Abstract: The material behavior of graphene, a single layer of
carbon lattice, is extremely sensitive to its dielectric environment. We
demonstrate improvement in electronic performance of graphene
nanowire interconnects with full encapsulation by lattice-matching,
chemically inert, 2D layered insulator hexagonal boron nitride (h-
BN). A novel layer-based transfer technique is developed to construct
the h-BN/MLG/h-BN heterostructures. The encapsulated graphene
wires are characterized and compared with that on SiO2 or h-BN
substrate without passivating h-BN layer. Significant improvements
in maximum current-carrying density, breakdown threshold, and
power density in encapsulated graphene wires are observed. These
critical improvements are achieved without compromising the carrier
transport characteristics in graphene. Furthermore, graphene wires
exhibit electrical behavior less insensitive to ambient conditions, as
compared with the non-passivated ones. Overall, h-BN/graphene/h-
BN heterostructure presents a robust material platform towards the
implementation of high-speed carbon-based interconnects.
Abstract: A graph G is fractional k-covered if for each edge e of
G, there exists a fractional k-factor h, such that h(e) = 1. If k = 2,
then a fractional k-covered graph is called a fractional 2-covered
graph. The binding number bind(G) is defined as follows,
bind(G) = min{|NG(X)|
|X|
: ├ÿ = X Ôèå V (G),NG(X) = V (G)}.
In this paper, it is proved that G is fractional 2-covered if δ(G) ≥ 4
and bind(G) > 5
3 .
Abstract: Applying the idea of soft set theory to lattice implication algebras, the novel concept of (implicative) filteristic soft lattice implication algebras which related to (implicative) filter(for short, (IF-)F-soft lattice implication algebras) are introduced. Basic properties of (IF-)F-soft lattice implication algebras are derived. Two kinds of fuzzy filters (i.e.(2, 2 _qk)((2, 2 _ qk))-fuzzy (implicative) filter) of L are introduced, which are generalizations of fuzzy (implicative) filters. Some characterizations for a soft set to be a (IF-)F-soft lattice implication algebra are provided. Analogously, this idea can be used in other types of filteristic lattice implication algebras (such as fantastic (positive implicative) filteristic soft lattice implication algebras).
Abstract: Emergence of smartphones brings to live the concept
of converged devices with the availability of web amenities. Such
trend also challenges the mobile devices manufactures and service
providers in many aspects, such as security on mobile phones,
complex and long time design flow, as well as higher development
cost. Among these aspects, security on mobile phones is getting more
and more attention. Microkernel based virtualization technology will
play a critical role in addressing these challenges and meeting mobile
market needs and preferences, since virtualization provides essential
isolation for security reasons and it allows multiple operating systems
to run on one processor accelerating development and cutting development
cost. However, virtualization benefits do not come for free.
As an additional software layer, it adds some inevitable virtualization
overhead to the system, which may decrease the system performance.
In this paper we evaluate and analyze the virtualization performance
cost of L4 microkernel based virtualization on a competitive mobile
phone by comparing the L4Linux, a para-virtualized Linux on top of
L4 microkernel, with the native Linux performance using lmbench
and a set of typical mobile phone applications.
Abstract: In this paper, we propose a new approach to query-by-humming, focusing on MP3 songs database. Since MP3 songs are much more difficult in melody representation than symbolic performance data, we adopt to extract feature descriptors from the vocal sounds part of the songs. Our approach is based on signal filtering, sub-band spectral processing, MDCT coefficients analysis and peak energy detection by ignorance of the background music as much as possible. Finally, we apply dual dynamic programming algorithm for feature similarity matching. Experiments will show us its online performance in precision and efficiency.