Xi'an Jiaotong University

About: Xi'an Jiaotong University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Heat transfer & Dielectric. The organization has 85440 authors who have published 99682 publications receiving 1579683 citations. The organization is also known as: '''Xi'an Jiaotong University''' & Xi'an Jiao Tong University.

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

Abstract: Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224x224) input image. This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with another pooling strategy, "spatial pyramid pooling", to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. Pyramid pooling is also robust to object deformations. With these advantages, SPP-net should in general improve all CNN-based image classification methods. On the ImageNet 2012 dataset, we demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures despite their different designs. On the Pascal VOC 2007 and Caltech101 datasets, SPP-net achieves state-of-the-art classification results using a single full-image representation and no fine-tuning. The power of SPP-net is also significant in object detection. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. In processing test images, our method is 24-102x faster than the R-CNN method, while achieving better or comparable accuracy on Pascal VOC 2007. In ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014, our methods rank #2 in object detection and #3 in image classification among all 38 teams. This manuscript also introduces the improvement made for this competition.

Large Piezoelectric Effect in Pb-Free Ceramics

Abstract: We report a non-Pb piezoelectric ceramic system Ba(Ti(0.8)Zr(0.2))O(3)-(Ba(0.7)Ca(0.3))TiO(3) which shows a surprisingly high piezoelectric coefficient of d(33) approximately 620 pC/N at optimal composition. Its phase diagram shows a morphotropic phase boundary (MPB) starting from a tricritical triple point of a cubic paraelectric phase (C), ferroelectric rhombohedral (R), and tetragonal (T) phases. The high piezoelectricity of the MPB compositions stems from the composition proximity of the MPB to the tricritical triple point, which leads to a nearly vanishing polarization anisotropy and thus facilitates polarization rotation between 001T and 111R states. We predict that the single-crystal form of the MPB composition of the present system may reach a giant d(33) = 1500-2000 pC/N. Our work may provide a new recipe for designing highly piezoelectric materials (both Pb-free and Pb-containing) by searching MPBs starting from a TCP.

Channel Pruning for Accelerating Very Deep Neural Networks

Abstract: In this paper, we introduce a new channel pruning method to accelerate very deep convolutional neural networks. Given a trained CNN model, we propose an iterative two-step algorithm to effectively prune each layer, by a LASSO regression based channel selection and least square reconstruction. We further generalize this algorithm to multi-layer and multi-branch cases. Our method reduces the accumulated error and enhance the compatibility with various architectures. Our pruned VGG-16 achieves the state-of-the-art results by 5× speed-up along with only 0.3% increase of error. More importantly, our method is able to accelerate modern networks like ResNet, Xception and suffers only 1.4%, 1.0% accuracy loss under 2× speedup respectively, which is significant.

A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications

Abstract: Fog/edge computing has been proposed to be integrated with Internet of Things (IoT) to enable computing services devices deployed at network edge, aiming to improve the user’s experience and resilience of the services in case of failures. With the advantage of distributed architecture and close to end-users, fog/edge computing can provide faster response and greater quality of service for IoT applications. Thus, fog/edge computing-based IoT becomes future infrastructure on IoT development. To develop fog/edge computing-based IoT infrastructure, the architecture, enabling techniques, and issues related to IoT should be investigated first, and then the integration of fog/edge computing and IoT should be explored. To this end, this paper conducts a comprehensive overview of IoT with respect to system architecture, enabling technologies, security and privacy issues, and present the integration of fog/edge computing and IoT, and applications. Particularly, this paper first explores the relationship between cyber-physical systems and IoT, both of which play important roles in realizing an intelligent cyber-physical world. Then, existing architectures, enabling technologies, and security and privacy issues in IoT are presented to enhance the understanding of the state of the art IoT development. To investigate the fog/edge computing-based IoT, this paper also investigate the relationship between IoT and fog/edge computing, and discuss issues in fog/edge computing-based IoT. Finally, several applications, including the smart grid, smart transportation, and smart cities, are presented to demonstrate how fog/edge computing-based IoT to be implemented in real-world applications.

Efficient organic solar cells processed from hydrocarbon solvents

Abstract: Organic solar cells have desirable properties, including low cost of materials, high-throughput roll-to-roll production, mechanical flexibility and light weight. However, all top-performance devices are at present processed using halogenated solvents, which are environmentally hazardous and would thus require expensive mitigation to contain the hazards. Attempts to process organic solar cells from non-halogenated solvents lead to inferior performance. Overcoming this hurdle, here we present a hydrocarbon-based processing system that is not only more environmentally friendly but also yields cells with power conversion efficiencies of up to 11.7%. Our processing system incorporates the synergistic effects of a hydrocarbon solvent, a novel additive, a suitable choice of polymer side chain, and strong temperature-dependent aggregation of the donor polymer. Our results not only demonstrate a method of producing active layers of organic solar cells in an environmentally friendly way, but also provide important scientific insights that will facilitate further improvement of the morphology and performance of organic solar cells. The processing of high-performance organic solar cells usually requires environmentally hazardous solvents. Now, hydrocarbon-based processing is shown to achieve relatively high performance in a more environmentally friendly way.