Journal Vol – 15 No -1, January 2020

Abstract:

The Multifocus image fusion objective in visual sensor networks is to
combine the multi-focused images of the same scene into a focused fused image with
improved reliability and interpretation. However, the existing fusion methods based
on focus measures are not able to get entire focused fused image since they neglect
the diagonal neighbor pixels during the selection of the focused objects. In order to
get an image with all objects in focus a novel image fusion method using extended
spatial frequency and wavelet based focus measures in the stationary wavelet
transform domain is proposed. In our method, initially the two multi-focus source
images are transformed and decomposed as low and high-frequency sub bands by
using stationary wavelet transform. Then, each sub band is divided into equal subblocks.
Focused sub-blocks of low and high-frequency sub bands are selected by
using the extended spatial frequency and wavelet based focus measures. Lastly, the
fused image is restored by performing the inverse stationary wavelet transform on
selected sub-blocks. The performance of the proposed method is verified by carrying
out the fusion on artificial, natural and misregistered multifocus images. The results
of the proposed method are then compared with the results of existing image fusion
methods. The experimental results indicate that proposed method not only removes
artifacts in the fused image due to the shift-invariance of stationary wavelet
transform and also preserves sharp details using extended spatial frequency and
wavelet based focus measures.

Keywords:

Extended spatial frequency,focus measures,image fusion,waveletbased focus measure,

Refference:

I. Bhatnagar G, Raman B. A new image fusion technique based on directive
contrast. ELCVIA: electronic letters on computer vision and image analysis
2009;8(2):18-38.
II. Borwonwatanadelok P, Rattanapitak W, Udomhunsakul S. Multi-focus
image fusion based on stationary wavelet transform and extended spatial
frequency measurement. In: IEEE 2009 International Conference on
Electronic Computer Technology, 2009 pp. 77-81.
III. Cao L, Jin L, Tao H, Li G, Zhuang Z, Zhang Y. Multi-focus image fusion
based on spatial frequency in discrete cosine transform domain. IEEE signal
processing letters 2014; 22(2):220-224.
IV. Chen Y, Blum RS. A new automated quality assessment algorithm for
image fusion. Image and vision computing 2009; 27(10):1421-32.
V. Haghighat MB, Aghagolzadeh A, Seyedarabi H. Multi-focus image fusion
for visual sensor networks in DCT domain. Computers & Electrical
Engineering 2011; 37(5):789-97.
VI. Huang W, Jing Z. Evaluation of focus measures in multi-focus image
fusion. Pattern recognition letters 2007; 28(4):493-500.
VII. Kumar BS. Multifocus and multispectral image fusion based on pixel
significance using discrete cosine harmonic wavelet transform. Signal,
Image and Video Processing 2013; 7(6):1125-1143.
VIII. Li H, Wei S, Chai Y. Multifocus image fusion scheme based on feature
contrast in the lifting stationary wavelet domain. EURASIP Journal on
Advances in Signal Processing 2012; 2012(1):39.
IX. Li S, Kwok JT, Wang Y. Combination of images with diverse focuses using
the spatial frequency. Information fusion 2001; 2(3):169-176.
X. Li S, Yang B, Hu J. Performance comparison of different multi-resolution
transforms for image fusion. Information Fusion 2011; 12(2):74-84.
XI. Naidu VP. Image fusion technique using multi-resolution singular value
decomposition. Defence Science Journal 2011; 61(5):479-484.
XII. Nayar SK, Nakagawa Y. Shape from focus: An effective approach for
rough surfaces. In: IEEE 1990 Robotics and Automation International
Conference;Cincinnati,USA; 1990. pp. 218-225.
XIII. Paul S, Sevcenco IS, Agathoklis P. Multi-exposure and multi-focus image
fusion in gradient domain. Journal of Circuits, Systems and Computers
2016; 25(10):1650123.
XIV. Pertuz S, Puig D, Garcia MA. Analysis of focus measure operators for
shape-from-focus. Pattern Recognition 2013; 46(5):1415-1432.
XV. Petrovic VS, Xydeas CS. Gradient-based multiresolution image fusion.
IEEE Transactions on Image processing 2004; 13(2):228-237.

