What Can I Do Around Here? Deep Functional Scene Understanding for Cognitive Robots

For robots that have the capability to interact with the physical environment through their end effectors, understanding the surrounding scenes is not merely a task of image classification or object recognition. To perform actual tasks, it is critical for the robot to have a functional understanding of the visual scene. Here, we address the problem of localizing and recognition of functional areas from an arbitrary indoor scene, formulated as a two-stage deep learning based detection pipeline. A new scene functionality testing-bed, which is complied from two publicly available indoor scene datasets, is used for evaluation. Our method is evaluated quantitatively on the new dataset, demonstrating the ability to perform efficient recognition of functional areas from arbitrary indoor scenes. We also demonstrate that our detection model can be generalized onto novel indoor scenes by cross validating it with the images from two different datasets

Cardiovascular Magnetic Resonance Imaging: From Morphology to Function

Cardiovascular magnetic resonance imaging (CMRI) which combines high image quality with advanced techniques to probe cardiovascular system is developing rapidly. Also, as a noninvasive imaging equipment, it has been accepted widely in clinical application. CMRI techniques produce high spatial, contrast, and temporal resolution image data for evaluation of cardiac and great vessel anatomy, coronary artery imaging, regional tissue characterization, vascular blood flow, cardiac chamber filling and contraction, and myocardial perfusion, myocardial viability. This chapter will cover the basic techniques of CMRI, practical tricks of how to perform CMRI, and clinical application in a variety of congenital heart disease, coronary artery disease, and non-ischemic heart disease, etc

Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo Matching

Leveraging on the recent developments in convolutional neural networks (CNNs), matching dense correspondence from a stereo pair has been cast as a learning problem, with performance exceeding traditional approaches. However, it remains challenging to generate high-quality disparities for the inherently ill-posed regions. To tackle this problem, we propose a novel cascade CNN architecture composing of two stages. The first stage advances the recently proposed DispNet by equipping it with extra up-convolution modules, leading to disparity images with more details. The second stage explicitly rectifies the disparity initialized by the first stage; it couples with the first-stage and generates residual signals across multiple scales. The summation of the outputs from the two stages gives the final disparity. As opposed to directly learning the disparity at the second stage, we show that residual learning provides more effective refinement. Moreover, it also benefits the training of the overall cascade network. Experimentation shows that our cascade residual learning scheme provides state-of-the-art performance for matching stereo correspondence. By the time of the submission of this paper, our method ranks first in the KITTI 2015 stereo benchmark, surpassing the prior works by a noteworthy margin.Comment: Accepted at ICCVW 2017. The first two authors contributed equally to this pape

Novel Low-Permittivity (Mg 1− x

The effects of B2O3–LiF addition on the phase composition, microstructures, and microwave dielectric properties of (Mg0.95Cu0.05)2SiO4 ceramics fabricated by a wet chemical method were studied in detail. The B2O3–LiF was selected as liquid-phase sintering aids to reduce the densification sintering temperature of (Mg0.95Cu0.05)2SiO4 ceramics. The B2O3 6%–Li2O 6%-modified (Mg0.95Cu0.05)2SiO4 ceramics sintered at 1200°C possess good performance of εr ∼ 4.37, Q×f ∼ 36,700 GHz and τf ∼ −42 ppm/°C

A High Quality and Quantity Hybrid Perovskite Quantum Dots (CsPbX\u3csub\u3e3\u3c/sub\u3e, X= Cl, Br and I) Powders Synthesis via Ionic Displacement

Recently, all-inorganic perovskites CsPbX3 (X= Cl, Br and I) quantum dots (QDs) have drawn great attentions because of their PL spectra tunable over the whole visible spectral region (400-700 nm) and adjustable bandgap, which revealed a promising potential on the field of photoelectronic devices, such as solar cells, LEDs and sensors. In this paper, CsPbX3 QDs and hybrid QDs, CsPbClxBr3-x and CsPbBrxI3-x were synthesized via one-step and two-step methods comparably. The optical bandgaps of CsPbCl3, CsPbBr3, and CsPbI3, were calculated as 3.08, 2.36, and 1.73eV, respectively, based on the Tauc\u27s equation and UV absorption spectra. Ionic displacement and phase transformation occurred during the mixing process were found based on the monitoring of PL spectra and HRTEM characterization. The long-term stability, dried, high quality and two-dimensional hybrid CsPbBrxI3-x QDs powders could be achieved via the two-step method. Polar solution inductions were used to wash and purify the CsPbX3 QDs, which help obtain of various compositions and well crystallize all-inorganic perovskites QDs powders