ORIGINAL_ARTICLE
Secret Information Steganography Using LSB Insertion Methodwithout Bit Layout Section with Increasing Substitution Rate and High Reliability
In this paper, a faster method for embedding cryptographic information in the image ispresented by expressing the concept of latent prints (Steganography). Data is encrypted bytwo methods before embedding to increase reliability. Then they are embedded into the imageby a button, a method has been expressed to reduce potential noise impact on the wayinformation is encoded.
http://jaiee.iau-ahar.ac.ir/article_513268_e3dadd7de8800b8ded291d581d34c62f.pdf
2013-03-01T11:23:20
2018-06-22T11:23:20
1
8
Substitution
Steganography
Cryptography
LSB
Farhad
Narimani
f-narimani @iau-ahar.ac.ir
true
1
AUTHOR
Mohammad Esmaeel
Akbari
makbari@tabrizu.ac.ir
true
2
AUTHOR
Hamid
Vahdati
h_vahdaty@yahoo.com
true
3
AUTHOR
[1] Rafael C. Gonzalez, Richard E. Woods
1
"Digital Image Processing", 3rd edition, 2007
2
[2] Vahid Nejat Mahboobabadi ,Vahid
3
Abdolmaleki, Majid Megdadi, " Secret
4
Information Steganography Using LSB
5
Insertion Method ", 5th National Conference of
6
Iranian command and control,Azar 1390.
7
[3] Greg kipper,"Investigator's Guide to
8
Steganography" ,Auerbach Publications, 2004
9
[4] Zhijie Shi and Ruby B. Lee, "Bit
10
Permutation Instructions for Accelerating
11
Software Cryptography", Proceedings of the
12
IEEE International Conference on Application-
13
Specific Systems, Architectures and Processors,
14
pp. 138-148, July 2000.
15
[5] Thomas H. Cormen, Charles E. Leiserson,
16
Ronald L. Rivest, “Introduction to Algorithms”,
17
The MIT Press, 3rd edition, 2009.
18
ORIGINAL_ARTICLE
Improvement of Left Ventricular Assist Device (LVAD) in Artificial Heart Using Particle Swarm Optimization
In this approach, the Left ventricular assist pump for patients with left ventricular failure isused. The failure of the left ventricle is the most common heart disease during these days. Inthis article, a State feedback controller method is used to optimize the efficiency of a samplingpump current. Particle Swarm Algorithm, which is a set of rules to update the position andvelocity, is applied to find the optimal State feedback controller parameters for the first time.In comparison to other optimization algorithms, including genetic algorithm, PSO has higherconvergence speed. As it is shown in the simulation part in the same number of iterations, thePSO algorithm decreases the cost function, which leads to desired transient and stabilityresponse of the system more effectively. In addition, in this work we propose a new structurefor the cost function which includes the dynamical equations of current sampling pump incombination with penalty sentences which decrease the speed and output fluctuations. In thisarticle, the system model and the system controlling parameters are set in such a way that theproposed cost function can be optimized. The efficiency of the method is illustrated insimulation part.
http://jaiee.iau-ahar.ac.ir/article_513269_58c7c5dd4919722b08f0abc026ad58cc.pdf
2013-03-01T11:23:20
2018-06-22T11:23:20
9
15
Current sample
optimization
Overshoot
PSO
Settling time
Stability
Majid
Neshat Yazdi
m-neshat@iau-ahar.ac.ir
true
1
AUTHOR
Reihaneh
Kardehi Moghaddam
r_k_moghaddam@mshdiau.ac.ir
true
2
AUTHOR
ORIGINAL_ARTICLE
Formation Control and Path Planning of Two Robots for Tracking a Moving Target
This paper addresses the dynamic path planning for two mobile robots in unknownenvironment with obstacle avoidance and moving target tracking. These robots must form atriangle with moving target. The algorithm is composed of two parts. The first part of thealgorithm used for formation planning of the robots and a moving target. It generates thedesired position for the robots for the next step. The second part is designed as the pathplanning for mobile robots. In this part desired trajectory of the robots for reaching thedesired position of formation is generated. The potential field method is used to pathplanning for the robots .This method enables the robot to achieve these tasks: to avoidobstacles, and to make ones way toward its new position. Finally, the effectiveness of theproposed algorithm is demonstrated through simulations.
http://jaiee.iau-ahar.ac.ir/article_513270_104eff786e5ea4fbc15f83e419089099.pdf
2013-03-01T11:23:20
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16
22
Path planning
Mobile robot
potential field
moving object tracking
[1] Tove Gustavi , Xiaoming Hu, “ Formation
1
Control for Mobile Robots with Limited
2
Sensor Information , ” IEEE, 2005, pp.