XVI. Pu T, Ni G. Contrast-based image fusion using the discrete wavelet
transform. Optical engineering 2000; 39(8):2075-2083.
XVII. Radha N, Babu TR. Performance evaluation of quarter shift dual tree
complex wavelet transform based multifocus image fusion using fusion
rules. International Journal of Electrical & Computer Engineering 2019;
9(2): 2377-2385.
XVIII. Sabre R, Wahyuni IS. Wavelet Decomposition in Laplacian Pyramid for
Image Fusion. International Journal of Signal Processing Systems 2016; 4
(1): pp.37-44.
XIX. Sahoo T, Mohanty S, Sahu S. Multi-focus image fusion using variance
based spatial domain and wavelet transform. In: IEEE 2011 International
Conference on Multimedia, Signal Processing and Communication
Technologies, 2011. pp. 48-51.
XX. Sharma EA, Gulati T. Performance Analysis of Unsupervised Change
Detection Methods for Remotely Sensed Images. International Journal of
Computational Intelligence Research 2017; 13(4):503-508.
XXI. Subbarao M, Tyan JK. Selecting the optimal focus measure for
autofocusing and depth-from-focus. IEEE transactions on pattern analysis
and machine intelligence 1998; 20(8):864-870.
XXII. Thelen A, Frey S, Hirsch S, Hering P. Improvements in shape-from-focus
for holographic reconstructions with regard to focus operators,
neighborhood-size, and height value interpolation. IEEE Transactions on
Image Processing 2008; 18(1):151-157.
XXIII. Vadhi R, Kilari V, Samayamantula S. Uniform based approach for image
fusion. In: Springer 2012 International Conference on Eco-friendly
Computing and Communication Systems; Berlin, Heidelberg; 2012. pp.
186-194.
XXIV. Wang WW, Shui PL, Song GX. Multifocus image fusion in wavelet
domain. In: IEEE 2003 Machine Learning and Cybernetics International
Conference; Xi’an, China; 2003. pp. 2887-2890.
XXV. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment:
from error visibility to structural similarity. IEEE transactions on image
processing 2004; 13(4):600-12.
XXVI. Xie H, Rong W, Sun L. Wavelet-based focus measure and 3-d surface
reconstruction method for microscopy images. In: 2006 IEEE/RSJ
International Conference on Intelligent Robots and Systems, 2006. pp. 229-
234.
XXVII. Xydeas CA, Petrovic V. Objective image fusion performance measure.
Electronics letters 2000; 36(4): 308-309.

XXVIII. Yang C, Zhang JQ, Wang XR, Liu X. A novel similarity based quality
metric for image fusion. Information Fusion 2008; 9(2):156-60.
XXIX. Yang J, Ma Y, Yao W, Lu WT. A Spatial domain and frequency domain
integrated approach to fusion multifocus images. The International Archives
of the Photogrammetry, Remote Sensing and Spatial Information Sciences
2008; 37(PART B7).
XXX. Yang Y, Zheng W, Huang S. Effective multifocus image fusion based on
HVS and BP neural network. The Scientific World Journal 2014.
XXXI. Zhang L, Zhang L, Mou X, Zhang D. FSIM: A feature similarity index for
image quality assessment. IEEE transactions on Image Processing 2011;
20(8):2378-86.
XXXII. Zheng Y, Essock EA, Hansen BC, Haun AM. A new metric based on
extended spatial frequency and its application to DWT based fusion
algorithms. Information Fusion 2007; 8(2):177-192.

View Download

Abstract:

In this present research work, durability studies like Sulphide attack, Acid
Attack, and Chloride Attack are studied for the lightweight fiber reinforced concrete
(LWFRC) by incorporating palm oil Shells (POS). Fiber-reinforced concrete is
achieved by introducing 0.5% ECR – Glass fibers to the volume of the concrete and it
will improve the ductility. Coarse aggregates are replacing with POS up to 50% (0,
10, 20, 30, 40 and 50) to achieve the Light Weight Concrete (LWC). To reduce the
greenhouses from cement industries, the Cement is replaced with Palm oil Fuel Ash
(POFA) and Ground Granulated Blast furnace Slag (GGBS) up to 50% (0, 10, 20,
30, 40 and 50) separately. By using all ingredients LWFRC is prepared and tested for
the chemical attacks.

