Machine Vision based Grabbing Objects with Manipulator System Design

In recent years, machine vision technology and robot control technology have attracted lots attention of the researchers. They provide people with fast and efficient services in many fields, which have an increasingly important impact on the modern manufacturing industry and the inspection industry. In this paper, a mechanical vision-based grab control system based on machine vision is developed and analyzed accordingly. This design employs industrial cameras with Gigabit Ethernet ports, six-degree-of-freedom servo drive robots. The Host computer control software is designed on the development platform provided by Microsoft and processed in machine vision image processing. The software has implemented an image processing algorithm. It aims to combine machine vision, robot control and other technologies to achieve precise positioning, recognition and capture of targets. In the end, the proposed method is displayed in the upper computer accordingly

Pulmonary Capillary Hemorrhage Induced by Acoustic Radiation Force Impulse Shear Wave Elastography in Ventilated Rats

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151830/1/jum14950.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151830/2/jum14950_am.pd

Reconstruction of tokamak plasma safety factor profile using deep learning

In tokamak operations, accurate equilibrium reconstruction is essential for reliable real-time control and realistic post-shot instability analysis. The safety factor (q) profile defines the magnetic field line pitch angle, which is the central element in equilibrium reconstruction. The motional Stark effect (MSE) diagnostic has been a standard measurement for the magnetic field line pitch angle in tokamaks that are equipped with neutral beams. However, the MSE data are not always available due to experimental constraints, especially in future devices without neutral beams. Here we develop a deep learning-based surrogate model of the gyrokinetic toroidal code for q profile reconstruction (SGTC-QR) that can reconstruct the q profile with the measurements without MSE to mimic the traditional equilibrium reconstruction with the MSE constraint. The model demonstrates promising performance, and the sub-millisecond inference time is compatible with the real-time plasma control system

Azorhizobium caulinodans c-di-GMP phosphodiesterase Chp1 involved in motility, EPS production, and nodulation of the host plant

Establishment of the rhizobia-legume symbiosis is usually accompanied by hydrogen peroxide (H2O2) production by the legume host at the site of infection, a process detrimental to rhizobia. In Azorhizobium caulinodans ORS571, deletion of chp1, a gene encoding c-di-GMP phosphodiesterase, led to increased resistance against H2O2 and to elevated nodulation efficiency on its legume host Sesbania rostrata. Three domains were identified in the Chp1: a PAS domain, a degenerate GGDEF domain, and an EAL domain. An in vitro enzymatic activity assay showed that the degenerate GGDEF domain of Chp1 did not have diguanylate cyclase activity. The phosphodiesterase activity of Chp1 was attributed to its EAL domain which could hydrolyse c-di-GMP into pGpG. The PAS domain functioned as a regulatory domain by sensing oxygen. Deletion of Chp1 resulted in increased intracellular c-di-GMP level, decreased motility, increased aggregation, and increased EPS (extracellular polysaccharide) production. H2O2-sensitivity assay showed that increased EPS production could provide ORS571 with resistance against H2O2. Thus, the elevated nodulation efficiency of the increment chp1 mutant could be correlated with a protective role of EPS in the nodulation process. These data suggest that c-di-GMP may modulate the A. caulinodans-S. rostrata nodulation process by regulating the production of EPS which could protect rhizobia against H2O2

Xenograft Tumors Vascularized with Murine Blood Vessels May Overestimate the Effect of Anti-Tumor Drugs: A Pilot Study

Recent evidence demonstrated that endothelial cells initiate signaling events that enhance tumor cell survival, proliferation, invasion, and tumor recurrence. Under this new paradigm for cellular crosstalk within the tumor microenvironment, the origin of endothelial cells and tumor cells may have a direct impact on the pathobiology of cancer. The purpose of this pilot study was to evaluate the effect of endothelial cell species (i.e. murine or human) on xenograft tumor growth and response to therapy. Tumor xenografts vascularized either with human or with murine microvascular endothelial cells were engineered, side-by-side, subcutaneously in the dorsum of immunodefficient mice. When tumors reached 200 mm3, mice were treated for 30 days with either 4 mg/kg cisplatin (i.p.) every 5 days or with 40 mg/kg sunitinib (p.o.) daily. Xenograft human tumors vascularized with human endothelial cells grow faster than xenograft tumors vascularized with mouse endothelial cells (P\u3c0.05). Notably, human tumors vascularized with human endothelial cells exhibited nuclear translocation of p65 (indicative of high NF-kB activity), and were more resistant to treatment with cisplatin or sunitinib than the contralateral tumors vascularized with murine endothelial cells (P\u3c0.05). Collectively, these studies suggest that the species of endothelial cells has a direct impact on xenograft tumor growth and response to treatment with the chemotherapeutic drug cisplatin or with the anti-angiogenic drug sunitinib

Rethinking the Reference-based Distinctive Image Captioning

Distinctive Image Captioning (DIC) -- generating distinctive captions that describe the unique details of a target image -- has received considerable attention over the last few years. A recent DIC work proposes to generate distinctive captions by comparing the target image with a set of semantic-similar reference images, i.e., reference-based DIC (Ref-DIC). It aims to make the generated captions can tell apart the target and reference images. Unfortunately, reference images used by existing Ref-DIC works are easy to distinguish: these reference images only resemble the target image at scene-level and have few common objects, such that a Ref-DIC model can trivially generate distinctive captions even without considering the reference images. To ensure Ref-DIC models really perceive the unique objects (or attributes) in target images, we first propose two new Ref-DIC benchmarks. Specifically, we design a two-stage matching mechanism, which strictly controls the similarity between the target and reference images at object-/attribute- level (vs. scene-level). Secondly, to generate distinctive captions, we develop a strong Transformer-based Ref-DIC baseline, dubbed as TransDIC. It not only extracts visual features from the target image, but also encodes the differences between objects in the target and reference images. Finally, for more trustworthy benchmarking, we propose a new evaluation metric named DisCIDEr for Ref-DIC, which evaluates both the accuracy and distinctiveness of the generated captions. Experimental results demonstrate that our TransDIC can generate distinctive captions. Besides, it outperforms several state-of-the-art models on the two new benchmarks over different metrics.Comment: ACM MM 202