Investigation on the Surface Plasmon Dispersion Engineering with TiN-Based Structures

In this thesis, we investigate the efficiency enhancement of surface plasmon (SP) coupling to the InGaN/ GaN QWs LED based on the strongly localized optical field and highly enhanced photon density of states near the SP frequency (ωsp) according to Purcell enhancement factor. Based on the Purcell effect, when the emission frequency approaches the surface plasmon frequency coupled to the active region, the energy coupled to the SP will notably increase and hence IQE will be strongly enhanced. In order to achieve the desirable long-wavelength emission and enhance the radiative efficiency for InGaN QWs LED, TiN and Au are selected as the appropriate materials for allowing the design of the surface plasmon frequency in the long-wavelength spectral regime (green-red). Such optimum design for engineering the surface plasmon frequency of the nano-metallic structures will result in enhanced radiation recombination rate from the active region. In this thesis, we investigate both single and double metallic layers structure to get the optimized model for Purcell enhancement in long-wavelength range. The effect of the metallic layer thicknesses has been exhibited in the computational studies. The Au and TiN single-layer structures can achieve the strong Purcell factor of ~1000 times and ~550 times in green and amber regime, respectively. The tunability of the SP frequency can be achieved by using the TiN/Au double-metallic layer structures for achieving optimized design to cover the peak Purcell factor ~ 500 times across the range of the surface plasmon frequencies of Au and TiN. The effect of different spacer separation between the InGaN QWs and metallic layer is also investigated. The variation of the spacer thickness affects the coupling efficiency, and this increased thickness also reduces the Purcell enhancement factor and decreases the surface plasmon frequency

A brief review of neural networks based learning and control and their applications for robots

As an imitation of the biological nervous systems, neural networks (NN), which are characterized with powerful learning ability, have been employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification and patterns recognition etc. This article aims to bring a brief review of the state-of-art NN for the complex nonlinear systems. Recent progresses of NNs in both theoretical developments and practical applications are investigated and surveyed. Specifically, NN based robot learning and control applications were further reviewed, including NN based robot manipulator control, NN based human robot interaction and NN based behavior recognition and generation

t(3;11)(p11;p15) NUP98/POU1F1

Review on t(3;11)(p11;p15), with data on clinics, and the genes involved

CRAVES: Controlling Robotic Arm with a Vision-based Economic System

Training a robotic arm to accomplish real-world tasks has been attracting increasing attention in both academia and industry. This work discusses the role of computer vision algorithms in this field. We focus on low-cost arms on which no sensors are equipped and thus all decisions are made upon visual recognition, e.g., real-time 3D pose estimation. This requires annotating a lot of training data, which is not only time-consuming but also laborious. In this paper, we present an alternative solution, which uses a 3D model to create a large number of synthetic data, trains a vision model in this virtual domain, and applies it to real-world images after domain adaptation. To this end, we design a semi-supervised approach, which fully leverages the geometric constraints among keypoints. We apply an iterative algorithm for optimization. Without any annotations on real images, our algorithm generalizes well and produces satisfying results on 3D pose estimation, which is evaluated on two real-world datasets. We also construct a vision-based control system for task accomplishment, for which we train a reinforcement learning agent in a virtual environment and apply it to the real-world. Moreover, our approach, with merely a 3D model being required, has the potential to generalize to other types of multi-rigid-body dynamic systems.Comment: 10 pages, 6 figure

DVGG: Deep Variational Grasp Generation for Dextrous Manipulation

Grasping with anthropomorphic robotic hands involves much more hand-object interactions compared to parallel-jaw grippers. Modeling hand-object interactions is essential to the study of multi-finger hand dextrous manipulation. This work presents DVGG, an efficient grasp generation network that takes single-view observation as input and predicts high-quality grasp configurations for unknown objects. In general, our generative model consists of three components: 1) Point cloud completion for the target object based on the partial observation; 2) Diverse sets of grasps generation given the complete point cloud; 3) Iterative grasp pose refinement for physically plausible grasp optimization. To train our model, we build a large-scale grasping dataset that contains about 300 common object models with 1.5M annotated grasps in simulation. Experiments in simulation show that our model can predict robust grasp poses with a wide variety and high success rate. Real robot platform experiments demonstrate that the model trained on our dataset performs well in the real world. Remarkably, our method achieves a grasp success rate of 70.7\% for novel objects in the real robot platform, which is a significant improvement over the baseline methods.Comment: Accepted by Robotics and Automation Letters (RA-L, 2021

Anti-thrombotic effect of combination of low molecular heparin and Xueshuantong after replantation of amputated finger

Purpose: To study the effects of low molecular heparin combined with Xueshuantong in preventing thrombosis after replantation of amputated finger.Methods: The treatment group (38 patients) was given 4500 IU of low molecular heparin sodium i.h. (hypodermic injection), q.d.(once daily), with 200 mL of 150 mg Xueshuantong injection and 5 % glucose injection, i.v.d. (intravenous drip), b.i.d. (twice daily). The control group received low molecular heparin sodium at 4500 IU i.h., q.d. alone. Treatment was for 3 days. Thereafter, D-dimer, fibrinogen, hemoglobin, platelets, prothrombin time (PT) and blood coagulation of patients in the 2 groups before and after treatment were compared. Differences in vasospasm, vascular thrombosis, finger necrosis, therapeutic effects and adverse reactions in patients in the 2 groups after treatment were recorded.Results: There were significant improvements in fibrinogen, platelet, PT levels, and blood coagulation time after treatment, with improvements better in the treatment group than in the control group (p < 0.05). Vasospasm cases (3) were lower in the treatment group than in the control group (8, p < 0.05), while vascular thrombosis and finger necrosis in both groups were comparable. Therapeutic effects and recovery were better in the treatment group than in the control group (p < 0.05).Conclusion: Combined injection of Xueshuantong and low molecular heparin exerts antithrombotic effects after replantation of amputated finger, improves coagulation function, and reduces incidence of vasospasm. It has better therapeutic effects than low molecular heparin, and it seems safe.Keywords: Xueshuantong, Low molecular heparin, Replantation, Amputated finger, Thrombosi

Modelling hysteresis in the transport of eroded sediment

Sediment transport hysteresis refers to the different sediment fluxes that can occur for the same discharge. For a single rainfall event, the overland flow hydrograph has rising and falling limbs, for which different hysteresis loops have been observed: (i) clockwise, (ii) anti-clockwise and (iii) figure 8 with both flow orientations. It has been suggested that the shape of these loops can be used to identify the different processes of runoff and sediment transport and the sediment source area. We present simulations carried out using the Hairsine-Rose (HR) soil erosion model that reproduce all of the above hysteresis loops for flow conditions that are straightforward to establish in a laboratory soil-erosion flume Based on the HR model, it is possible to explain the causes of the various types of hysteresis loops, in particular the role of the particle size distribution and the deposited layer of previously eroded sediment. Both of these aspects of the HR model, which are not typically included in commonly used erosion models, are crucial to produce these loops. Furthermore, we found that more involved hysteresis patterns do not depend on complicated rainfall distributions. Instead, spatial distributions of deposited sediment from a previous erosion event play a dominant role in determining the overall form and shape of the loop