3
1791 – 1796
4
[2] ZHANG LiangSHEN PeiYi XIAO
5
xiao, “A New Method for Moving Object
6
Tracking With Multi-robot,” Journal of
7
Convergence Information Technology,
8
Volume 6, 2011, pp. 35 – 43.
9
[3] O.Hachour, “ Path planning of Autonomous
10
Mobile robot, ” INTERNATIONAL
11
JOURNAL OF SYSTEMS
12
APPLICATIONS , ENGINEERING &
13
DEVELOPMENT, Issue 4, Volume 2,
14
[4] Amit Konar , Pradipta kumar Das ,and
15
Romesh Laishram, “ Path Planning of
16
Mobile Robot in Unknown Environment, ”
17
Special Issue of IJCCT, Vol.1 , Issue 2, 3,
18
[5] Lei Tang1 ,Songyi Dian1* Gangxu Gu2,
19
Kunli Zhou1,Suihe Wang1, Xinghuan
20
Feng3, “ A Novel Potential Field Method
21
for Obstacle Avoidance and Path Planning
22
of Mobile Robot, ” IEEE,2010.
23
[6] Hassan AbuMateir, Walid Issa, “Path
24
planning of Autonomous Mobile robot New
25
Approach,” 2010-2011.
26
[7] S.S. GE , Y.J. CUI, “Dynamic Motion
27
Planning for Mobile Robots Using Potential
28
Field Method , ” Manufactured in The
29
Netherlands, 2002.
30
[8] Cao Qixin, Huang Yanwen, Zhou Jingliang,
31
“An Evolutionary Artificial Potential Field
32
Algorithm for Dynamic Path Planning of
33
Mobile Robot,” IEEE, 2006.
34
[9] VAMSIKRISHNA GOPIKRISHNA,
35
“TEMPORAL POTENTIAL FUNCTION
36
APPROACH FOR PATH PLANNING IN
37
DYNAMIC ENVIRONMENTS,” The
38
University of Texas at Arlington, 2008.
39
[10] Lu Yin, Yixin Yin, and Cheng-Jian Lin, “A
40
NEW POTENTIAL FIELD METHOD
41
FOR MOBILE ROBOT PATH
42
PLANNING IN THE DYNAMIC
43
ENVIRONMENTS,” Asian Journal of
44
Control, Vol. 11, No. 2, 2009, pp. 214-225.
45
[11] Grundel and D.A., “Searching for a moving
46
target: optimal path planning,” IEEE ,2005,
47
pp. 867 – 872
48
[12] Song Ping, Li Kejie, Han Xiaobing, Qi
49
Guangping , “ Formation and obstacleavoidance
50
control for mobile swarm robots
51
based on artificial potential field,”IEEE,
52
2009, pp. 2273 – 2277
53
[13] Qian Jia, Xingsong Wang, “Path planning
54
for mobile robots based on a modified
55
potential model,” IEEE, 2009, pp. 4946 –
56
[14] Xiaoming Hu, Karl H. Johansson, Manuel
57
Mazo Jr, Alberto Speranzon Speranzon , “
58
Multi-Robot Tracking of a Moving Object
59
Using Directional sensors”
60
2004,IEEE,vol.
61
ORIGINAL_ARTICLE
Assessment of DSACC and QPART Algorithms in Ad Hoc Networks
The rapid advancement in wireless over wired has augmented the need for improving theQuality of Service (QoS) over such wireless links. However, the wireless ad hoc networkshave too low bandwidth, and establishing a QoS in these networks is a difficult issue. So,support of quality of service in ad hoc networks is the topical issue among the networkscience researchers. In this research we are going to evaluate the performances of DSACC(Distributed Scheduling algorithm with Collision Control) and QPART (QoS protocol for Adhoc Real Time Traffic) algorithms in different conditions. These two algorithms are able tosupport quality of service in ad hoc networks. It should be noted that we have used ns-2simulator software to compare these two algorithms.
http://jaiee.iau-ahar.ac.ir/article_513271_a743c9f9a75f2343a5b5468946060df0.pdf
2013-03-01T11:23:20
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23
28
Ad Hoc
Quality of service
Scheduling
Distributed
[1] S. Lee, G.-S. Ahn, X. Zhang, and A. T.
