Abstract: For the last decade, researchers have started to focus
their interest on Multicast Group Key Management Framework. The
central research challenge is secure and efficient group key
distribution. The present paper is based on the Bit model based
Secure Multicast Group key distribution scheme using the most
popular absolute encoder output type code named Gray Code. The
focus is of two folds. The first fold deals with the reduction of
computation complexity which is achieved in our scheme by
performing fewer multiplication operations during the key updating
process. To optimize the number of multiplication operations, an
O(1) time algorithm to multiply two N-bit binary numbers which
could be used in an N x N bit-model of reconfigurable mesh is used
in this proposed work. The second fold aims at reducing the amount
of information stored in the Group Center and group members while
performing the update operation in the key content. Comparative
analysis to illustrate the performance of various key distribution
schemes is shown in this paper and it has been observed that this
proposed algorithm reduces the computation and storage complexity
significantly. Our proposed algorithm is suitable for high
performance computing environment.
Abstract: ESPRIT-TLS method appears a good choice for high
resolution fault detection in induction machines. It has a very high
effectiveness in the frequency and amplitude identification.
Contrariwise, it presents a high computation complexity which
affects its implementation in real time fault diagnosis. To avoid this
problem, a Fast-ESPRIT algorithm that combined the IIR band-pass
filtering technique, the decimation technique and the original
ESPRIT-TLS method was employed to enhance extracting accurately
frequencies and their magnitudes from the wind stator current with
less computation cost. The proposed algorithm has been applied to
verify the wind turbine machine need in the implementation of an online,
fast, and proactive condition monitoring. This type of remote
and periodic maintenance provides an acceptable machine lifetime,
minimize its downtimes and maximize its productivity. The
developed technique has evaluated by computer simulations under
many fault scenarios. Study results prove the performance of Fast-
ESPRIT offering rapid and high resolution harmonics recognizing
with minimum computation time and less memory cost.
Abstract: This paper describes a cycle accurate simulation results of weight values learned by an auto-encoder behavior model in terms of pre-route simulation. Given the results we visualized the first layer representations with natural images. Many common deep learning threads have focused on learning high-level abstraction of unlabeled raw data by unsupervised feature learning. However, in the process of handling such a huge amount of data, the learning method’s computation complexity and time limited advanced research. These limitations came from the fact these algorithms were computed by using only single core CPUs. For this reason, parallel-based hardware, FPGAs, was seen as a possible solution to overcome these limitations. We adopted and simulated the ready-made auto-encoder to design a behavior model in VerilogHDL before designing hardware. With the auto-encoder behavior model pre-route simulation, we obtained the cycle accurate results of the parameter of each hidden layer by using MODELSIM. The cycle accurate results are very important factor in designing a parallel-based digital hardware. Finally this paper shows an appropriate operation of behavior model based pre-route simulation. Moreover, we visualized learning latent representations of the first hidden layer with Kyoto natural image dataset.
Abstract: Red blood cells (RBCs) are among the most
commonly and intensively studied type of blood cells in cell biology.
Anemia is a lack of RBCs is characterized by its level compared to
the normal hemoglobin level. In this study, a system based image
processing methodology was developed to localize and extract RBCs
from microscopic images. Also, the machine learning approach is
adopted to classify the localized anemic RBCs images. Several
textural and geometrical features are calculated for each extracted
RBCs. The training set of features was analyzed using principal
component analysis (PCA). With the proposed method, RBCs were
isolated in 4.3secondsfrom an image containing 18 to 27 cells. The
reasons behind using PCA are its low computation complexity and
suitability to find the most discriminating features which can lead to
accurate classification decisions. Our classifier algorithm yielded
accuracy rates of 100%, 99.99%, and 96.50% for K-nearest neighbor
(K-NN) algorithm, support vector machine (SVM), and neural
network RBFNN, respectively. Classification was evaluated in highly
sensitivity, specificity, and kappa statistical parameters. In
conclusion, the classification results were obtained within short time
period, and the results became better when PCA was used.
Abstract: In the paper, a fast high-resolution range profile synthetic algorithm called orthogonal matching pursuit with sensing dictionary (OMP-SD) is proposed. It formulates the traditional HRRP synthetic to be a sparse approximation problem over redundant dictionary. As it employs a priori that the synthetic range profile (SRP) of targets are sparse, SRP can be accomplished even in presence of data lost. Besides, the computation complexity decreases from O(MNDK) flops for OMP to O(M(N + D)K) flops for OMP-SD by introducing sensing dictionary (SD). Simulation experiments illustrate its advantages both in additive white Gaussian noise (AWGN) and noiseless situation, respectively.
Abstract: Based on the combined shape feature and texture
feature, a fast object detection method with rotation invariant features
is proposed in this paper. A quick template matching scheme based
online learning designed for online applications is also introduced in
this paper. The experimental results have shown that the proposed
approach has the features of lower computation complexity and
higher detection rate, while keeping almost the same performance
compared to the HOG-based method, and can be more suitable for
run time applications.
Abstract: In this paper, we propose a dual version of the first
threshold ring signature scheme based on error-correcting code proposed
by Aguilar et. al in [1]. Our scheme uses an improvement of
Véron zero-knowledge identification scheme, which provide smaller
public and private key sizes and better computation complexity than
the Stern one. This scheme is secure in the random oracle model.
Abstract: Traditional parallel single string matching algorithms
are always based on PRAM computation model. Those algorithms
concentrate on the cost optimal design and the theoretical speed.
Based on the distributed string matching algorithm proposed by
CHEN, a practical distributed string matching algorithm architecture
is proposed in this paper. And also an improved single string matching
algorithm based on a variant Boyer-Moore algorithm is presented. We
implement our algorithm on the above architecture and the
experiments prove that it is really practical and efficient on distributed
memory machine. Its computation complexity is O(n/p + m), where n
is the length of the text, and m is the length of the pattern, and p is the
number of the processors.
Abstract: A novel path planning approach is presented to solve
optimal path in stochastic, time-varying networks under priori traffic
information. Most existing studies make use of dynamic programming
to find optimal path. However, those methods are proved to
be unable to obtain global optimal value, moreover, how to design
efficient algorithms is also another challenge.
This paper employs a decision theoretic framework for defining
optimal path: for a given source S and destination D in urban transit
network, we seek an S - D path of lowest expected travel time
where its link travel times are discrete random variables. To solve
deficiency caused by the methods of dynamic programming, such as
curse of dimensionality and violation of optimal principle, an integer
programming model is built to realize assignment of discrete travel
time variables to arcs. Simultaneously, pruning techniques are also
applied to reduce computation complexity in the algorithm. The final
experiments show the feasibility of the novel approach.