3,576 research outputs found

    Energy-Efficient Design of STAR-RIS Aided MIMO-NOMA Networks

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    Simultaneous transmission and reflection-reconfigurable intelligent surface (STAR-RIS) can provide expanded coverage compared with the conventional reflection-only RIS. This paper exploits the energy efficient potential of STAR-RIS in a multiple-input and multiple-output (MIMO) enabled non-orthogonal multiple access (NOMA) system. Specifically, we mainly focus on energy-efficient resource allocation with MIMO technology in the STAR-RIS assisted NOMA network. To maximize the system energy efficiency, we propose an algorithm to optimize the transmit beamforming and the phases of the low-cost passive elements on the STAR-RIS alternatively until the convergence. Specifically, we first decompose the formulated energy efficiency problem into beamforming and phase shift optimization problems. To efficiently address the non-convex beamforming optimization problem, we exploit signal alignment and zero-forcing precoding methods in each user pair to decompose MIMO-NOMA channels into single-antenna NOMA channels. Then, the Dinkelbach approach and dual decomposition are utilized to optimize the beamforming vectors. In order to solve non-convex phase shift optimization problem, we propose a successive convex approximation (SCA) based method to efficiently obtain the optimized phase shift of STAR-RIS. Simulation results demonstrate that the proposed algorithm with NOMA technology can yield superior energy efficiency performance over the orthogonal multiple access (OMA) scheme and the random phase shift scheme

    Solving Variational Inequalities Defined on A Domain with Infinitely Many Linear Constraints

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    We study a variational inequality problem whose domain is defined by infinitely many linear inequalities. A discretization method and an analytic center based inexact cutting plane method are proposed. Under proper assumptions, the convergence results for both methods are given. We also provide numerical examples for the proposed methods

    Modeling, Analysis, and Optimization of Grant-Free NOMA in Massive MTC via Stochastic Geometry

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    Massive machine-type communications (mMTC) is a crucial scenario to support booming Internet of Things (IoTs) applications. In mMTC, although a large number of devices are registered to an access point (AP), very few of them are active with uplink short packet transmission at the same time, which requires novel design of protocols and receivers to enable efficient data transmission and accurate multi-user detection (MUD). Aiming at this problem, grant-free non-orthogonal multiple access (GF-NOMA) protocol is proposed. In GF-NOMA, active devices can directly transmit their preambles and data symbols altogether within one time frame, without grant from the AP. Compressive sensing (CS)-based receivers are adopted for non-orthogonal preambles (NOP)-based MUD, and successive interference cancellation is exploited to decode the superimposed data signals. In this paper, we model, analyze, and optimize the CS-based GF-MONA mMTC system via stochastic geometry (SG), from an aspect of network deployment. Based on the SG network model, we first analyze the success probability as well as the channel estimation error of the CS-based MUD in the preamble phase and then analyze the average aggregate data rate in the data phase. As IoT applications highly demands low energy consumption, low infrastructure cost, and flexible deployment, we optimize the energy efficiency and AP coverage efficiency of GF-NOMA via numerical methods. The validity of our analysis is verified via Monte Carlo simulations. Simulation results also show that CS-based GF-NOMA with NOP yields better MUD and data rate performances than contention-based GF-NOMA with orthogonal preambles and CS-based grant-free orthogonal multiple access.Comment: This paper is submitted to IEEE Internet Of Things Journa

    Salvia miltiorrhiza aqueous root extract plays an important role in improving locomotor activity in rats with spinal cord injury

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    Purpose: To investigate the activity of the aqueous root extract of Salvia miltiorrhiza (S. miltiorrhiza) (Lamiaceae), collected from Anhui Province, China, for the treatment of spinal cord injury (SCI) in Sprague-Dawley (SD) rats.Methods: In total, 30 adult rats were selected and divided into three groups; normal control, untreated and treated. Aqueous root extract of S. miltiorrhiza was introduced intraperitoneally to the treated group. Basso, Beattie and Bresnahan rating scale (BBB) was used to evaluate improvement in locomotor activity after SCI. Total RNA was extracted from tissue sections using Sepasol (NacalaiTesque) and RNA samples were reverse-transcribed using M-MLV reverse transcriptase. BioSense SC-810 Gel Documentation System and Gel-Pro 3.1 software were employed for the analysis of band intensity.Results: A significant reduction in SCI cavity area was observed in the S. miltiorrhiza extract-treated group, relative to the untreated group, after 11 days (0.10 ± 0.05 mm2 treated vs. 0.30 ± 0.01 mm2 untreated). Treatment with root extract also improved the BBB scores; the treated group scored 15, compared to a score of 8 for the untreated group. In addition, the degradation of neurons at the site of injury in the spinal cord was reduced in the treated group compared to the untreated group. Treatment with S. miltiorrhiza aqueous root extract also significantly increased the expression of platelet-derived growth factor-B (PDGF-B) mRNA (p < 0.01).Conclusion: These data suggest that, in addition to other pharmacological activities, S. miltiorrhiza extract has therapeutic potential for the treatment of neuronal degeneration following SCI.Keywords: Salvia Miltiorrhiza, Neurons, Spinal cord injury, Locomotor capacity, Platelet-derived growth factor-B, Basso, Beattie and Bresnahan rating scal