Keywords:

Light Weight Concrete,Fiber Reinforced Concrete,ECR – Glass Fibers,Sulphide Attack,Chloride Attack,Magnesium Attack,GGBS,POFA,POS,Durability,

Refference:

I. Antony Godwin I, Nancy Deborah S, Julius Ponraj I, Vinslin Blessho R,
Stephen C, “Experimental Investigation on the Mechanical and
Microstructural Properties of Concrete with Agro-Waste”, International
Journal of Engineering &Technology, 7 (3.12), PP 33-37, 2018.
II. Chia Chia Thong, Delsye Ching Lee Teo, and Chee Khoon Ng, “Durability
Characteristics of Polyvinyl Alcohol–Treated Oil Palm Shell Concrete”, J.
Mater. Civ. Eng., Vol 29, Issue 10, 2017
III. DC L Teo, M A Mannan, V J Kurian, “Durability of Light Weight OPS
Concrete under Different Curing Conditions”, Materials and Structures,
2009.
IV. Erevan Serri, Mohd. Zaillian Suleiman and Md Azree Othman Mydin,
“Durability Performance of Oil Palm Shell Lightweight Concrete as
Insulation Concrete”, Applied Mechanics and Materials, Vol. 747, 2015.
V. Faiz Shaikh “Mechanical and Durability Properties of Green Star
Concretes”, Buildings, PP: 1-12, 2018.
VI. Kim Hung Mo, Fatin Amirah Mohd Anor, U. Johnson AlengaramMohd
Zamin Jumaat and K. Jagannadha Rao “Properties of metakaolin-blended oil
palm shell lightweight concrete”, European Journal of Environmental and
Civil Engineering, PP 1-17,2016.
VII. K Muthusamy, M Y Fadzil, A Z Muhammad Nazrin Akmal, S Wan Ahmad,
Z Nur Azzimah, H Mohd Hanafi and R Mohamad Hafizuddin “Effect of fly
ash content towards Sulphate resistance of oil palm shell lightweight
aggregate concrete”, IOP Conference Series: Materials Science and
Engineering, PP 1-5,2018.
VIII. M. Almograbi, “Durability study of lightweight concrete material made
from date palm seeds (DPS)”, WIT Transactions on the Built Environment,
Vol 112.
IX. Maria Mavroulidou “Mechanical Properties and Durability of Concrete with
Water Cooled Copper Slag Aggregate”, Waste Biomass Valor, 8, PP: 1841–
1854, 2017.
X. Matthew R Sherman, David B Mc Donald, Donald W Pfeifer, “Durability
Aspects of Precast Prestressed Concrete Part 1: Historical Review”, 1996.

XI. Mehdi Maghfouri, Payam Shafigh and Muhammad Aslam “Optimum Oil
Palm Shell Content as Coarse Aggregate in Concrete Based on Mechanical
and Durability Properties”, Advances in Materials Science and Engineering,
PP 1-14, 2018.
XII. Ming Kun Yew, Hilmi Bin Mahmud, Bee Chin Ang, and Ming Chian Yew,
“Effects of Oil Palm Shell Coarse Aggregate Species on High Strength
Lightweight Concrete”, ScientificWorldJourna, 2014.
XIII. Ming Kun Yew, Ming Chian Yew, Lip Huat Sa1, Siong Kang Lim, Jing
Hang Beh, Tan Ching Ng, “Enhancement of Durability Properties and
Drying Shrinkage of Heat-treated Oil Palm Shell Species High-strength
Lightweight Concrete”, Nanoscience and Nanotechnology, Volume 2, Issue
1, 2018.
XIV. Noh Irwan Ahmad, Khairulzan Yahya “The Effect of Oil Palm Shell as
Coarse Aggregate Replacement on Densities and Compressive Strength of
Concrete”.

View Download

Abstract:

In this work plasma emission spectra and atomic structure of the holmium
target by Q-switched Nd:YAG laser (1064 nm) has been studied. This work was done
theoretically and experimentally. Cowan code was used to get the emission spectra
for different transition of the holmium target. In the experimental work, the
influences of the laser pulse energy and pulse repetition rate on the emission lines
intensity of the laser induced plasma spectrum by spectroscopic technique in air
has been investigated. Three laser pulse energies (600, 700 and 800) mJ with
repetition rate (5Hz, and 20Hz) are used .The result indicate that, the emission line
intensities increase with increasing of the laser pulse energy and repetition rate. The
holmium target can give a good emission spectra in the UV region (200-400) nm
.The best emission spectra appeared when the laser pulse energy is 800mJ and 20 Hz
repetition rate at λ= 341.54nm, 342.76nm, and 345.53nm with the maximum intensity
of 80000 counts .