1
Campbell. “INSIGNIA: An IP-Based
2
Quality of Service Framework for Mobile
3
Ad Hoc Networks” // Journal of Parallel
4
and Distributed Computing, Special issue
5
on Wireless and Mobile Computing and
6
Communications, 2000, vol.60, pp. 374-
7
[2] G.-S. Ahn, A. Campbell, A. Veres, and L.-
8
H. Sun. “Supporting Service
9
Differentiation for Real-Time and Best-
10
Effort Traffic in Stateless Wireless Ad Hoc
11
Networks (SWAN)” // IEEE Transactions
12
on Mobile Computing, 2002, vol.1,
13
pp.192–207.
14
[3] S. Chen and K. Nahrstedt. “Distributed
15
Quality-of-Service Routing in Ad-Hoc
16
Networks” // IEEE Journal of Selected
17
Areas in Communications, 1999, vol.17,
18
pp. 1454-1465.
19
[4] T. Chen, M. Gerla, and J. Tsai. “QoS
20
Routing Performance in a Multi-hop,
21
Wireless Network” / In Proceedings of the
22
IEEE ICUPC’97, San Diego, 1997, pp.
23
[5] P. Sinha, R. Sivakumar, and V.
24
Bharghavan. CEDAR: “A Core-Extraction
25
Distributed Ad Hoc Routing Algorithm” /
26
In Proceedings of the IEEE Conference on
27
Computer Communications (INFOCOM),
28
New York, NY, 1999, pp. 202-209.
29
[6] http://www.csd.uoc.gr/~hy439/reading/802
30
.11-1999.pdf.
31
[7] I. Ada and C. Castelluccia.
32
“Differentiation Mechanisms for IEEE
33
802.11” / In Proceedings of the IEEE
34
Conference on Computer Communications
35
(INFOCOM), Alaska, 2001, pp. 209-218.
36
[8] F. Cal´i, M. Conti, and E. Gregori.
37
“Tuning of the IEEE 802.11 Protocol to
38
Achieve a Theoretical Throughput Limit”
39
// IEEE/ACM Transactions on Networking,
40
2000, vol.8, pp. 785-799.
41
[9] T. S. Ho and K. C. Chen. “Performance
42
Evaluation and Enhancement of
43
CSMA/CA MAC Protocol for 802.11
44
[10] H. Kim and J. C. Hou. “Improving
45
Protocol Capacity with Model-based
46
Frame Scheduling in IEEE 802.11-
47
operated WLANs” / In Proceedings of the
48
Ninth Annual International Conference on
49
Mobile Computing and Networking (Mobi
50
COM’03), San Diego, 2003, pp. 190-204.
51
[11] R. Rozovsky and P. Kumar. ” A MAC
52
Protocol for Ad Hoc Networks” / In
53
Proceedings of the 2nd -ACM International
54
Symposium on Mobile Ad Hoc
55
Networking and Computing
56
(MobiHoc’01), Long Beach, CA, 2001,
57
pp. 67-75.
58
[12] A. Veres, A. T. Campbell, M. Barry, and
59
L.-H. Sun. “Supporting Service
60
Differentiation in Wireless Packet
61
Networks Using Control” // IEEE Journal
62
of Selected Areas in Communications,
63
2001, vol.19, pp. 2081-2093.
64
[13] Y. Yang, "Distributed QoS guarantees for
65
real-time traffic in ad hoc networks".
66
Sensor and Ad Hoc Communications and
67
Networks, 2004, USA, pp. 118-127.
