Sand Casting Implementation of Two-Dimensional Digital Code Direct-Part-Marking Using Additively Manufactured Tags

Statistical quality control is used in foundries to identify special cause defects and root causes by correlating process input variations with casting defects. A difficulty exists in associating process data collected with individual cast parts as the parts are processed through the foundry and then out into the supply chain. Typically, alphanumeric labels for marking castings and manual identification of the castings with route-paper based tracing approaches have been used. Such manual-based systems make root cause analysis of quality defect issues tedious. This study presents the development of a semi-automated approach using 3D printed sand mold inserts shaped as 2D matrix codes which thereby permit directly cast identification code into the parts. This enables automated part tracking at the very beginning of the casting process including mold making. Automated scan based tracking of parts through a foundry and subsequent supply chain allows for statistical process data collected to also be associated with each part processed with unique identification, building upon the part history and pedigree.

Particle Shape Impact on the Radiative Forcing Efficiency Estimated from Single Levitated (NH4)2SO4 Particles

Levitation of single trapped particles enables the exploration of fundamental physicochemical aerosol properties never previously achieved. Experimental measurements showed that (NH4)2SO4’s particle shape deviated from sphericity during the crystallization process. Despite that, salt aerosols are assumed to be spheres even in low relative humidity (RH) in most climate models. In the analysis performed here, Mie and T-Matrix codes were operated to simulate crucial parameters needed to estimate the radiative forcing efficiency: extinction efficiency, asymmetry parameter and backscattering fraction. The incorporation of non-spherical effects in (NH4)2SO4 particles can cause a difference of up to 46% radiative forcing efficiency compared to the assumption of sphericity in the 0.3–0.6 µm particle radius range.

Comparative Study of Data Matrix Codes Localization and Recognition Methods

We provide a comprehensive and in-depth overview of the various approaches applicable to the recognition of Data Matrix codes in arbitrary images. All presented methods use the typical “L” shaped Finder Pattern to locate the Data Matrix code in the image. Well-known image processing techniques such as edge detection, adaptive thresholding, or connected component labeling are used to identify the Finder Pattern. The recognition rate of the compared methods was tested on a set of images with Data Matrix codes, which is published together with the article. The experimental results show that methods based on adaptive thresholding achieved a better recognition rate than methods based on edge detection.

Studying and Modeling the Performance of the TCM-STBC Systems in the Rayleigh Channel

Multiple-input multiple-output (MIMO) systems will play an important role in future generations of wireless networks. Space-time block code (STBC) and space-time trellis code (STTC) are two techniques that may be used in multi-antenna radio systems. This paper aims, most importantly, to study the performance of STBC systems at different vallues of such parameters as spectral efficiency, matrix codes and constellations. A performance comparison between STBC and STTC schemes is performed. In order to show the efficiency of the system’s ability to communicate with uncoded and coded transmission structures over AWGN and Rayleigh channels, the trellis-coded modulation (TCM) is introduced. The results obtained show that the proposed TCM-STBC system model, using one and two receiving antennas, improves the performance of Rayleigh channel communication systems at 9.5 dB and 11.5 dB for a BER of 10−5

Integrating real-time data analysis into automatic tracking of social insects

Automatic video tracking has become a standard tool for investigating the social behaviour of insects. The recent integration of computer vision in tracking technologies will probably lead to fully automated behavioural pattern classification within the next few years. However, many current systems rely on offline data analysis and use computationally expensive techniques to track pre-recorded videos. To address this gap, we developed BACH (Behaviour Analysis maCHine), a software that performs video tracking of insect groups in real time. BACH uses object recognition via convolutional neural networks and identifies individually tagged insects via an existing matrix code recognition algorithm. We compared the tracking performances of BACH and a human observer (HO) across a series of short videos of ants moving in a two-dimensional arena. We found that BACH detected ant shapes only slightly worse than the HO. However, its matrix code-mediated identification of individual ants only attained human-comparable levels when ants moved relatively slowly, and fell when ants walked relatively fast. This happened because BACH had a relatively low efficiency in detecting matrix codes in blurry images of ants walking at high speeds. BACH needs to undergo hardware and software adjustments to overcome its present limits. Nevertheless, our study emphasizes the possibility of, and the need for, further integrating real-time data analysis into the study of animal behaviour. This will accelerate data generation, visualization and sharing, opening possibilities for conducting fully remote collaborative experiments.