Keywords:

Emission spectra,pulse energy,Nd-YAG laser,holmium,

Refference:

I. A. K. Aadim, (2015). Characterization of Laser induced cadmium plasma in
air. Iraqi Journal of Science, 56(3B), 2292-2296.
II. B. Cagnac, & C. J. Pebay-Peyroula, (1975). Modern atomic physics:
fundamental principles.

III. G. Başar, N. Al-Labady, B. Özdalgiç, F. Güzelçimen, A. Er, K. I. Öztürk, & S.
Kröger, (2017). Line Identification of Atomic and Ionic Spectra of Holmium in
the Near-UV. II. Spectra of Ho ii and Ho iii. The Astrophysical Journal
Supplement Series, 228(2), 17.
IV. G. M.Kompitsas, F.Roubani-Kalantzopoulou, I. Bassiotis, A. Diamantopoulou,
& A. Giannoudakos, (2000). Laser induced plasma spectroscopy (LIPS) as an
efficient method for elemental analysis of environmental samples.
V. G. Nave, (2003). Atomic transition rates for neutral holmium (Ho I). JOSA
B, 20(10), 2193-2202.
VI. H. G. Jihad, & A. K.Aadim, K (2018). Spectroscopic study the plasma
parameters for Pb doped CuO prepared by pulse Nd: YAG laser deposition. Iraqi
Journal of Physics, 16(38), 1-9.
VII. H. H. Murbat, & A. H. Hamza, (2017) The Influence of Nd: YAG Laser Energy
on Plasma Characteristics Produced on Si: Al Alloy Target in Atmosph
VIII. J. Gurell, M. G. Wahlgren, G. Nave, & F. J. Wyart, (2009). Wavelengths, energy
levels and hyperfine structure constants in Ho ii. Physica Scripta, 79(3), 035306.
IX. L.Fechner, (2016). High-Resolution Experiments on Strong-Field Ionization of
Atoms and Molecules: Test of Tunneling Theory, the Role of Doubly Excited
States, and Channel-Selective Electron Spectra. Springer.
X. M.Corsi, G.Cristoforetti, M.Hidalgo, S. Legnaioli, V.Palleschi, A.Salvetti, & C.
Vallebona, (2006). Double pulse, calibration-free laser-induced breakdown
spectroscopy: a new technique for in situ standard-less analysis of polluted
soils. Applied Geochemistry, 21(5), 748-755.
XI. N.Al-Labady, B. Özdalgiç, A. Er, F. Güzelçimen, K. I. Öztürk, S. Kröger & G.
Başar, (2017). Line identification of atomic and ionic spectra of holmium in the
near-UV. Part I. spectrum of Ho I. The Astrophysical Journal Supplement
Series, 228(2), 16.
XII. N. S. Mazhir, A. N. Abdullah, F. A. Rauuf, H. A. Ali, & I. H.al-Ahmed, (2018).
Effects of Gas Flow on Spectral Properties of Plasma Jet Induced by
Microwave. Baghdad Science Journal, 15(1), 81-86.
XIII. P. A. Rodgers, A. M. Rogoyski, S. J. Rose, Rutherford Appleton Laboratory
Report, RAL-89-127, Dec.1989.
XIV. R. d’Agostino, P. Favia, C. Oeh, & R. M.Wertheimer, (2005). Low‐temperature
plasma processing of materials: past, present, and future. Plasma Processes and
Polymers, 2(1), 7-15.
XV. R. D. Cowan, J. of Optical Society of America, 58 (1968) 808-818.
XVI. S. M. Mahmoad, (2018). The emission spectra and hydrodynamic properties of
Al plasma using Nd-YAG laser. Iraqi Journal of Physics (IJP), 16(38), 83-98

View Download

Abstract:

Accuracy in spectrum sensing is very much required in cognitive radio network,
which is a revolutionary paradigm to drift the spectrum underutilization problem. To
enhance the detection performance in presence of shadowing or fading multiple SUs
cooperate among themselves. But the collaboration and so the detection process is
severely affected by the presence of some harmful secondary users known as
Malicious users. As a result of this false sensing, spectrum wastage or interference
with primary users may happen which are not at all desired for the system. The
proposed approach in this paper has intelligently excluded these malicious users
from the decision making process and thus improves the efficiency of the system.

