Markerless Image Alignment Method for Pressure-Sensitive Paint Image

We propose a markerless image alignment method for pressure-sensitive paint measurement data replacing the time-consuming conventional alignment method in which the black markers are placed on the model and are detected manually. In the proposed method, feature points are detected by a boundary detection method, in which the PSP boundary is detected using the Moore-Neighbor tracing algorithm. The performance of the proposed method is compared with the conventional method based on black markers, the difference of Gaussian (DoG) detector, and the Hessian corner detector. The results by the proposed method and the DoG detector are equivalent to each other. On the other hand, the performances of the image alignment using the black marker and the Hessian corner detector are slightly worse compared with the DoG and the proposed method. The computational cost of the proposed method is half of that of the DoG method. The proposed method is a promising for the image alignment in the PSP application in the viewpoint of the alignment precision and computational cost.

Sweep-tracing algorithm: in silico slip crystallography and tension-compression asymmetry in BCC metals

Direct Molecular Dynamics (MD) simulations are being increasingly employed to model dislocation-mediated crystal plasticity with atomic resolution. Thanks to the dislocation extraction algorithm (DXA), dislocation lines can be now accurately detected and positioned in space and their Burgers vector unambiguously identified in silico, while the simulation is being performed. However, DXA extracts static snapshots of dislocation configurations that by themselves present no information on dislocation motion. Referred to as a sweep-tracing algorithm (STA), here we introduce a practical computational method to observe dislocation motion and to accurately quantify its important characteristics such as preferential slip planes (slip crystallography). STA reconnects pairs of successive snapshots extracted by DXA and computes elementary slip facets thus precisely tracing the motion of dislocation segments from one snapshot to the next. As a testbed for our new method, we apply STA to the analysis of dislocation motion in large-scale MD simulations of single crystal plasticity in BCC metals. We observe that, when the crystal is subjected to uniaxial deformation along its [001] axis, dislocation slip predominantly occurs on the {112} maximum resolved shear stress plane under tension, while in compression slip is non-crystallographic (pencil) resulting in asymmetric mechanical response. The marked contrast in the observed slip crystallography is attributed to the twinning/anti-twinning asymmetry of shears in the {112} planes relatively favoring dislocation motion in the twinning sense while hindering dislocations from moving in the anti-twinning directions.

Discrete Environment-Driven GPU-Based Ray Launching: Validation and Applications

In this work, the Discrete, Environment-Driven Ray Launching (DED-RL) algorithm, which makes use of parallelization on Graphic Processing Units, fully described in a previous paper, has been validated versus a large set of measurements to evaluate its performance in terms of both computational efficiency and accuracy. Three major urban areas have been considered, including a very challenging scenario in central San Francisco that was used as a benchmark to test an image-ray tracing algorithm in a previous work. Results show that DED-RL is as accurate as ray tracing, despite the much lower computation time, reduced by more than three orders of magnitude with respect to ray tracing. Moreover, the accuracy level only marginally depends on discretization pixel size, at least for the considered pixel size range. The unprecedented computational efficiency of DED-RL opens the way to numerous applications, ranging from RF coverage optimization of drone-aided cellular networks to efficient fingerprinting localization applications, as briefly discussed in the paper.

Analyzing normal and disrupted leukemic stem cell adhesion to bone marrow stromal cells by single-molecule tracking nanoscopy

ABSTRACT Leukemic stem cells (LSCs) adhere to bone niches through adhesion molecules. These interactions, which are deeply reorganized in tumors, contribute to LSC resistance to chemotherapy and leukemia relapse. However, LSC adhesion mechanisms and potential therapeutic disruption using blocking antibodies remain largely unknown. Junctional adhesion molecule C (JAM-C, also known as JAM3) overexpression by LSCs correlates with increased leukemia severity, and thus constitutes a putative therapeutic target. Here, we took advantage of the ability of nanoscopy to detect single molecules with nanometric accuracy to characterize junctional adhesion molecule (JAM) dynamics at leuko-stromal contacts. Videonanoscopy trajectories were reconstructed using our dedicated multi-target tracing algorithm, pipelined with dual-color analyses (MTT2col). JAM-C expressed by LSCs engaged in transient interactions with JAM-B (also known as JAM2) expressed by stromal cells. JAM recruitment and colocalization at cell contacts were proportional to JAM-C level and reduced by a blocking anti-JAM-C antibody. MTT2col revealed, at single-molecule resolution, the ability of blocking antibodies to destabilize LSC binding to their niches, opening opportunities for disrupting LSC resistance mechanisms.

Emission source tracing based on bionic algorithm mobile sensors with artificial olfactory system

The leakage of hazardous chemicals and toxic volatile substances in the atmosphere may cause serious consequences such as explosion and poisoning. To identify the unknown leakage locations and gas compositions, a mobile robot system to trace the leak source in the outdoor was investigated. First, two bionic searching algorithms, Zigzag and Silkworm algorithms, were tested with outdoor experiments for locating the leak source. The results showed that the locating errors of these two algorithms were within 0.5 m in 10 by 20 m search space, but the failing ratio of Zigzag and Silkworm algorithm was still high (about 40–50%). Therefore, an improved tracing algorithm combining the Silkworm and Zigzag algorithm, called as zigzag–Silkworm algorithm, was proposed. Compared with Silkworm and Zigzag algorithms, zigzag–Silkworm algorithm had a higher success ratio of 80% in outdoor source tracing tests, and the searching efficiency was enhanced, the efficiency parameter L: L 0 has improved from 2.58 for Silkworm and 2.66 for Zigzag to 2.17 for zigzag–Silkworm. Then, in order to identify the composition of the leaked gases during the source tracing, an artificial olfaction system (AOS) based on the gas sensor array and support vector machine was set on the mobile robot. The test results in the source tracing experiments with ammonia and ethanol emissions indicated that the recognition accuracy of emission gases reached to 99% with AOS equipped on the robot. Therefore, the mobile robot system equipped with the zigzag–Silkworm algorithm and the AOS is feasible to trace the leakage source and identify the emission composition in the outdoor leakage event with good performance in efficiency and accuracy although some underlying problems still need to be addressed in future work.

C3VFC: A Method for Tracing and Quantification of Microglia in 3D Temporal Images

Automatic glia reconstruction is essential for the dynamic analysis of microglia motility and morphology, notably so in research on neurodegenerative diseases. In this paper, we propose an automatic 3D tracing algorithm called C3VFC that uses vector field convolution to find the critical points along the centerline of an object and trace paths that traverse back to the soma of every cell in an image. The solution provides detection and labeling of multiple cells in an image over time, leading to multi-object reconstruction. The reconstruction results can be used to extract bioinformatics from temporal data in different settings. The C3VFC reconstruction results found up to a 53% improvement on the next best performing state-of-the-art tracing method. C3VFC achieved the highest accuracy scores, in relation to the baseline results, in four of the five different measures: Entire structure average, the average bi-directional entire structure average, the different structure average, and the percentage of different structures.