    Localized Langerhans cell histiocytosis masquerading as Brodie s abscess in a 2-year-old child: a case report

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    Langerhans cell histiocytosis (LCH), formerly known as histiocytosis X, refers to a spectrum of diseases characterized by idiopathic proliferation of histiocytes that produce either focal (localized LCH) or systemic manifestations (Hand–Schüller–Christian disease and Letterer–Siwe disease). Localized LCH accounts for approximately 60–70 % of all LCH cases. Osseous involvement is the most common manifestation and typically involves the flat bones, along with lesions of the skull, pelvis, and ribs. Localized LCH in bone shows a wide spectrum of clinical manifestations and radiologic features that may mimic those of infections as well as benign and malignant tumors. The diagnostic imaging findings of localized LCH are also diverse and challenging. The penumbra sign is a common and characteristic magnetic resonance imaging (MRI) feature of Brodie’s abscess, but is rarely seen in localized LCH. In this report, we describe a case of localized LCH misdiagnosed as Brodie’s abscess in a 2-year-old child based on clinical symptoms, laboratory findings, and pre-diagnostic MRI findings (penumbra sign). Therefore, the penumbra sign is not sufficient to clearly establish the diagnosis of Brodie’s abscess, and the differential diagnosis of localized LCH should be considered when a child with an osteolytic lesion presents with a penumbra sign

    Microblog Rumor Detection Method Based on Propagation Path Tree Kernel Learning

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    The rapid development of online social platforms such as microblog promotes the widespread propagation of various rumors information,thereby posing potential threats to social order.Rumor detection on microblog can effectively curb the spread of rumors and is of great significance for purifying the network environment and maintaining social stability.In view of the fact that the traditional rumor detection model only considers the characteristics of users,contents and communication statistics,and ignores the structural problem that the characteristics of users′ influence and emotional feedback increase with the forwarding and comment relationship in the process of rumor communication,a path tree kernel rumor automatic detection model based on the microblog information propagation tree is proposed in this paper.It embeds users’ influence,emotional feedback,contents into the nodes ofpropagation tree.By calculating the path similarity from the root node to the leaf node in propagation tree,the similarity between the microblog information propagation tree structure is obtained.Furthermore,the model uses the support vector machine classifier based on the propagation path tree kernel todetect microblog rumors.Experimental results show that the accuracy of the proposed model reaches 93.5%,which is better than that of the rumor detection models without considering the structure of propagation path

    ATRW: A Benchmark for Amur Tiger Re-identification in the Wild

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    Monitoring the population and movements of endangered species is an important task to wildlife conversation. Traditional tagging methods do not scale to large populations, while applying computer vision methods to camera sensor data requires re-identification (re-ID) algorithms to obtain accurate counts and moving trajectory of wildlife. However, existing re-ID methods are largely targeted at persons and cars, which have limited pose variations and constrained capture environments. This paper tries to fill the gap by introducing a novel large-scale dataset, the Amur Tiger Re-identification in the Wild (ATRW) dataset. ATRW contains over 8,000 video clips from 92 Amur tigers, with bounding box, pose keypoint, and tiger identity annotations. In contrast to typical re-ID datasets, the tigers are captured in a diverse set of unconstrained poses and lighting conditions. We demonstrate with a set of baseline algorithms that ATRW is a challenging dataset for re-ID. Lastly, we propose a novel method for tiger re-identification, which introduces precise pose parts modeling in deep neural networks to handle large pose variation of tigers, and reaches notable performance improvement over existing re-ID methods. The dataset is public available at https://cvwc2019.github.io/ .Comment: ACM Multimedia (MM) 202

    A Practical Guide for X-Ray Diffraction Characterization of Ga(Al, In)N Alloys

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    Ga(In, Al)N alloys are used as an active layer or cladding layer in light emitting diodes and laser diodes. x-ray diffraction is extensively used to evaluate the crystalline quality, the chemical composition and the residual strain in Ga(Al,In)N thin films, which directly determine the emission wavelength and the device performance. Due to the minor mismatch in lattice parameters between Ga(Al, In)N alloy and a GaN virtual substrate, x-ray diffraction comes to a problem to separate the signal from Ga(Al,In)N alloy and GaN. We give a detailed comparison on different diffraction planes. In order to balance the intensity and peak separation between Ga(Al,In)N alloy and GaN, (0004) and (1015) planes make the best choice for symmetric scan and asymmetric scan, respectively.Comment: 9 pages, 5 figure
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