Keywords:

Cognitive radio,fusion rules,cooperative spectrum sensing,quantized fusion rule,

Refference:

I. A. Ghasemi and E. S. Sousa, “Collaborative spectrum sensing for opportunistic access in fading environments,” in Proceedings of the 1st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN ’05), pp. 131– 136, November 2005

II. A. Ghasemi and E. S. Sousa, “Opportunistic spectrum access in fading channels through collaborative sensing,” Journal of Communications, vol. 2,no. 2, pp. 71–82, 2007
III. A. Ghasemi, & E. S. Sousa, “Spectrum sensing in cognitive radio networks: the cooperation-processing tradeoff”, Wireless Communications and Mobile Computing, 7(9), 1049-1060, 2007
IV. A. Chakraborty, and J.S. Banerjee, “An Advance Q Learning (AQL) Approach for Path Planning and Obstacle Avoidance of a Mobile Robot”. International Journal of Intelligent Mechatronics and Robotics, 3(1), pp 53-73
2013
V. A. Chakraborty, J. S. Banerjee, and A. Chattopadhyay, “Non-Uniform Quantized Data Fusion Rule Alleviating Control Channel Overhead for Cooperative Spectrum Sensing in Cognitive Radio Networks”. In: Proc. IACC, pp 210-215 2017
VI. A. Chakraborty, J. S. Banerjee, and A. Chattopadhyay, “Non-uniform quantized data fusion rule for data rate saving and reducing control channel overhead for cooperative spectrum sensing in cognitive radio networks”, Wireless Personal Communications, Springer, 104(2), 837-851, 2019
VII. D. Das, et. al., “Analysis of Implementation Factors of 3D Printer: The Key Enabling Technology for making Prototypes of the Engineering Design and Manufacturing”, International Journal of Computer Applications, pp.8-14, 2017
VIII. E. Hossain, D. Niyato, and Z. Han, “Dynamic Spectrum Access in Cognitive Radio Networks”, Cambridge University Press, Cambridge, UK, 2008 IX. E. Visotsky, S. Ku ffher, and R. Peterson, “On collaborative detection of TV transmissions in support of dynamic spectrum sharing”, in Proceedings of the 1st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN ’05), pp. 338–345, Baltimore, USA, November 2005
X. F. Akyildiz, B. F. Lo, and R. Balakrishnan, “Cooperative spectrum sensing in cognitive radio networks: A survey”, Physical Communication (Elsevier) Journal, vol. 4, no. 1, pp. 40-62, March. 2011

XI. I. Pandey, et. al., “WBAN: A Smart Approach to Next Generation ehealthcare System”, In 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), pp. 344-349, IEEE, 2019

XII. J. Mitola III and G. Q. Maguire Jr., “Cognitive radio: making software radios more personal”, IEEE Personal Communications, vol. 6, no. 4, pp. 13–18, 1999
XIII. J. Mitola III, “Cognitive Radio— An Integrated Agent Architecture for Software Defined Radio”.Royal Institute of Technology, 2000
XIV. J. S. Banerjee, A. Chakraborty, and A. Chattopadhyay, “Relay node selection using analytical hierarchy process (AHP) for secondary transmission in multi-user cooperative cognitive radio systems”, in Proc. ETAEERE 2016,
LNEE-Springer, Dec. 2016
XV. J. S. Banerjee, A. Chakraborty, and A. Chattopadhyay, “Fuzzy based relay selection for secondary transmission in cooperative cognitive radio networks”, in Proc. OPTRONIX 2016, Springer, India, Aug. 2016
XVI. J. S. Banerjee, et. al., “A Comparative Study on Cognitive Radio Implementation Issues”, International Journal of Computer Applications, vol.45, no.15, pp. 44-51, May.2012
XVII. J. S. Banerjee, A. Chakraborty, and A. Chattopadhyay, “Reliable best-relay selection for secondary transmission in co-operation based cognitive radio systems: A multi-criteria approach”, Journal of Mechanics of Continua and
Mathematical Sciences, 13(2), 24-42, 2018
XVIII. J. S. Banerjee, and A. Chakraborty, “Fundamentals of Software Defined Radio and Cooperative Spectrum Sensing: A Step Ahead of Cognitive Radio Networks”. In Handbook of Research on Software-Defined and Cognitive Radio Technologies for Dynamic Spectrum Management, IGI Global, pp 499-543 2015
XIX. J. S. Banerjee, A. Chakraborty, and K. Karmakar, “Architecture of Cognitive Radio Networks”. In N. Meghanathan & Y.B.Reddy (Ed.), Cognitive Radio Technology Applications for Wireless and Mobile Ad Hoc Networks, IGI
Global, pp 125-152 2013
XX. J. S. Banerjee, and A. Chakraborty, “Modeling of Software Defined Radio Architecture & Cognitive Radio, the Next Generation Dynamic and Smart Spectrum Access Technology”. In M.H. Rehmani & Y. Faheem (Ed.),
Cognitive Radio Sensor Networks: Applications, Architectures, and Challenges, IGI Global, pp. 127-158 2014
XXI. J. Banerjee, et. al., “Impact of machine learning in various network security applications”, In 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), pp. 276-281, IEEE, 2019
XXII. J. S. Banerjee, et. al., “A Survey on Agri-Crisis in India Based on Engineering Aspects”, Int. J. of Data Modeling and Knowledge Management, 3(1–2), pp.71-76, 2013
XXIII. J. S. Banerjee, A. Chakraborty, and A. Chattopadhyay, “A novel best relay selection protocol for cooperative cognitive radio systems using fuzzy AHP”, Journal of Mechanics of Continua and Mathematical Sciences, 13(2), 72-87,
2018
XXIV. K. B. Letaief and W. Zhang, “Cooperative spectrum sensing”, in Cognitive Wireless Communication Networks, Springer, New York, NY, USA, 2007

