Scheme for deterministic Bell-state-measurement-free quantum teleportation

A deterministic teleportation scheme for unknown atomic states is proposed in cavity QED. The Bell state measurement is not needed in the teleportation process, and the success probability can reach 1.0. In addition, the current scheme is insensitive to the cavity decay and thermal field.Comment: 3 pages, no figur

A Survey of Positioning Systems Using Visible LED Lights

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe

A Stacked Multi-Granularity Convolution Denoising Auto-Encoder

With the development of big data, artificial intelligence has provided many intelligent solutions to urban life. For instance, an image-based intelligent technology, such as image classification of diseases, is widely used in daily life. However, the image in real life is mostly unlabeled, so the performance of many image-based intelligent models shows limitations. Therefore, how to use a large amount of unlabeled image data to build an efficient and high-quality model for better urban life has been an urgent research topic. In this paper, we propose an unsupervised image feature extraction method that is referred to as a stacked multi-granularity convolution denoising auto-encoder (SMGCDAE). The algorithm is based on a convolutional neural network (CNN), yet it introduces a multi-granularity kernel. This approach resolved issues with image unicity by extracting a diverse category of high-level features. In addition, the denoising auto-encoder ensures stability and improves the classification accuracy by extracting more robust features. The algorithm was assessed using three image benchmark datasets and a series of meningitis images, achieving higher average accuracy than other methods. These results suggest that the algorithm is capable of extracting more discriminative high-level features and thus offers superior performance compared with the existing methodologies

A Novel Optimal Mapping Algorithm With Less Computational Complexity for Virtual Network Embedding

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Network Virtualization (NV) is widely accepted as one enabling technology for future network, which enables multiple Virtual Networks (VNs) with different paradigms and protocols to coexist on the shared Substrate Network (SN). One key challenge in network virtualization is Virtual Network Embedding (VNE), which maps a virtual network onto the shared SN. Since VNE is NP-hard, existing efforts mainly focus on proposing heuristic algorithms that try to achieve feasible VN embedding in reasonable time, consequently the resulted embedding is not optimal. To tackle this difficulty, we propose a candidate assisted (CAN-A) optimal VNE algorithm with lower computational complexity. The key idea of the CAN-A algorithm lies in constructing the candidate substrate node subset and the candidate substrate path subset before embedding. This reduces the mapping execution time substantially without performance loss. In the following embedding, four types of node and link constraints are considered in the CAN-A algorithm, making it more applicable to realistic networks. Simulation results show that the execution time of CAN-A is hugely cut down compared with pure VNE-MIP algorithm. CAN-A also outperforms the typical heuristic algorithms in terms of other performance indices, such as the average virtual network request (VNR) acceptance ratio and the average virtual link propagation delay

MRM-Lasso: A Sparse Multiview Feature Selection Method via Low-Rank Analysis

© 2015 IEEE. Learning about multiview data involves many applications, such as video understanding, image classification, and social media. However, when the data dimension increases dramatically, it is important but very challenging to remove redundant features in multiview feature selection. In this paper, we propose a novel feature selection algorithm, multiview rank minimization-based Lasso (MRM-Lasso), which jointly utilizes Lasso for sparse feature selection and rank minimization for learning relevant patterns across views. Instead of simply integrating multiple Lasso from view level, we focus on the performance of sample-level (sample significance) and introduce pattern-specific weights into MRM-Lasso. The weights are utilized to measure the contribution of each sample to the labels in the current view. In addition, the latent correlation across different views is successfully captured by learning a low-rank matrix consisting of pattern-specific weights. The alternating direction method of multipliers is applied to optimize the proposed MRM-Lasso. Experiments on four real-life data sets show that features selected by MRM-Lasso have better multiview classification performance than the baselines. Moreover, pattern-specific weights are demonstrated to be significant for learning about multiview data, compared with view-specific weights

Broadband RCS Reduction of Microstrip Patch Antenna Using Bandstop Frequency Selective Surface

In this article, a simple and effective approach is presented to reduce the Radar Cross Section (RCS) of microstrip patch antenna in ultra broad frequency band. This approach substitutes a metallic ground plane of a conventional patch antenna with a hybrid ground consisting of bandstop Frequency Selective Surface (FSS) cells with partial metallic plane. To demonstrate the validity of the proposed approach, the influence of different ground planes on antenna’s performance is investigated. Thus, a patch antenna with miniaturized FSS cells is proposed. The results suggest that this antenna shows 3dB RCS reduction almost in the whole out-of operating band within 1-20GHz for wide incident angles when compared to conventional antenna, while its radiation characteristics are sustained simultaneously. The reasonable agreement between the measured and the simulated results verifies the efficiency of the proposed approach. Moreover, this approach doesn’t alter the lightweight, low-profile, easy conformal and easy manufacturing nature of the original antenna and can be extended to obtain low-RCS antennas with metallic planes in broadband that are quite suitable for the applications which are sensitive to the variation of frequencies

An efficient flow control algorithm for multi-rate multicast networks

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Prehospital delay for acute coronary syndrome in China

Background: Despite the effectiveness of timely reperfusion therapy for acute coronary syndrome, prehospital delay remains a global concern. Objective: This article assesses the state-of-the-science regarding prehospital delay for acute coronary syndrome in China. Method: Electronic databases and hand searching were undertaken using key words such as prehospital delay, care-seeking delay, coronary heart disease, heart disease, acute coronary syndrome, unstable angina pain, acute myocardial infarction, cardiovascular disease, chest pain, and Chin (China/Chinese). The Chinese search was supervised by a Chinese health librarian. Results: Based on the search criteria, 28 studies were identified and reviewed using a standardized data extraction tool. Older age, attribution of symptoms to noncardiac causes, lack of health insurance coverage, poor access to transportation, and female sex were identified as contributing to prehospital delay. Conclusion: Health system reforms in China are necessary, particularly with regard to addressing the needs of older people, women, and other vulnerable populations in the context of the rising number of people with coronary heart disease. Developing targeted strategies, learned from both national and international experience, are required to develop targeted interventions. Copyright © 2010 Wolters Kluwer Health. Lippincott Williams & Wilkins