The paper deals with the possibilities of using Data Matrix codes in production engineering. We designed and tested the computationally efficient method for locating the Data Matrix code in the images. The location search procedure consists of identification of candidate regions using image binarization, then joining adjacent points into continuous regions and also examining outer boundaries of the regions. Afterwards we verify the presence of the Finder Pattern (as two perpendicular line segments) and Timing Pattern (as alternating sequence of black and white modules) in these candidate regions. Such procedure is invariant to shift rotation and scale change of Data Matrix codes. The method we have proposed has been verified on a set of real industrial images and compared to other commercial algorithms. We are also convinced that such technique is also suitable for real-time processing and has achieved better results than comparable commercial algorithms.
Data Matrix codes can be a significant factor in increasing productivity and efficiency in production processes. An important point in deploying Data Matrix codes is their recognition and decoding. In this paper is presented a computationally efficient algorithm for locating Data Matrix codes in the images. Image areas that may contain the Data Matrix code are to be identified firstly. To identify these areas, the thresholding, connected components labelling and examining outer bounding-box of the continuous regions is used. Subsequently, to determine the boundaries of the Data Matrix code more precisely, we work with the difference of adjacent projections around the Finder Pattern. The dimensions of the Data Matrix code are determined by analyzing the local extremes around the Timing Pattern. We verified the proposed method on a testing set of synthetic and real scene images and compared it with the results of other open-source and commercial solutions. The proposed method has achieved better results than competitive commercial solutions.
One of the most frequently measured quantity is temperature, which is also one of the most important physical quantities. Temperature has influence on the almost all states and processes in the nature as well as in technique. A wide range of temperature sensors is currently available on the market. They use different measurement principles and exist in many designs. According to the location of the sensing element in the measured environment, they are divided into two main groups: contact and non-contact. Further, we can divide the temperature sensors according to the physical principle on which they work. The article deals with the analysis and comparison of selected Arduino-compatible contact temperature sensors. The temperature measurement of machine functional nodes and its diagnostics are part of maintenance and engineering diagnostics. At present, NC and CNC machine diagnostics are an important trend in machine condition monitoring and machine status prediction to maintain production quality. Machine status monitoring allows reducing of machine service costs as well as maintaining the high production quality.
Data matrix codes are two-dimensional (2D) matrix bar codes, which are the descendants of the well known 1D bar codes. However, compared to 1D bar codes, they allow to store much more information in the same area. Comparing data matrix codes with QR codes, for example, we find them much more effective in marking small objects or in the case that you have only a very small area for placing a code in. Their capacity and ability of decoding also a code that is partly damaged make them an appropriate solution for industrial applications. In the following paper we compare the impact of various cameras on the detection and decoding of data matrix codes in real scene images. The location of the code is based on the fact that typical bordering of a data matrix code forms a region of connected points which create “L”, the so-called finder pattern, and the parallel dotting, the so-called timing pattern. In the first step, we try to locate the finder pattern using adaptive thresholding and connecting neighbouring points to continuous regions. Then we search for the regions where 3 outer boundary points form a isosceles right triangle that could represent the finder pattern. In the second step, we have to verify the timing pattern. We look for an even number of crossings between the background and foreground. Experimental results show that the algorithm we have proposed provides better results than competitive solutions.