68
[14] S.H. Hosseini Nazhad, R.M.Alguliev,
69
"Light Weight Distributed QoS Adapter in
70
Large-Scale Ad hoc Networks" // Journal
71
of American Science, 2011, vol.7, pp. 28-
72
ORIGINAL_ARTICLE
Soft Switching MBC Controller for MIMO Linear Hybrid Systems
Switching supervisory is the most important section of a feedback control process inMIMO hybrid systems. By choosing a non-compatible controller, system may go tounstable mode or high overshoot response. In this paper, a new method of switchingfor selecting MBC controllers is discussed. Results of the simulation show the MIMO(2-in 2-out) linear hybrid system can be switched stable and low overshooting inswitching time. This method can be expanded to nonlinear MIMO systems.
http://jaiee.iau-ahar.ac.ir/article_513272_f2443442264ee4fe605899249a577ab1.pdf
2013-03-01T11:23:20
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29
36
[1] Beldiman O., Bushnell L. “Stability,
1
linearization and control of switched
2
systems” Proc. American Control
3
Conference, June 1999.
4
[2] Branicky M.S. “Stability of switched
5
and hybrid systems” Proc. IEEE Conf. on
6
Decision & Control, Dec. 1994.
7
[3] Agrachev A. A., Liberzon D., “Liealgebraic
8
conditions for exponential
9
stability of switched systems” Proc.
10
IEEE Conf. on Decision & Control, Dec.
11
[4] J. Malmborg, B. Bernhardsson, and K. J.
12
ÄstrÖm. A stabilizing switching scheme
13
for multicontroller systems. In Proc. of
14
13th IFAC, pp.229–234, 1996.
15
[5] Hespanha J. P., Morse A. S. “Stability
16
of switched systems with average dwelltime”
17
Proc. IEEE Conf. on Decision &
18
Control, Dec. 1999.
19
[6] He K., Lemmon M. D. “Using
20
dynamical invariants in the analysis of
21
hybrid dynamical systems” Proc. IFAC
22
14th Triennial World Congress, 1999.
23
[7] Li Jing Lina Li ; Di Tong " A sliding
24
mode control for switched systems " 2nd
25
International Conference on Intelligent
26
Control and Information Processing
27
ORIGINAL_ARTICLE
Petri Net Modeling for Parallel Bank ATM Systems
In this paper the real time operation of an automatic teller machine (ATM) is analyzed using aTimed Petri Net (TPN) model. In the modeling, the probability of arrivals, the speed andattentiveness of customers (clients) are taken to account. Different parameters are based onthe statistical data. The model is simulated for 24 hours. The diagrams of number ofsucceeded customers, failed references to ATM, idle times of ATM and wait times ofcustomers are the outputs of TPN model.
http://jaiee.iau-ahar.ac.ir/article_513280_bf708fb3938dc0faa81ac9f815e88133.pdf
2013-03-01T11:23:20
2018-06-22T11:23:20
37
43
TPN model - ATM - Petri
[1] T. S. Staines, “ Using a Timed Petri Net
1
(TPN) to Model a Bank ATM”, IEEE
2
International Symposium and Workshop on
3
Engineering of computer Based Systems 2006
4
[2] M. Reid, W.M. Zuberek, “Timed Petri Net
5
Models of ATM LANs”, Lecture Notes In
6
Computer Science 1605, Applications of Petri
7
Nets to Communication Networks, Springer-
8
Verlag, 1999.
9
[3] M. Awad, J. Kuusela, and J. Ziegler, Objectriented
10
Technology for Real-Time Systems,
11
nglewood Cliffs, N.J.: Prentice Hall, 1996.
12
[4] J. Carlson, “Languages and Methods for
13
pecifying Real-Time Systems”, MRTC report,
14
Malardalen Real-Time Research Centre,
15
Malardalen University, Aug 2002.
16
[5] C. Capellmann, H. Dibold and U. Herzog,
17
“Using High-Level Petri Nets in the field of
18
Intelligent etworks”,Lecture Notes In
19
Computer Science 1605, Applications of Petri
20
Nets to Communication Networks, Springererlag,
21
[6] HPSIM Petri Net simulation tool, Copyright
22
(C) 1999 -2001 Henryk Anschuetz was used
23
to build the Petri Net in Figure 6.
