) Compression using huffman code
-with a number of bits k per code word
-provide huffman table
Huffman coding is OPTIMAL for a symbol-by-symbol coding with a known input probability distribution.This technique uses a variable-length code table for encoding a source symbol. The table is derived in a particular way based on the estimated probability of occurrence for each possible value of the source symbol .Huffman coding uses a specific method for representing each symbol, resulting in a prefix code that expresses the most common characters using shorter strings of bits than those used for less common source symbols.The Huffman coding is a procedure to generate a binary code tree.
MATSNL is a package of MATLAB M-files for computing wireless sensor node lifetime/power budget and solving OPTIMAL node architecture choice problems. It is intended as an analysis and simulation tool for researchers and educators that are easy to use and modify. MATSNL is designed to give the rough power/ lifetime predictions based on node and application specifications while giving useful insight on platform design for the large node lifetime by providing side-by-side comparison across various platforms. The MATSNL code and manual can be found at the bottom of this page. A related list of publications describing the models used in MATSNL is posted on the ENALAB part of the 2 project at http://www.eng.yale.edu/enalab/aspire.htm
The emphasis of this book is on real-time application of Synopsys tools, used
to combat various problems seen at VDSM geometries. Readers will be
exposed to an effective design methodology for handling complex, submicron
ASIC designs. Significance is placed on HDL coding styles,
synthesis and optimization, dynamic simulation, formal verification, DFT
scan insertion, links to layout, physical synthesis, and static timing analysis.
At each step, problems related to each phase of the design flow are identified,
with solutions and work-around described in detail. In addition, crucial issues
related to layout, which includes clock tree synthesis and back-end
integration (links to layout) are also discussed at length. Furthermore, the
book contains in-depth discussions on the basics of Synopsys technology
libraries and HDL coding styles, targeted towards OPTIMAL synthesis solution.
The NetBeans IDE has seen adoption snowballing over the past
years, particularly with the introduction of a completely new,
rewritten, slick Java editor. You’ll fnd this reference card helpful
if you want to get as much out of the Java editor as its authors
intended when creating it. It lists all the keyboard shortcuts in carefully thought out categories and it provides a thorough
exposition of OPTIMAL handling of Java code in the editor,
covering viewing, navigation, source handling, and refactoring.
Get NetBeans IDE: http://www.netbeans.org/downloads
MATSNL is a package of MATLAB M-files for computing wireless sensor node
lifetime/power budget and solving OPTIMAL node architecture choice problems. It is intended
as an analysis and simulation tool for researchers and educators that are easy to use and
modify. MATSNL is designed to give the rough power/ lifetime predictions based on node
and application specifications while giving useful insight on platform design for the large
node lifetime by providing side-by-side comparison across various platforms.
sba, a C/C++ package for generic sparse bundle adjustment is almost invariably used as the last step of every feature-based multiple view reconstruction vision algorithm to obtain OPTIMAL 3D structure and motion (i.e. camera matrix) parameter estimates. Provided with initial estimates, BA simultaneously refines motion and structure by minimizing the reprojection error between the observed and predicted image points.
Computational models are commonly used in engineering design and scientific discovery activities for simulating
complex physical systems in disciplines such as fluid mechanics, structural dynamics, heat transfer, nonlinear
structural mechanics, shock physics, and many others. These simulators can be an enormous aid to engineers who
want to develop an understanding and/or predictive capability for complex behaviors typically observed in the
corresponding physical systems. Simulators often serve as virtual prototypes, where a set of predefined system
parameters, such as size or location dimensions and material properties, are adjusted to improve the performance
of a system, as defined by one or more system performance objectives. Such optimization or tuning of the
virtual prototype requires executing the simulator, evaluating performance objective(s), and adjusting the system
parameters in an iterative, automated, and directed way. System performance objectives can be formulated, for
example, to minimize weight, cost, or defects; to limit a critical temperature, stress, or vibration response; or
to maximize performance, reliability, throughput, agility, or design robustness. In addition, one would often
like to design computer experiments, run parameter studies, or perform uncertainty quantification (UQ). These
approaches reveal how system performance changes as a design or uncertain input variable changes. Sampling
methods are often used in uncertainty quantification to calculate a distribution on system performance measures,
and to understand which uncertain inputs contribute most to the variance of the outputs.
A primary goal for Dakota development is to provide engineers and other disciplinary scientists with a systematic
and rapid means to obtain improved or OPTIMAL designs or understand sensitivity or uncertainty using simulationbased
models. These capabilities generally lead to improved designs and system performance in earlier design
stages, alleviating dependence on physical prototypes and testing, shortening design cycles, and reducing product
development costs. In addition to providing this practical environment for answering system performance questions,
the Dakota toolkit provides an extensible platform for the research and rapid prototyping of customized
methods and meta-algorithms
Abstract—In the future communication applications, users
may obtain their messages that have different importance levels
distributively from several available sources, such as distributed
storage or even devices belonging to other users. This
scenario is the best modeled by the multilevel diversity coding
systems (MDCS). To achieve perfect (information-theoretic)
secrecy against wiretap channels, this paper investigates the
fundamental limits on the secure rate region of the asymmetric
MDCS (AMDCS), which include the symmetric case as a special
case. Threshold perfect secrecy is added to the AMDCS model.
The eavesdropper may have access to any one but not more than
one subset of the channels but know nothing about the sources,
as long as the size of the subset is not above the security level.
The question of whether superposition (source separation) coding
is OPTIMAL for such an AMDCS with threshold perfect secrecy
is answered. A class of secure AMDCS (S-AMDCS) with an
arbitrary number of encoders is solved, and it is shown that linear
codes are OPTIMAL for this class of instances. However, in contrast
with the secure symmetric MDCS, superposition is shown to
be not OPTIMAL for S-AMDCS in general. In addition, necessary
conditions on the existence of a secrecy key are determined as a
design guideline.
