking so that three‐dimensional weld seam could be tracked without teaching and good welding formation could be accomplished.

Adaptive image processing method was used for various types of weld seam. Also the relationship between welding height and arc signal was calibrated. Through the decomposition and synthesis, three‐dimensional space type weld seam could be extracted and tracked well. The workpiece without teaching was finally tracked precisely and in a timely way with use of the fuzzy controller.

Composite sensing technology including arc and visual sensing had obvious advantages. Image processing method could be used for tracking plane weld seam efficiently while arc sensing could characterize welding height. Through the coupled controlling algorithm, arc sensing and visual sensing could be fused effectively.

How to couple information more accurately and quickly was still one of the most important problems in composite sensing technology.

Composite sensing technology could reduce costs to achieve weld seam instead such expensive device as laser sensor. The simulating parts of scalloped segment of bottom board for rockets were tracked in the project. Once more adaptive algorithms were developed, more complicated practical workpieces could be dealt with in robotic welding which promotes the application of industry robots.

A useful method for three‐dimensional space type weld seam tracking without teaching was developed. The whole procedure of adaptive image processing method was simple but efficient and robust. The coupled controlling strategy addressed could accomplish seam tracking by composite sensing technology.

The purpose of this paper is to analyze the technology of capturing and processing weld images in real‐time, which is very important to the seam tracking and the weld quality control during the robotic gas tungsten arc welding (GTAW) process.

By analyzing some main parameters on the effect of image capturing, a passive vision sensor for welding robot was designed in order to capture clear and steady welding images. Based on the analysis of the characteristic of the welding images, a new improved Canny algorithm was proposed to detect the edges of seam and pool, and extract the seam and pool characteristic parameters. Finally, the image processing precision was verified by the random welding experiments.

It was found that the seam and pool images can be clearly acquired by using the passive vision system, and the welding image characteristic parameters were accurately extracted through processing. The experiment results show that the precision range of the image processing can be controlled about within ±0.169 mm, which can completely meet the requirement of real‐time seam tracking for welding robot.

This system will be applied to the industrial welding robot production during the GTAW process.

It is very important for the type of teaching‐playback robots with the passive vision that the real‐time images of seam and pool are acquired clearly and processed accurately during the robotic welding process, which helps determine follow‐up seam track and the control of welding quality.

Aims to develop a robotic platform to autonomously track a moving object

This robotic platform, based on a modular system known as SafeBot, uses two sensors: a visual CCD camera and a laser‐based range sensor. The rigidly mounted camera tracks an object in front of the platform and generates appropriate drive commands to keep the object in view, even if the object itself moves. The range sensor detects other objects as the platform moves to provide real‐time obstacle avoidance while continuously tracking the original object.

The current approach successfully tracks an object, particularly a human subject, and avoids reasonably sized obstacles, but on‐board processing limitations restrict the speed of the object to approximately 5 km/h.

The core technology – a moving object tracked by a mobile robot with real‐time obstacle avoidance – is an integrated system comprising object tracking on a mobile platform and real‐time obstacle avoidance with robotic control. This system is applicable to a variety of automated applications such as inventory management, industrial palette distribution, and intruder surveillance.

As a means of data acquisition for the situation awareness, computer vision-based motion capture technologies have increased the potential to observe and assess manual activities for the prevention of accidents and injuries in construction. This study thus aims to present a computationally efficient and robust method of human motion data capture for the on-site motion sensing and analysis.

This study investigated a tracking approach to three-dimensional (3D) human skeleton extraction from stereo video streams. Instead of detecting body joints on each image, the proposed method tracks locations of the body joints over all the successive frames by learning from the initialized body posture. The corresponding body joints to the ones tracked are then identified and matched on the image sequences from the other lens and reconstructed in a 3D space through triangulation to build 3D skeleton models. For validation, a lab test is conducted to evaluate the accuracy and working ranges of the proposed method, respectively.

Results of the test reveal that the tracking approach produces accurate outcomes at a distance, with nearly real-time computational processing, and can be potentially used for site data collection. Thus, the proposed approach has a potential for various field analyses for construction workers’ safety and ergonomics.

Recently, motion capture technologies have rapidly been developed and studied in construction. However, existing sensing technologies are not yet readily applicable to construction environments. This study explores two smartphones as stereo cameras as a potentially suitable means of data collection in construction for the less operational constrains (e.g. no on-body sensor required, less sensitivity to sunlight, and flexible ranges of operations).