Abstract: With the fast evolution of digital data exchange, security information becomes much important in data storage and transmission. Due to the increasing use of images in industrial process, it is essential to protect the confidential image data from unauthorized access. In this paper, we analyze the Advanced Encryption Standard (AES), and we add a key stream generator (A5/1, W7) to AES to ensure improving the encryption performance; mainly for images characterised by reduced entropy. The implementation of both techniques has been realized for experimental purposes. Detailed results in terms of security analysis and implementation are given. Comparative study with traditional encryption algorithms is shown the superiority of the modified algorithm.
Abstract: In wireless sensor networks,the mobile agent technology is used in data fusion. According to the node residual energy and the results of partial integration,we design the node clustering algorithm. Optimization of mobile agent in the routing within the cluster strategy for wireless sensor networks to further reduce the amount of data transfer. Through the experiments, using mobile agents in the integration process within the cluster can be reduced the path loss in some extent.
Abstract: This work presents a multiple objective linear programming (MOLP) model based on the desirability function approach for solving the aggregate production planning (APP) decision problem upon Masud and Hwang-s model. The proposed model minimises total production costs, carrying or backordering costs and rates of change in labor levels. An industrial case demonstrates the feasibility of applying the proposed model to the APP problems with three scenarios of inventory levels. The proposed model yields an efficient compromise solution and the overall levels of DM satisfaction with the multiple combined response levels. There has been a trend to solve complex planning problems using various metaheuristics. Therefore, in this paper, the multi-objective APP problem is solved by hybrid metaheuristics of the hunting search (HuSIHSA) and firefly (FAIHSA) mechanisms on the improved harmony search algorithm. Results obtained from the solution of are then compared. It is observed that the FAIHSA can be used as a successful alternative solution mechanism for solving APP problems over three scenarios. Furthermore, the FAIHSA provides a systematic framework for facilitating the decision-making process, enabling a decision maker interactively to modify the desirability function approach and related model parameters until a good optimal solution is obtained with proper selection of control parameters when compared.
Abstract: How to coordinate the behaviors of the agents through
learning is a challenging problem within multi-agent domains.
Because of its complexity, recent work has focused on how
coordinated strategies can be learned. Here we are interested in using
reinforcement learning techniques to learn the coordinated actions of a
group of agents, without requiring explicit communication among
them. However, traditional reinforcement learning methods are based
on the assumption that the environment can be modeled as Markov
Decision Process, which usually cannot be satisfied when multiple
agents coexist in the same environment. Moreover, to effectively
coordinate each agent-s behavior so as to achieve the goal, it-s
necessary to augment the state of each agent with the information
about other existing agents. Whereas, as the number of agents in a
multiagent environment increases, the state space of each agent grows
exponentially, which will cause the combinational explosion problem.
Profit sharing is one of the reinforcement learning methods that allow
agents to learn effective behaviors from their experiences even within
non-Markovian environments. In this paper, to remedy the drawback
of the original profit sharing approach that needs much memory to
store each state-action pair during the learning process, we firstly
address a kind of on-line rational profit sharing algorithm. Then, we
integrate the advantages of modular learning architecture with on-line
rational profit sharing algorithm, and propose a new modular
reinforcement learning model. The effectiveness of the technique is
demonstrated using the pursuit problem.
Abstract: This paper presents Genetic Algorithm (GA) based
approach for the allocation of FACTS (Flexible AC Transmission
System) devices for the improvement of Power transfer capacity in an
interconnected Power System. The GA based approach is applied on
IEEE 30 BUS System. The system is reactively loaded starting from
base to 200% of base load. FACTS devices are installed in the
different locations of the power system and system performance is
noticed with and without FACTS devices. First, the locations, where
the FACTS devices to be placed is determined by calculating active
and reactive power flows in the lines. Genetic Algorithm is then
applied to find the amount of magnitudes of the FACTS devices. This
approach of GA based placement of FACTS devices is tremendous
beneficial both in terms of performance and economy is clearly
observed from the result obtained.
Abstract: This paper considers the problem of finding low cost
chip set for a minimum cost partitioning of a large logic circuits. Chip
sets are selected from a given library. Each chip in the library has a
different price, area, and I/O pin. We propose a low cost chip set
selection algorithm. Inputs to the algorithm are a netlist and a chip
information in the library. Output is a list of chip sets satisfied with
area and maximum partitioning number and it is sorted by cost. The
algorithm finds the sorted list of chip sets from minimum cost to
maximum cost. We used MCNC benchmark circuits for experiments.
The experimental results show that all of chip sets found satisfy the
multiple partitioning constraints.
Abstract: This paper proposes a new solution to string matching problem. This solution constructs an inverted list representing a string pattern to be searched for. It then uses a new algorithm to process an input string in a single pass. The preprocessing phase takes 1) time complexity O(m) 2) space complexity O(1) where m is the length of pattern. The searching phase time complexity takes 1) O(m+α ) in average case 2) O(n/m) in the best case and 3) O(n) in the worst case, where α is the number of comparing leading to mismatch and n is the length of input text.
