32 research outputs found
Texture Evolution and Grain Competition in NiGe Film on Ge(001)
Get PDFNational Natural Science Foundation of China [61176092, 61036003, 60837001]; National Basic Research Program of China [2012CB933503, 2013CB632103]; Ph. D. Programs Foundation of Ministry of Education of China [20110121110025]; Fundamental Research Funds for the Central Universities [2010121056]; Natural Science Foundation of Fujian Province of China [2012J01284]; Opened Fund of the State Key Laboratory on Integrated Optoelectronics [2011KFB004]To understand the agglomeration mechanism of NiGe films grown on Ge(001), texture structures of NiGe films are revealed by X-ray pole figure measurement. Two preferred epitaxial orientations of the NiGe grains are identified to be NiGe(4 (5) over bar4) II Ge(001) NiGe[(1) over bar 01] II Ge[110] and NiGe(130) II Ge(001) NiGe[002] II Ge[110]. The component of the first epitaxial alignment becomes dominating and the latter diminishing with increasing annealing temperature. The NiGe grains of the second epitaxial alignment are unstable and diminishing at high temperature due to the relatively higher interface/surface energy. The competition of grains with various epitaxial orientations has made a significant contribution to film agglomeration. (C) 2013 The Japan Society of Applied Physic
Direct and indirect effects of climate on richness drive the latitudinal diversity gradient in forest trees
Get PDFData accessibility statement: Full census data are available upon reasonable request from the ForestGEO data portal, http://ctfs.si.edu/datarequest/ We thank Margie Mayfield, three anonymous reviewers and Jacob Weiner for constructive comments on the manuscript. This study was financially supported by the National Key R&D Program of China (2017YFC0506100), the National Natural Science Foundation of China (31622014 and 31570426), and the Fundamental Research Funds for the Central Universities (17lgzd24) to CC. XW was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB3103). DS was supported by the Czech Science Foundation (grant no. 16-26369S). Yves Rosseel provided us valuable suggestions on using the lavaan package conducting SEM analyses. Funding and citation information for each forest plot is available in the Supplementary Information Text 1.Peer reviewedPostprin
Arbuscular mycorrhizal trees influence the latitudinal beta-diversity gradient of tree communities in forests worldwide
Get PDFAcknowledgements This research paper was funded by the National Natural Science Foundation of China (31925027, 31622014 and 31570426) and the Fundamental Research Funds for the Central Universities (20lgpy116). Funding and citation information for each forest plot is available in Supplementary References. Full raw census data are available on reasonable request from the ForestGEO (https:// www.forestgeo.si.edu/). Bioclimatic variables and solar radiation are available from the WorldClim Database (http://worldclim.org/version2) and potential evapotranspiration and aridity index are available from the Global Aridity Index (Global-Aridity) and Global Potential Evapo-Transpiration (Global-PET) Geospatial Database (https://cgiarcsi. community/data/global-aridity-and-pet-database/).Peer reviewedPublisher PD
Deep Learning for Wireless Multi-User Multi-Antenna Communications
No full textMulti-user multiple-input multiple-output (MU-MIMO) can significantly improve the system capacity, spectrum efficiency, and link reliability by multiplexing different user terminals' (UT) transmissions in the spatial domain. However, demultiplexing the transmitted signal at the receiver side, known as MU-MIMO detection, can become a signal processing challenge when the user load in the spatial domain is high. In the past two decades, enormous research effects have been paid towards achieving a good performance-complexity trade-off. Recently, deep learning technologies have been introduced into this domain, where neural networks are employed to replace partially or fully the conventional function inside the MU-MIMO detection. Deep learning-based MIMO (deep-MIMO) detection is appealing in the sense that it has the potential to offer: 1) better support to parallel computing; 2) low online computation-complexities which are comparable with that of linear MIMO receivers; and 3) native optimization for specific wireless channels. In the literature, most of the existing deep-MIMO detection approaches belong to the family of model-driven detection, where their performance depends heavily on their conventional models. Moreover, they are mostly coherent detection, which either assume perfect channel knowledge or require channel estimation. In this case, the channel estimation overhead becomes a big problem in delay-sensitive communications. Motivated by the above observations, major contributions of this thesis are presented:First, a number of data-driven deep-MIMO detection approaches are developed. The development starts from a simple detection network, termed DDNet. It has been shown that DDNet can achieve near-optimum