What you always wanted to know about networking but were afraid to ask!
* How the Internet works
* How e-mail, e-learning, and telephony work on the Internet
* What makes a network safe
* How traffic gets from here to there
* Disaster recovery and other ways to keep a network running
* How businesses share data
* See the world of Cisco networking with this illustrated guide s visual approach to learning
* Useful for both novices and networking professionals
* Covers a broad variety of internetworking topics-from e-mail to VPNs
At last-an illustrated guide to the world of Cisco networking. Cisco Networking Simplified breaks down the complicated world of internetworking into easy-to-understand parts. Learn quickly and easily the fundamentals of a variety of topics, such as security, IP telephony, and quality of service, from the full-color diagrams and clear explanations found in Cisco Networking Simplified.
A large body of computer-aided techniques has been developed in recent years to assist
in the process of modeling, analyzing, and designing communication systems . These
computer-aided techniques fall into two categories: formula-based approaches, where the
computer is used to evaluate complex formulas, and simulation-based approaches, where the
computer is used to simulate the waveforms or signals that flow through the system. The
second approach, which involves “waveform”-level simulation (and often incorporates
analytical techniques), is the subject of this book.
Since performance evaluation and trade off studies are the central issues in the analysis
and design of communication systems, we will focus on the use of simulation for evaluating
the performance of analog and digital communication systems with the emphasis on digitalcommunication systems.
The object detector described below has been initially proposed by
P.F. Felzenszwalb in [Felzenszwalb2010]. It is based on a
Dalal-Triggs detector that uses a single filter on histogram of
oriented gradients (HOG) features to represent an object category.
This detector uses a sliding window approach, where a filter is
applied at all positions and scales of an image. The first
innovation is enriching the Dalal-Triggs model using a
star-structured part-based model defined by a “root” filter
(analogous to the Dalal-Triggs filter) plus a set of parts filters
and associated deformation models. The score of one of star models
at a particular position and scale within an image is the score of
the root filter at the given location plus the sum over parts of the
maximum, over placements of that part, of the part filter score on
its location minus a deformation cost easuring the deviation of the
part from its ideal location relative to the root. Both root and
part filter scores are defined by the dot product between a filter
(a set of weights) and a subwindow of a feature pyramid computed
from the input image. Another improvement is a representation of the
class of models by a mixture of star models. The score of a mixture
model at a particular position and scale is the maximum over
components, of the score of that component model at the given
location.
We introduce a sub-cell WENO reconstruction method to evaluate spatial derivatives in the high-order ADER scheme. The basic idea in our reconstruction is to use only r stencils to reconstruct the point-wise values of solutions and spatial derivatives for the 2r-1 th order
ADER scheme in one dimension, while in two dimensions, the dimension-by-dimension sub-cell reconstruction approach for spatial derivatives is employed. Compared with the original ADER scheme of Toro and Titarev (2002) [2] that uses the direct derivatives of reconstructed polynomials for solutions to evaluate spatial derivatives, our method not only reduces greatly the computational costs of the ADER scheme on a given mesh,
but also avoids possible numerical oscillations near discontinuities, as demonstrated by a number of one- and two-dimensional numerical tests. All these tests show that the 5th-order ADER scheme based on our sub-cell reconstruction method achieves the desired accuracy, and is essentially non-oscillatory and computationally cheaper for problems with discontinuities.
We consider the problem of target localization by a
network of passive sensors. When an unknown target emits an
acoustic or a radio signal, its position can be localized with multiple
sensors using the time difference of arrival (TDOA) information.
In this paper, we consider the maximum likelihood formulation
of this target localization problem and provide efficient convex
relaxations for this nonconvex optimization problem.We also propose
a formulation for robust target localization in the presence of
sensor location errors. Two Cramer-Rao bounds are derived corresponding
to situations with and without sensor node location errors.
Simulation results confirm the efficiency and superior performance
of the convex relaxation approach as compared to the
existing least squares based approach when large sensor node location
errors are present.
Lithium–sulfur batteries are a promising energy-storage technology due to their relatively low cost and high theoretical energy density. However, one of their major technical problems is the shuttling of soluble polysulfides between electrodes, resulting in rapid capacity fading. Here, we present a metal–organic framework (MOF)-based battery separator to mitigate the shuttling problem. We show that the MOF-based separator acts as an ionic sieve in lithium–sulfur batteries, which selectively sieves Li+ ions while e ciently suppressing undesired polysulfides migrating to the anode side. When a sulfur-containing mesoporous carbon material (approximately 70 wt% sulfur content) is used as a cathode composite without elaborate synthesis or surface modification, a lithium–sulfur battery with a MOF-based separator exhibits a low capacity decay rate (0.019% per cycle over 1,500 cycles). Moreover, there is almost no capacity fading after the initial 100 cycles. Our approach demonstrates the potential for MOF-based materials as separators for energy-storage applications.
The 4.0 kbit/s speech codec described in this paper is based on a
Frequency Domain Interpolative (FDI) coding technique, which
belongs to the class of prototype waveform Interpolation (PWI)
coding techniques. The codec also has an integrated voice
activity detector (VAD) and a noise reduction capability. The
input signal is subjected to LPC analysis and the prediction
residual is separated into a slowly evolving waveform (SEW) and
a rapidly evolving waveform (REW) components. The SEW
magnitude component is quantized using a hierarchical
predictive vector quantization approach. The REW magnitude is
quantized using a gain and a sub-band based shape. SEW and
REW phases are derived at the decoder using a phase model,
based on a transmitted measure of voice periodicity. The spectral
(LSP) parameters are quantized using a combination of scalar
and vector quantizers. The 4.0 kbits/s coder has an algorithmic
delay of 60 ms and an estimated floating point complexity of
21.5 MIPS. The performance of this coder has been evaluated
using in-house MOS tests under various conditions such as
background noise. channel errors, self-tandem. and DTX mode
of operation, and has been shown to be statistically equivalent to
ITU-T (3.729 8 kbps codec across all conditions tested.
Accurate pose estimation plays an important role in solution of simultaneous localization and mapping (SLAM) problem, required for many robotic applications. This paper presents a new approach called R-SLAM, primarily to overcome systematic and non-systematic odometry errors which are generally caused by uneven floors, unexpected objects on the floor or wheel-slippage due to skidding or fast turns.The hybrid approach presented here combines the strengths of feature based and grid based methods to produce globally consistent high resolution maps within various types of environments.
