A soft robotic fish can quickly swim and turn with a fully integrated onboard system for power and remote control.

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This paper addresses the problem of downlink channel estimation in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. The existing methods usually exploit hidden sparsity under a discrete Fourier transform (DFT) basis to estimate the downlink channel. However, there are at least two shortcomings of these DFT-based methods: 1) they are applicable to uniform linear arrays (ULAs) only, since the DFT basis requires a special structure of ULAs; and 2) they always suffer from a performance loss due to the leakage of energy over some DFT bins. To deal with the above shortcomings, we introduce an off-grid model for downlink channel sparse representation with arbitrary 2D-array antenna geometry, and propose an efficient sparse Bayesian learning (SBL) approach for the sparse channel recovery and off-grid refinement. The main idea of the proposed off-grid method is to consider the sampled grid points as adjustable parameters. Utilizing an in-exact block majorization-minimization (MM) algorithm, the grid points are refined iteratively to minimize the off-grid gap. Finally, we further extend the solution to uplinkaided channel estimation by exploiting the angular reciprocity between downlink and uplink channels, which brings enhanced recovery performance.

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The performance of the existing sparse Bayesian learning (SBL) methods for off-gird DOA estimation is dependent on the trade off between the accuracy and the computational workload. To speed up the off-grid SBL method while remain a reasonable accuracy, this letter describes a computationally efficient root SBL method for off-grid DOA estimation, where a coarse refinable grid, whose sampled locations are viewed as the adjustable parameters, is adopted. We utilize an expectation-maximization (EM) algorithm to iteratively refine this coarse grid, and illustrate that each updated grid point can be simply achieved by the root of a certain polynomial. Simulation results demonstrate that the computational complexity is significantly reduced and the modeling error can be almost eliminated.Comment: 4 pages, 4 figure

This study investigated the effect of silane and surfactant treatments of graphene nanoplatelets (GnPs) on the mechanical and thermal properties of silicone rubber (SR) composites. GnPs were modified with aminopropyltriethoxysilane (APTES), vinyltrimethoxysilane (VTMS), and Triton X-100, and then the pristine GnPs and functionalized GnPs were individually incorporated into the SR. Compared with the pristine GnP/SR composite, the composites reinforced with modified GnP showed better tensile strength, elongation at break, and thermal conductivity properties due to better dispersion of modified GnPs and stronger interfacial interactions between the modified GnPs and matrix. The mechanical properties and thermal conductivity of the VTMS-GnP/SR composite were comparable to the properties of the Triton-GnP counterpart, but better than that of the APTES-GnP/SR composite. In addition, the VTMS-GnP/SR composite demonstrated the highest thermal stability and crystallization temperature among the four types of composites. The remarkable improvement of mechanical and thermal properties of the VTMS-GnP/SR composite was mainly due to the covalent linkage of VTMS-GnP with SR. The VTMS treatment was a more appropriate modification of GnP particles to improve the multifunctional properties of SR.

Graphene nanosheets offer intriguing electronic, thermal and mechanical properties and are expected to find a variety of applications in high‐performance nanocomposite materials. The great challenge of exfoliating and dispersing pristine graphite or graphene sheets in various solvents or matrices can be achieved by facilely and properly chemical functionalization of the carbon nanosheets. Here we reported an efficient way to functionalize graphene sheets with presynthesized polymer via a combination of atom transfer nitroxide radical coupling chemistry with the grafting‐onto strategy, which enable us to functionalize graphene sheets with well‐defined polymer synthesized via living radical polymerization. A radical scavenger species, 2,2,6,6‐tetramethylpiperidine‐1‐oxyl (TEMPO), was firstly anchored onto COOH groups on graphene oxide (GO) to afford TEMPO‐functionalized graphene sheets (GS‐TEMPO), meanwhile, the GO sheets were thermally reduced. Next, GS‐TEMPO reacted with Br‐terminated well‐defined poly(N‐isopropylacrylamide) (PNIPAM) homopolymer, which was presynthesized by SET‐LRP, in the presence of CuBr/N,N,N′,N′,N″‐pentamethyldiethylenetriamine to form PNIPAM‐graphene sheets (GS‐PNIPAM) nanocomposite in which the polymers were covalently linked onto the graphene via the alkoxyamine conjunction points. The PNIPAM‐modified graphene sheets are easily dispersible in organic solvents and water, and a temperature‐induced phase transition was founded in the water suspension of GS‐PNIPAM. © 2011 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem, 2011

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