Appropriation and Representation

Feng Menglong (1574–1646) was recognized as the most knowledgeable connoisseur of popular literature of his time. He is known today for compiling three famous collections of vernacular short stories, each containing forty stories, collectively known as Sanyan. Appropriation and Representation adapts concepts of ventriloquism and dialogism from Bakhtin and Holquist to explore Feng’s methods of selecting source materials. Shuhui Yang develops a model of development in which Feng’s approach to selecting and working with his source materials becomes clear. More broadly, Appropriation and Representation locates Feng Menglong’s Sanyan in the cultural milieu of the late Ming, including the archaist movement in literature, literati marginality and anxieties, the subversive use of folk works, and the meiren xiangcao tradition—appropriating a female identity to express male frustration. Against this background, a rationale emerges for Feng’s choice to elevate and promote the vernacular story while stepping back form an overt authorial role

Doing More with the Dew: A New Approach to Cloud-Dew Architecture

While the popularity of cloud computing is exploding, a new network computing paradigm is just beginning. In this paper, we examine this exciting area of research known as dew computing and propose a new design of cloud-dew architecture. Instead of hosting only one dew server on a user's PC - as adopted in the current dewsite application - our design promotes the hosting of multiple dew servers instead, one for each installed domain. Our design intends to improve upon existing cloud-dew architecture by providing significantly increased freedom in dewsite development, while also automating the chore of managing dewsite content based on the user's interests and browsing habits. Other noteworthy benefits, all at no added cost to dewsite users, are briefly explored as well

Development of Metamaterial EBG Absorbers for Application of Wireless Inter/Intrachip Communication Systems

First, the chapter presents a novel design of electromagnetic bandgap (EBG) absorber with the characteristics of broad bandwidth, low profile, and polarization‐independence to a normal incident electromagnetic wave. The absorber is composed of three consecutive octagon or decagon loops, and highly‐resistive frequency selective surface (FSS) layers. Second, based on the feature of the designed absorber unit, a broadband, metamaterial absorber‐bounded, wireless inter/intrachip (WIIC) communication channel is constructed at the center frequency of 60 GHz. Third, in order to validate the developed methodology used in WIIC analysis, a wired channel on a conventional PCB has been measured, simulated, and analyzed. Fourth, with the extracted S‐parameters of the WIIC system and wired PCB channel, the system impulse responses and transfer functions of the investigated channels have been further extracted, which are used for validation and BER analysis of the WIIC system. Finally, it has been shown that based on the derived BER results, the performance of the designed WIIC channel is close to that of an additive Gaussian white noise (AWGN) channel when the WIIC transceivers are built in with the functionalities of forward error control (FEC), channel estimation, and equalization

High-efficient Bloch simulation of magnetic resonance imaging sequences based on deep learning

Objective: Bloch simulation constitutes an essential part of magnetic resonance imaging (MRI) development. However, even with the graphics processing unit (GPU) acceleration, the heavy computational load remains a major challenge, especially in large-scale, high-accuracy simulation scenarios. This work aims to develop a deep learning-based simulator to accelerate Bloch simulation. Approach: The simulator model, called Simu-Net, is based on an end-to-end convolutional neural network and is trained with synthetic data generated by traditional Bloch simulation. It uses dynamic convolution to fuse spatial and physical information with different dimensions and introduces position encoding templates to achieve position-specific labeling and overcome the receptive field limitation of the convolutional network. Main Results: Compared with mainstream GPU-based MRI simulation software, Simu-Net successfully accelerates simulations by hundreds of times in both traditional and advanced MRI pulse sequences. The accuracy and robustness of the proposed framework were verified qualitatively and quantitatively. Besides, the trained Simu-Net was applied to generate sufficient customized training samples for deep learning-based T2 mapping and comparable results to conventional methods were obtained in the human brain. Significance: As a proof-of-concept work, Simu-Net shows the potential to apply deep learning for rapidly approximating the forward physical process of MRI and may increase the efficiency of Bloch simulation for optimization of MRI pulse sequences and deep learning-based methods.Comment: 18 pages, 8 figure

High-efficient deep learning-based DTI reconstruction with flexible diffusion gradient encoding scheme

Purpose: To develop and evaluate a novel dynamic-convolution-based method called FlexDTI for high-efficient diffusion tensor reconstruction with flexible diffusion encoding gradient schemes. Methods: FlexDTI was developed to achieve high-quality DTI parametric mapping with flexible number and directions of diffusion encoding gradients. The proposed method used dynamic convolution kernels to embed diffusion gradient direction information into feature maps of the corresponding diffusion signal. Besides, our method realized the generalization of a flexible number of diffusion gradient directions by setting the maximum number of input channels of the network. The network was trained and tested using data sets from the Human Connectome Project and a local hospital. Results from FlexDTI and other advanced tensor parameter estimation methods were compared. Results: Compared to other methods, FlexDTI successfully achieves high-quality diffusion tensor-derived variables even if the number and directions of diffusion encoding gradients are variable. It increases peak signal-to-noise ratio (PSNR) by about 10 dB on Fractional Anisotropy (FA) and Mean Diffusivity (MD), compared with the state-of-the-art deep learning method with flexible diffusion encoding gradient schemes. Conclusion: FlexDTI can well learn diffusion gradient direction information to achieve generalized DTI reconstruction with flexible diffusion gradient schemes. Both flexibility and reconstruction quality can be taken into account in this network.Comment: 11 pages,6 figures,3 table

Extended dominating set in ad hoc networks using cooperative communication

Abstract. We propose a notion of extended dominating set whereby each node in an ad hoc network is covered by either a dominating neighbor or several 2-hop dominating neighbors. This work is motivated by cooperative communication in ad hoc networks where transmitting independent copies of a packet generates diversity and combats the effects of fading. In this paper we propose several efficient heuristic algorithms for constructing a small extended dominating set