performance in simple MU-MIMO networks. However, our theoretical work reveals that DDNet is challenged by the signal processing scalability problem with respect to the number of active users. In order to scale up the data-driven deep-MIMO detection, a modular neural network approach, termed ModNet, is developed. It is shown that ModNet offers scalable detection performances for both under- and fully- loaded MIMO systems, while other approaches can perform well mainly for under-loaded MIMO systems. Moreover, ModNet demonstrates remarkable performance in over-loaded MIMO systems with a performance gap less than 1.5 dB to the optimum MLSD at BER of 10^{-2}. Even so, we show that existing deep-MIMO detection approaches suffer from channel over-training. To solve this problem, an orthogonal stochastic gradient descent (O-SGD) algorithm is developed, which enables single ModNet to efficiently work under multiple channel models.Second, an end-to-end learning approach for MU-SIMO joint transmitter and non-coherent receiver design, termed JTRD-Net, where transmitter side consists of a group of parallel linear layers for multiuser waveform design. The non-coherent receiver is modeled as a feed-forward deep neural network (DNN) so as to provide multiuser detection (MUD) capabilities. The entire JTRD-Net can be trained from end to end through deep learning. After training, JTRD-Net can work efficiently in a non-coherent manner. Simulation results show that JTRD-Net outperforms the existing pilot-based solutions and non-coherent detection approaches for at least 2 dB in complex Gaussian channels and 4.5 dB in Kronecker MIMO channels thanks to the coding gain offered by joint transmitter and receiver design
Unsupervised Deep Learning for MU-SIMO Joint Transmitter and Noncoherent Receiver Design
Get PDFThis work aims to handle the joint transmitter
and noncoherent receiver optimization for multiuser single-input
multiple-output (MU-SIMO) communications through unsupervised
deep learning. It is shown that MU-SIMO can be modeled
as a deep neural network with three essential layers, which
include a partially-connected linear layer for joint multiuser
waveform design at the transmitter side, and two nonlinear layers
for the noncoherent signal detection. The proposed approach
demonstrates remarkable MU-SIMO noncoherent communication
performance in Rayleigh fading channels
End-to-End Learning for Uplink MU-SIMO Joint Transmitter and Non-Coherent Receiver Design in Fading Channels
No full textIn this paper, a novel end-to-end learning approach, namely JTRD-Net, is proposed for uplink multiuser single-input multiple-output (MU-SIMO) joint transmitter and non-coherent receiver design (JTRD) in fading channels. The basic idea lies in the use of artificial neural networks (ANNs) to replace traditional communication modules at both transmitter and receiver sides. More specifically, the transmitter side is modeled as a group of parallel linear layers, which are responsible for multiuser waveform design; and the non-coherent receiver is formed by a deep feed-forward neural network (DFNN) so as to provide multiuser detection (MUD) capabilities. The entire JTRD-Net can be trained from end to end to adapt to channel statistics through deep learning. After training, JTRD-Net can work efficiently in a non-coherent manner without requiring any levels of channel state information (CSI). In addition to the network architecture, a novel weight-initialization method, namely symmetrical-interval initialization, is proposed for JTRD-Net. It is shown that the symmetrical-interval initialization outperforms the conventional method (e.g. Xavier initialization) in terms of well-balanced convergence-rate among users. Simulation results show that the proposed JTRD-Net approach takes significant advantages in terms of reliability and scalability over baseline schemes onboth i.i.d. complex Gaussian channels and spatially-correlated channels
An Orthogonal-SGD based Learning Approach for MIMO Detection under Multiple Channel Models
No full textIn this paper, an orthogonal stochastic gradient descent (O-SGD) based learning approach is proposed to tackle the wireless channel over-training problem inherent in artificial neural network (ANN)-assisted MIMO signal detection. Our basic idea lies in the discovery and exploitation of the training-sample orthogonality between the current training epoch and past training epochs. Unlike the conventional SGD that updates the neural network simply based upon current training samples, O-SGD discovers the correlation between current training samples and historical training data, and then updates the neural network with those uncorrelated components. The network updating occurs only in those identified null subspaces. By such means, the neural network can understand and memorize uncorrelated components between different wireless channels, and thus is more robust to wireless channel variations. This hypothesis is confirmed through our extensive computer simulations as well as performance comparison with the conventional SGD approach