Abstract: The widely used Total Variation de-noising algorithm can preserve sharp edge, while removing noise. However, since fixed regularization parameter over entire image, small details and textures are often lost in the process. In this paper, we propose a modified Total Variation algorithm to better preserve smaller-scaled features. This is done by allowing an adaptive regularization parameter to control the amount of de-noising in any region of image, according to relative information of local feature scale. Experimental results demonstrate the efficient of the proposed algorithm. Compared with standard Total Variation, our algorithm can better preserve smaller-scaled features and show better performance.
Abstract: Automatic face detection is a complex problem in
image processing. Many methods exist to solve this problem such as
template matching, Fisher Linear Discriminate, Neural Networks,
SVM, and MRC. Success has been achieved with each method to
varying degrees and complexities. In proposed algorithm we used
upright, frontal faces for single gray scale images with decent
resolution and under good lighting condition. In the field of face
recognition technique the single face is matched with single face
from the training dataset. The author proposed a neural network
based face detection algorithm from the photographs as well as if any
test data appears it check from the online scanned training dataset.
Experimental result shows that the algorithm detected up to 95%
accuracy for any image.
Abstract: Online trading is an alternative to conventional shopping method. People trade goods which are new or pre-owned before. However, there are times when a user is not able to search the items wanted online. This is because the items may not be posted as yet, thus ending the search. Conventional search mechanism only works by searching and matching search criteria (requirement) with data available in a particular database. This research aims to match current search requirements with future postings. This would involve the time factor in the conventional search method. A Car Matching Alert System (CMAS) prototype was developed to test the matching algorithm. When a buyer-s search returns no result, the system saves the search and the buyer will be alerted if there is a match found based on future postings. The algorithm developed is useful and as it can be applied in other search context.
Abstract: The job shop scheduling problem (JSSP) is well known as one of the most difficult combinatorial optimization problems. This paper presents a hybrid genetic algorithm for the JSSP with the objective of minimizing makespan. The efficiency of the genetic algorithm is enhanced by integrating it with a local search method. The chromosome representation of the problem is based on operations. Schedules are constructed using a procedure that generates full active schedules. In each generation, a local search heuristic based on Nowicki and Smutnicki-s neighborhood is applied to improve the solutions. The approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm.
Abstract: Software effort estimation is the process of predicting
the most realistic use of effort required to develop or maintain
software based on incomplete, uncertain and/or noisy input. Effort
estimates may be used as input to project plans, iteration plans,
budgets. There are various models like Halstead, Walston-Felix,
Bailey-Basili, Doty and GA Based models which have already used
to estimate the software effort for projects. In this study Statistical
Models, Fuzzy-GA and Neuro-Fuzzy (NF) Inference Systems are
experimented to estimate the software effort for projects. The
performances of the developed models were tested on NASA
software project datasets and results are compared with the Halstead,
Walston-Felix, Bailey-Basili, Doty and Genetic Algorithm Based
models mentioned in the literature. The result shows that the NF
Model has the lowest MMRE and RMSE values. The NF Model
shows the best results as compared with the Fuzzy-GA based hybrid
Inference System and other existing Models that are being used for
the Effort Prediction with lowest MMRE and RMSE values.
Abstract: Cyber physical system (CPS) for target tracking, military surveillance, human health monitoring, and vehicle detection all require maximizing the utility and saving the energy. Sensor selection is one of the most important parts of CPS. Sensor selection problem (SSP) is concentrating to balance the tradeoff between the number of sensors which we used and the utility which we will get. In this paper, we propose a performance constrained slide windows (PCSW) based algorithm for SSP in CPS. we present results of extensive simulations that we have carried out to test and validate the PCSW algorithms when we track a target, Experiment shows that the PCSW based algorithm improved the performance including selecting time and communication times for selecting.
Abstract: Multimedia information availability has increased
dramatically with the advent of video broadcasting on handheld
devices. But with this availability comes problems of maintaining the
security of information that is displayed in public. ISMA Encryption
and Authentication (ISMACryp) is one of the chosen technologies for
service protection in DVB-H (Digital Video Broadcasting-
Handheld), the TV system for portable handheld devices. The
ISMACryp is encoded with H.264/AVC (advanced video coding),
while leaving all structural data as it is. Two modes of ISMACryp are
available; the CTR mode (Counter type) and CBC mode (Cipher
Block Chaining) mode. Both modes of ISMACryp are based on 128-
bit AES algorithm. AES algorithms are more complex and require
larger time for execution which is not suitable for real time
application like live TV. The proposed system aims to gain a deep
understanding of video data security on multimedia technologies and
to provide security for real time video applications using selective
encryption for H.264/AVC. Five level of security proposed in this
paper based on the content of NAL unit in Baseline Constrain profile
of H.264/AVC. The selective encryption in different levels provides
encryption of intra-prediction mode, residue data, inter-prediction
mode or motion vectors only. Experimental results shown in this
paper described that fifth level which is ISMACryp provide higher
level of security with more encryption time and the one level provide
lower level of security by encrypting only motion vectors with lower
execution time without compromise on compression and quality of
visual content. This encryption scheme with compression process
with low cost, and keeps the file format unchanged with some direct
operations supported. Simulation was being carried out in Matlab.
