ARQ Protocols in Cognitive Decode-and-Forward Relay Networks: Opportunities Gain

In this paper, two novel automatic-repeat-request (ARQ) based protocols were proposed, which exploit coop- eration opportunity inherent in secondary retransmission to create access opportunities. If the signal was not decoded correctly in destination, another user can be acted as a relay to reduce retransmission rounds by relaying the signal. For comparison, we also propose a Direct ARQ Protocol. Specif- ically, we derive the exact closed-form outage probability of three protocols, which provides an effective means to evalu- ate the effects of several parameters. Moreover, we propose a new metric to evaluate the performance improvement for cognitive networks. Finally, Monte Carlo simulations were presented to validate the theory analysis, and a comparison is made among the three protocols

Simulating the Range Expansion of Spartina alterniflora

Environmental factors play an important role in the range expansion of Spartina alterniflora in estuarine salt marshes. CA models focusing on neighbor effect often failed to account for the influence of environmental factors. This paper proposed a CCA model that enhanced CA model by integrating constrain factors of tidal elevation, vegetation density, vegetation classification, and tidal channels in Chongming Dongtan wetland, China. Meanwhile, a positive feedback loop between vegetation and sedimentation was also considered in CCA model through altering the tidal accretion rate in different vegetation communities. After being validated and calibrated, the CCA model is more accurate than the CA model only taking account of neighbor effect. By overlaying remote sensing classification and the simulation results, the average accuracy increases to 80.75% comparing with the previous CA model. Through the scenarios simulation, the future of Spartina alterniflora expansion was analyzed. CCA model provides a new technical idea and method for salt marsh species expansion and control strategies research

Variational Denoising Network: Toward Blind Noise Modeling and Removal

Blind image denoising is an important yet very challenging problem in computer vision due to the complicated acquisition process of real images. In this work we propose a new variational inference method, which integrates both noise estimation and image denoising into a unique Bayesian framework, for blind image denoising. Specifically, an approximate posterior, parameterized by deep neural networks, is presented by taking the intrinsic clean image and noise variances as latent variables conditioned on the input noisy image. This posterior provides explicit parametric forms for all its involved hyper-parameters, and thus can be easily implemented for blind image denoising with automatic noise estimation for the test noisy image. On one hand, as other data-driven deep learning methods, our method, namely variational denoising network (VDN), can perform denoising efficiently due to its explicit form of posterior expression. On the other hand, VDN inherits the advantages of traditional model-driven approaches, especially the good generalization capability of generative models. VDN has good interpretability and can be flexibly utilized to estimate and remove complicated non-i.i.d. noise collected in real scenarios. Comprehensive experiments are performed to substantiate the superiority of our method in blind image denoising.Comment: 11 pages, 4 figure

Income inequality, innovation and carbon emission: Perspectives on sustainable growth

The present study aims to investigate the impact of income inequality and economic growth on carbon dioxide (CO2) emission through the moderating role of innovation in China at national and regional levels. To test the hypothesised relationships, this study took the data from 1995 to 2015 and employed panel estimation. Findings of the whole analysis show that income inequality and economic growth influence CO2 emission in China. Moreover, technological innovation moderates the proposed link. However, the findings at the regional level are mixed, thus confirming the existence of regional differences. Policy implications are also discussed