Using Deep Learning Technology to Realize the Automatic Control Program of Robot Arm Based on Hand Gesture Recognition

In this study, the robot arm control, computer vision, and deep learning technologies are combined to realize an automatic control program. There are three functional modules in this program, i.e., the hand gesture recognition module, the robot arm control module, and the communication module. The hand gesture recognition module records the user’s hand gesture images to recognize the gestures’ features using the YOLOv4 algorithm. The recognition results are transmitted to the robot arm control module by the communication module. Finally, the received hand gesture commands are analyzed and executed by the robot arm control module. With the proposed program, engineers can interact with the robot arm through hand gestures, teach the robot arm to record the trajectory by simple hand movements, and call different scripts to satisfy robot motion requirements in the actual production environment

Discovery of gamma-ray emission from a strongly lobe-dominated quasar 3C 275.1

We systematically analyze the 6-year {\it Fermi}/LAT data of the lobe-dominated quasars (LDQs) in the complete LDQ sample from 3CRR survey and report the discovery of high-energy $\gamma$-ray emission from 3C 275.1. The $\gamma$-ray emission of 3C 207 is confirmed and significant variability of the lightcurve is identified. We do not find statistically significant $\gamma$-ray emission from other LDQs. 3C 275.1 is the known $\gamma$-ray quasar with the lowest core dominance parameter (i.e., $R=0.11$). We also show that both the northern radio hotspot and parsec jet models can reasonably reproduce the $\gamma$-ray data. The parsec jet model, however, is favored by the potential $\gamma$-ray variability at the timescale of months. We suggest that some dimmer $\gamma$-ray LDQs will be detected in the future and LDQs could contribute non-negligibly to the extragalactic $\gamma$-ray background.Comment: 26 pages, 10 figures, 3 tables; ApJ in pres

Hyperspectral Image Analysis for Mechanical and Chemical Properties of Concrete and Steel Surfaces

According to the 2017 ASCE Report Card, 39% and 15% of 614,387 bridges in the U.S. are more than 50 years and 40 to 49 years, respectively. The number of deficient bridges is increasing. One of the most common causes of reinforced concrete deterioration is corrosion of steel reinforcing bars. Currently, most bridges are visually inspected every two years using boom/snooper trucks to get access to various areas to be inspected. The subjective visual inspection often leads to inconsistent results that are less useful in bridge management. Hyperspectral camera, installed on an unmanned aerial vehicle, can potentially supplement visual inspection with quantifiable and reliable imagery from remote and safe operations. It can be used to identify physical characteristics (e.g., concrete cracks)and characterize chemical features (e.g., steel corrosion)

A machine vision based automatic optical inspection system for measuring drilling quality of printed circuit boards

In this paper, we develop and put into practice an automatic optical inspection (AOI) system based on machine vision to check the holes on a printed circuit board (PCB). We incorporate the hardware and software. For the hardware part, we combine a PC, the three-axis positioning system, a lighting device, and charge-coupled device cameras. For the software part, we utilize image registration, image segmentation, drill numbering, drill contrast, and defect displays to achieve this system. Results indicated that an accuracy of 5 mu m could be achieved in errors of the PCB holes allowing comparisons to be made. This is significant in inspecting the missing, the multi-hole, and the incorrect location of the holes. However, previous work only focuses on one or other feature of the holes. Our research is able to assess multiple features: missing holes, incorrectly located holes, and excessive holes. Equally, our results could be displayed as a bar chart and target plot. This has not been achieved before. These displays help users to analyze the causes of errors and immediately correct the problems. In addition, this AOI system is valuable for checking a large number of holes and finding out the defective ones on a PCB. Meanwhile, we apply a 0.1-mm image resolution, which is better than others used in industry. We set a detecting standard based on 2-mm diameter of circles to diagnose the quality of the holes within 10 s

Meta-analysis of nasopharyngeal carcinoma microarray data explores mechanism of EBV-regulated neoplastic transformation

<p>Abstract</p> <p>Background</p> <p>Epstein-Barr virus (EBV) presumably plays an important role in the pathogenesis of nasopharyngeal carcinoma (NPC), but the molecular mechanism of EBV-dependent neoplastic transformation is not well understood. The combination of bioinformatics with evidences from biological experiments paved a new way to gain more insights into the molecular mechanism of cancer.</p> <p>Results</p> <p>We profiled gene expression using a meta-analysis approach. Two sets of meta-genes were obtained. Meta-A genes were identified by finding those commonly activated/deactivated upon EBV infection/reactivation. These genes could be key players for pathways de-regulated by EBV during latent infection and lytic proliferation. Meta-B genes were obtained from differential genes commonly expressed in NPC and PEL (primary effusion lymphoma). We then integrated meta-A, meta-B and associated factors into an interaction network using acquired information. Our analysis suggests that NPC transformation depends on timely regulation of DEK, CDK inhibitor(s), p53, RB and several transcriptional cascades, interconnected by E2F, AP-1, NF-κB, STAT3 among others during latent and lytic cycles.</p> <p>Conclusion</p> <p>In conclusion, our meta-analysis strategy re-analyzed EBV-related tumor data sets and identified sets of meta-genes possibly involved in maintaining latent or switching to lytic cycles of EBV in NPC. The results of this analysis may shed new lights to further our understanding of the EBV-led neoplastic transformation.</p