Abstract: In this paper, the hardware implementation of the
RSA public-key cryptographic algorithm is presented. The RSA
cryptographic algorithm is depends on the computation of repeated
modular exponentials.
The Montgomery algorithm is used and modified to reduce
hardware resources and to achieve reasonable operating speed for
FPGA. An efficient architecture for modular multiplications based on
the array multiplier is proposed. We have implemented a RSA
cryptosystem based on Montgomery algorithm. As a result, it is
shown that proposed architecture contributes to small area and
reasonable speed.
Abstract: Direct search methods are evolutionary algorithms used to solve optimization problems. (DS) methods do not require any information about the gradient of the objective function at hand while searching for an optimum solution. One of such methods is Pattern Search (PS) algorithm. This paper presents a new approach based on a constrained pattern search algorithm to solve a security constrained power system economic dispatch problem (SCED). Operation of power systems demands a high degree of security to keep the system satisfactorily operating when subjected to disturbances, while and at the same time it is required to pay attention to the economic aspects. Pattern recognition technique is used first to assess dynamic security. Linear classifiers that determine the stability of electric power system are presented and added to other system stability and operational constraints. The problem is formulated as a constrained optimization problem in a way that insures a secure-economic system operation. Pattern search method is then applied to solve the constrained optimization formulation. In particular, the method is tested using one system. Simulation results of the proposed approach are compared with those reported in literature. The outcome is very encouraging and proves that pattern search (PS) is very applicable for solving security constrained power system economic dispatch problem (SCED).
Abstract: During the last years, the genomes of more and more
species have been sequenced, providing data for phylogenetic recon-
struction based on genome rearrangement measures. A main task in
all phylogenetic reconstruction algorithms is to solve the median of
three problem. Although this problem is NP-hard even for the sim-
plest distance measures, there are exact algorithms for the breakpoint
median and the reversal median that are fast enough for practical use.
In this paper, this approach is extended to the transposition median as
well as to the weighted reversal and transposition median. Although
there is no exact polynomial algorithm known even for the pairwise
distances, we will show that it is in most cases possible to solve
these problems exactly within reasonable time by using a branch and
bound algorithm.
Abstract: In this paper we present a novel approach for human
Body configuration based on the Silhouette. We propose to address
this problem under the Bayesian framework. We use an effective
Model based MCMC (Markov Chain Monte Carlo) method to solve
the configuration problem, in which the best configuration could be
defined as MAP (maximize a posteriori probability) in Bayesian
model. This model based MCMC utilizes the human body model to
drive the MCMC sampling from the solution space. It converses the
original high dimension space into a restricted sub-space constructed
by the human model and uses a hybrid sampling algorithm. We
choose an explicit human model and carefully select the likelihood
functions to represent the best configuration solution. The
experiments show that this method could get an accurate
configuration and timesaving for different human from multi-views.
Abstract: Nowadays, Gene Ontology has been used widely by many researchers for biological data mining and information retrieval, integration of biological databases, finding genes, and incorporating knowledge in the Gene Ontology for gene clustering. However, the increase in size of the Gene Ontology has caused problems in maintaining and processing them. One way to obtain their accessibility is by clustering them into fragmented groups. Clustering the Gene Ontology is a difficult combinatorial problem and can be modeled as a graph partitioning problem. Additionally, deciding the number k of clusters to use is not easily perceived and is a hard algorithmic problem. Therefore, an approach for solving the automatic clustering of the Gene Ontology is proposed by incorporating cohesion-and-coupling metric into a hybrid algorithm consisting of a genetic algorithm and a split-and-merge algorithm. Experimental results and an example of modularized Gene Ontology in RDF/XML format are given to illustrate the effectiveness of the algorithm.
Abstract: Every day human life experiences new equipments
more automatic and with more abilities. So the need for faster
processors doesn-t seem to finish. Despite new architectures and
higher frequencies, a single processor is not adequate for many
applications. Parallel processing and networks are previous solutions
for this problem. The new solution to put a network of resources on a
chip is called NOC (network on a chip). The more usual topology for
NOC is mesh topology. There are several routing algorithms suitable
for this topology such as XY, fully adaptive, etc. In this paper we
have suggested a new algorithm named Intermittent X, Y (IX/Y). We
have developed the new algorithm in simulation environment to
compare delay and power consumption with elders' algorithms.