Abstract: A new approach for the improvement of coding gain
in channel coding using Advanced Encryption Standard (AES) and
Maximum A Posteriori (MAP) algorithm is proposed. This new
approach uses the avalanche effect of block cipher algorithm AES
and soft output values of MAP decoding algorithm. The performance
of proposed approach is evaluated in the presence of Additive White
Gaussian Noise (AWGN). For the verification of proposed approach,
computer simulation results are included.
Abstract: This paper details the application of a genetic
programming framework for induction of useful classification rules
from a database of income statements, balance sheets, and cash flow
statements for North American public companies. Potentially
interesting classification rules are discovered. Anomalies in the
discovery process merit further investigation of the application of
genetic programming to the dataset for the problem domain.
Abstract: In this paper, an analysis is presented, which
demonstrates the effect pre-logic factoring could have on an
automated combinational logic synthesis process succeeding it. The
impact of pre-logic factoring for some arbitrary combinatorial
circuits synthesized within a FPGA based logic design environment
has been analyzed previously. This paper explores a similar effect,
but with the non-regenerative logic synthesized using elements of a
commercial standard cell library. On an overall basis, the results
obtained pertaining to the analysis on a variety of MCNC/IWLS
combinational logic benchmark circuits indicate that pre-logic
factoring has the potential to facilitate simultaneous power, delay and
area optimized synthesis solutions in many cases.
Abstract: We demonstrate single-photon interference over 10 km using a plug and play system for quantum key distribution. The quality of the interferometer is measured by using the interferometer
visibility. The coding of the signal is based on the phase coding and the value of visibility is based on the interference effect, which result a number of count. The setup gives full control of polarization inside
the interferometer. The quality measurement of the interferometer is based on number of count per second and the system produces 94 % visibility in one of the detectors.
Abstract: Realistic 3D face model is desired in various
applications such as face recognition, games, avatars, animations, and
etc. Construction of 3D face model is composed of 1) building a face
shape model and 2) rendering the face shape model. Thus, building a
realistic 3D face shape model is an essential step for realistic 3D face
model. Recently, 3D morphable model is successfully introduced to
deal with the various human face shapes. 3D dense correspondence
problem should be precedently resolved for constructing a realistic 3D
dense morphable face shape model. Several approaches to 3D dense
correspondence problem in 3D face modeling have been proposed
previously, and among them optical flow based algorithms and TPS
(Thin Plate Spline) based algorithms are representative. Optical flow
based algorithms require texture information of faces, which is
sensitive to variation of illumination. In TPS based algorithms
proposed so far, TPS process is performed on the 2D projection
representation in cylindrical coordinates of the 3D face data, not
directly on the 3D face data and thus errors due to distortion in data
during 2D TPS process may be inevitable.
In this paper, we propose a new 3D dense correspondence algorithm
for 3D dense morphable face shape modeling. The proposed algorithm
does not need texture information and applies TPS directly on 3D face
data. Through construction procedures, it is observed that the proposed
algorithm constructs realistic 3D face morphable model reliably and
fast.
Abstract: We created the tool, which combines the powerful
GENESIS (GEneral NEural SImulation System) simulation language
with the up-to-date visualisation and internet techniques. Our
solution resides in the connection between the simulation output from
GENESIS, which is converted to the data-structure suitable for
WWW browsers and VRML (Virtual Reality Modelling Language)
viewers. The selected GENESIS simulations are once exported into
the VRML code, and stored in our neurovisualisation portal
(webserver). There, the loaded models, demonstrating mainly the
spread of electrical signal (action potentials, postsynaptic potentials)
along the neuronal membrane (axon, dendritic tree, neuron) could be
displayed in the client-s VRML viewer, without interacting with
original GENESIS environment. This enables the visualisation of
basic neurophysiological phenomena designed for GENESIS
simulator on the independent OS (operation system).
Abstract: This paper proposes a location-aware system for
household robots which allows users to paste predefined paper tags at
different locations according to users- comprehension of the house. In this system a household robot may be aware of its location and the
attributes thereof by visually recognizing the tags when the robot is moving. This paper also presents a novel user interface to define a
moving path of the robot, which allows users to draw the path in the air
with a finger so as to generate commands for following motions.
Abstract: Support vector machines (SVMs) have shown
superior performance compared to other machine learning techniques,
especially in classification problems. Yet one limitation of SVMs is
the lack of an explanation capability which is crucial in some
applications, e.g. in the medical and security domains. In this paper, a
novel approach for eclectic rule-extraction from support vector
machines is presented. This approach utilizes the knowledge acquired
by the SVM and represented in its support vectors as well as the
parameters associated with them. The approach includes three stages;
training, propositional rule-extraction and rule quality evaluation.
Results from four different experiments have demonstrated the value
of the approach for extracting comprehensible rules of high accuracy
and fidelity.
Abstract: With the widespread growth of applications of
Wireless Sensor Networks (WSNs), the need for reliable security
mechanisms these networks has increased manifold. Many security
solutions have been proposed in the domain of WSN so far. These
solutions are usually based on well-known cryptographic
algorithms.
In this paper, we have made an effort to survey well known
security issues in WSNs and study the behavior of WSN nodes that
perform public key cryptographic operations. We evaluate time
and power consumption of public key cryptography algorithm for
signature and key management by simulation.
Abstract: In this paper, different approaches to solve the
forward kinematics of a three DOF actuator redundant hydraulic
parallel manipulator are presented. On the contrary to series
manipulators, the forward kinematic map of parallel manipulators
involves highly coupled nonlinear equations, which are almost
impossible to solve analytically. The proposed methods are using
neural networks identification with different structures to solve the
problem. The accuracy of the results of each method is analyzed in
detail and the advantages and the disadvantages of them in
computing the forward kinematic map of the given mechanism is
discussed in detail. It is concluded that ANFIS presents the best
performance compared to MLP, RBF and PNN networks in this
particular application.
Abstract: This paper proposes a low-cost reconfigurable
architecture for AES algorithm. The proposed architecture separates
SubBytes and MixColumns into two parallel data path, and supports
different bit-width operation for this two data path. As a result, different number of S-box can be supported in this architecture. The
throughput and power consumption can be adjusted by changing the
number of S-box running in this design. Using the TSMC 0.18μm CMOS standard cell library, a very low-cost implementation of 7K
Gates is obtained under 182MHz frequency. The maximum throughput is 360Mbps while using 4 S-Box simultaneously, and the
minimum throughput is 114Mbps while only using 1 S-Box