24
ORIGINAL_ARTICLE
Rotated Unscented Kalman Filter for Two State Nonlinear Systems
In the several past years, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) havebecame basic algorithm for state-variables and parameters estimation of discrete nonlinear systems.The UKF has consistently outperformed for estimation. Sometimes least estimation error doesn't yieldwith UKF for the most nonlinear systems. In this paper, we use a new approach for a two variablestate nonlinear systems which it is called Rotated UKF (R_UKF). R_UKF can be reduced estimationerror and reached for least error in state estimation.
http://jaiee.iau-ahar.ac.ir/article_513281_cd12a128646a129d504fa22f0e70d218.pdf
2013-03-01T11:23:20
2018-06-22T11:23:20
44
48
Extended kalman filter (EKF)
unscented kalman filter (UKF)
rotated UKF (R-UKF)
[1] Simon Haykin, Kalman Filtering and
1
neural networks. Communications
2
Research Laboratory, McMaster
3
University, Hamilton, Ontario, Canada,
4
John Wiley & Sons, Inc. NewYork, ISBN
5
0-471-36998-5.
6
[2] S. J. Julier and J. K. Uhlmann, “A New
7
Extension of the Kalman Filter to
8
Nonlinear Systems,” in Proc. of Aero
9
Sense: The 11th Int. Symp. On
10
Aerospace/Defense Sensing, Simulation
11
and Controls., 1997.
12
[3] E.Wan, R. van derMerwe, and A. T.
13
Nelson, “Dual Estimation and the
14
Unscented Transformation,” in Neural
15
Information Processing Systems 12. 2000,
16
pp. 666–672, MIT Press.
17
[4] E. A. Wan and R. van der Merwe, “The
18
Unscented Kalman Filter for Nonlinear
19
Estimation,” in Proc. of IEEE Symposium
20
2000 (AS-SPCC), Lake Louise, Alberta,
21
Canada, Oct. 2000.
22
[5] S.Gannot, D. Burshtein, and E. Weinstein,
23
“Iterative and Sequential Kalman Filter-
24
Based Speech enhancement Algorithms,”
25
IEEE Trans. on Speech and Audio Proc.,
26
vol. 6, no. 4, pp. 373–385, Jul. 1998.
27
[6] J. L. Crassidis and J. L. Junkins," Optimal
28
Estimation of Dynamic Systems". Boca
29
Raton, Florida: CRC Press, to be
30
published 2004.
31
[7] S. J. Julier, “The Scaled Unscented
32
Transformation,” in Proceedings of the
33
American Control Conference, vol. 6,
34
pp. 4555–4559, 2002.
35
ORIGINAL_ARTICLE
A New Approach of Backbone Topology Design Used by Combination of GA and PSO Algorithms
A number of algorithms based on the evolutionary processing have been proposed forcommunication networks backbone such as Genetic Algorithm (GA). However, there has beenlittle work on the SWARM optimization algorithms such as Particle Swarm Optimization(PSO) for backbone topology design. In this paper, the performance of PSO on GA isdiscussed and a new algorithm as PSOGA is proposed for the network topology design. Thesimulations for specific examples show that the performance of the new algorithm is betterthan other common methods.
http://jaiee.iau-ahar.ac.ir/article_513282_d41d8cd98f00b204e9800998ecf8427e.pdf
2013-03-01T11:23:20
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49
57
backbone
cost
Genetic Algorithm
Network Topology design
PSO algorithm
SWARM intelligence
[1] A. Dutta, S. Mitra,"Integrating heuristic
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knowledge and optimization models for
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communication network design", IEEE
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Trans. Knowledge and Data Engineering ,
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Vol 5,1993 Dec, pp 999-1017.
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[2] A. Kershenbaum, p. Kermani, G. A.
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Grover,”MENTOR: An algorithm for
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mesh network topological optimization
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and routing”, IEEETrans.
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Communications , Vol 39,1991, Apr, pp
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[3] Anagnostou, G.,Ronquist, E.,Patera, A.,"A
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Design ,"Computer Method in Applied
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[4] B. A. Coan, W. E. Leland, M. P. Vechi, A.
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IEEETrans. Reliability, Vol 40 ,1991 Oct,
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pp404-416.
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[5] Baker, J,"Reducing Bias and inefficiency
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in the Selection Algorithm,"Genetic
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Algorithms and their Applications:
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Conference on Genetic Algorithms,
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Technology, .,2000 July, PPS. 14-21.
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Art , 1996 Jul, pp 64-92 Univ. of Michigan
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Spanning Tree-based Genetic Algorithm
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for Bicriteria Topological Network
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Computation , pp. 15-20,1998.
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Algorithms and Engineering
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Architecture,www.cisco.com,2000.
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Algorithm,"Toplogy Design of
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C.,NATO ASI Series, 1998, PPS. 89-102.
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