Resilient overlay network for real-time interactive multimedia sessions in corporate networks

Real-time interactive multimedia communications are becoming increasingly useful for education, business, e-commerce and e-government, providing an enriched user experience in teleconferencing, e-meetings, distance training and product demonstrations. Large corporations are usually located at several sites, so real-time multipoint sessions within corporations are especially difficult. IP multicast is available or feasible within each site of an organization. Thus, corporate networks can be considered as various multicast-capable networks interconnected through a wide area network without multicast connectivity. This paper proposes a resilient self-managed overlay network to support real-time multipoint interactive sessions within corporate networks. The proposed overlay takes advantage of the configuration of corporate networks to self-organize and provide an efficient media delivery service, making use of multicast communications wherever available. Various self-healing techniques are implemented allowing for the continuity of ongoing sessions in spite of network disruptions and entity failures. Extensive simulations and tests have been carried out to assess the performance and resilience of the overlay facing several types of disruptions.     

A Non-Invasive Technique for Online Defect Detection on Steel Strip Surfaces

During the production of steel strips, a large amount of surface defects can be generated, due to harsh environmental conditions. A high number of surface defects can lead to rejection by the customer, which represents significant economic losses to the production plant. Thus, it is very important to detect the presence and type of the defects generated during the production of each steel strip. Using this information, it is possible to determine whether a strip is suitable for sale, and it may also be useful to determine the origin of defects and, if possible, prevent them from being generated in subsequent strips. To perform these tasks, non-invasive inspection techniques are usually used, carried out automatically by artificial vision systems. Although the inspection conducted by humans is more accurate, they become fatigued quickly, or may even be unable to carry out the inspection correctly when the forward speed of the strip is high. In this paper, a new detection technique is proposed, based on the division of an image into a set of overlapping areas. The optimum values for the configuration parameters of the detection technique are automatically determined using a genetic algorithm. After the detection phase, all the defects are classified using a neural network. A very satisfactory success rate has been achieved in both detection and classification phases.     

An improved 3D imaging system for dimensional quality inspection of rolled products in the metal industry

Measurement, inspection and quality control in industry have benefited from 3D techniques for imaging and visualization in recent years. The development of machine vision devices at decreased costs, as well as their miniaturization and integration in industrial processes, have accelerated the use of 3D imaging systems in industry. In this paper we describe how to improve the performance of a 3D imaging system for inline dimensional quality inspection of long, flat-rolled metal products manufactured in rolling mills we designed and developed in previous works. Two dimensional characteristics of rolled products are measured by the system: width and flatness. The system is based on active triangulation using a single-line pattern projected onto the surface of the product under inspection for range image acquisition. Taking the system calibration into account the range images are transformed into a calibrated point cloud representing the 3D surface reconstruction of the product. Two approaches to improve the line detection and extraction method used in the original system are discussed, one intended for high-speed processing with lower accuracy, and the other providing high accuracy while incurring higher computational time expenses. A mechanism to remove, or at least reduce, the effects of product movements while manufacturing, such as bouncing and flapping, is also proposed to improve the performance of the system.     

Fast and robust laser stripe extraction for 3D reconstruction in industrial environments

The use of 3D reconstruction based on active laser triangulation techniques is very complex in industrial environments. The main problem is that most of these techniques are based on laser stripe extraction methods which are highly sensitive to noise, which is virtually inevitable in these conditions. In industrial environments, variable luminance, reflections which show up in the images as noise, and uneven surfaces are common. These factors modify the shape of the laser profile. This work proposes a fast, accurate, and robust method to extract laser stripes in industrial environments. Specific procedures are proposed to extract the laser stripe projected on the background, using a boundary linking process, and on the foreground, using an improved Split-and-Merge approach with different approximation functions including linear, quadratic, and Akima splines. Also, a novel procedure to automatically define the region of interest in the image is proposed. The real-time performance of the proposed method is analyzed by measuring the time taken by the tasks involved in their application. Finally, the proposed extraction method is applied to two real applications: 3D reconstruction of steel strips and weld seam tracking.     

Symbiotic human–robot collaborative approach for increased productivity and enhanced safety in the aerospace manufacturing industry

The International Journal of Advanced Manufacturing Technology - Robots are perfect substitutes for skilled workforce on some repeatable, general, and strategically important tasks, but this...     

Efficient registration of 2D points to CAD models for real-time applications

Efficient registration is a major challenge for real-time machine vision applications. Modern acquisition hardware can produce data at extremely high rates. Thus, efficient registration algorithms are required to align data to reference models to detect deviations and take correcting actions if needed. In this work, an efficient registration procedure of 2D points to CAD (computer-aided design) models is proposed. Recent developments in the field are reviewed and evaluated in terms of accuracy, speed and robustness. Efficient algorithms are proposed for the most computationally expensive parts of the registration, including an estimation of the rigid transform, a calculation of the closest point to geometric primitives, and an estimation of the surface normal. Furthermore, a novel primitive caching procedure is proposed that, when combined with an R-tree, greatly improves the execution speed of the registration. The result is a very accurate registration procedure, since geometric primitives are treated analytically with no point sampling required. At the same time, the proposed procedure is robust, very fast, and can achieve the correct registration in less than one millisecond.     

An efficient method for defect detection during the manufacturing of web materials

Defect detection is becoming an increasingly important task during the manufacturing process. The early detection of faults or defects and the removal of the elements that may produce them are essential to improve product quality and reduce the economic impact caused by discarding defective products. This point is especially important in the case of products that are very expensive to produce. In this paper, the authors propose a method to detect a specific type of defect that may occur during the production of web materials: periodical defects. This type of defect is very harmful, as it can generate many surface defects, greatly reducing the quality of the end product and, on occasions, making it unsuitable for sale. To run the proposed method, two different functions must be executed a large number of times. Since the time available to perform the detection of these defects may be limited, it is very important to consume the least amount of time possible. In order to reduce the overall time required for detection, an analysis of how the method accesses the input data is performed. Thus, the most efficient data structure to store the information is determined. At the end of the paper, several experiments are performed to verify that both the proposed method and the data structure used to store the information are the most suitable to solve the aforementioned problem.     

Real-Time Assessment of the Reliability of Welds in Steel Strips

Some steel manufacturing lines require a continuous infinite steel strip for their permanent operation. In order to fulfill this requirement, single steel strips are welded in the input section of these lines. However, welding becomes a critical process, since a possible failure could delay the normal operation of a manufacturing line for days, making a testing process by a technician necessary. This paper proposes a method to assess the reliability of welds in steel strips based on statistical information gathered from the welding variables. Using the reliability assessment, the technician can adapt the tests by reducing or even eliminating them.