XXV. Laneman, J. N., et al. “Cooperative diversity in wireless networks: Efficient protocols and outage behavior”, IEEE Trans. Inform. Theory, 50(12), pp.3062-3080, 2004
XXVI. M. K. Simon, & M. S. Alouini, [Digital communication over fading channels]. John Wiley & Sons, Hoboken, NJ, Vol. 95, 2005

XXVII. O. Saha; A. Chakraborty, and J. S. Banerjee, “A Decision Framework of ITBased Stream Selection Using Analytical Hierarchy Process (AHP) for Admission in Technical Institutions”, In: Proc. OPTRONIX 2017, IEEE, pp.
1-6, Nov. 2017
XXVIII. O. Saha; A. Chakraborty, and J. S. Banerjee, “A Fuzzy AHP Approach to ITBased Stream Selection for Admission in Technical Institutions in India”, In: Proc. IEMIS, AISC-Springer, pp. 847-858, 2019

XXIX. R. Chen, J.-M. Park, and J. H. Reed, “Defense against primary user emulation attacks in cognitive radio networks”, IEEE Journalon Selected Areas in Communications,vol.26,no.1,pp. 25–37, 2008
XXX. R. Chen, J.-M. Park, and K. Bian, “Robust distributed spectrum sensing in cognitive radio networks”, in Proceedings of IEEE International Conference on Computer Communications (INFOCOM ’08), pp. 31–35, Phoenix, Ariz,
USA, April 2008

XXXI. S. Haykin, “Cognitive radio: brain-empowered wireless communications”, IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201–220, 2005
XXXII. S. M. Mishra, A. Sahai, and R. W. Brodersen, “Cooperative sensing among cognitive radios”, in Proceedings of the IEEE International Conference on Communications (ICC ’06), vol. 4, pp. 1658–1663, Istanbul, Turkey, June 2006
XXXIII. S. Paul, A. Chakraborty, and J. S. Banerjee, “A Fuzzy AHP-Based Relay Node Selection Protocol for Wireless Body Area Networks (WBAN)”, In: Proc. OPTRONIX 2017, IEEE, pp. 1-6, Nov. 2017
XXXIV. S. Paul, A. Chakraborty, and J. S. Banerjee, “The Extent Analysis Based Fuzzy AHP Approach for Relay Selection in WBAN”, In: Proc. CISC, (pp.331-341). Springer, Singapore, 2019
XXXV. T. Newman and T. Clancy, “Security threats to cognitive radio signal classifiers”, in Proceedings of the Virginia Tech Wireless Personal Communications Symposium, Blacksburg, USA, June 2009
XXXVI. T. Clancy and N. Goergen, “Security in cognitive radio networks: threats and mitigation”, in Proceedings of the 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (Crown Com ’08), Singapore, May 2008
XXXVII. Z. Han and K. J. R. Liu, “Resource Allocation for Wireless Networks: Basics, Techniques, and pplications”, Cambridge University Press, Cambridge, UK, 2008

View Download

Abstract:

This paper proposes the design strategy of low power Dickson charge pump
using associated block who is help in proper functionality at full chip level because of
single charge circuit cannot be design for portable handheld based application. When
a low power optimization based high speed charge pump circuit is designed at system
level, then entire circuit block operates at different supply voltage so it requires. For
this, the circuit designer needs a level shifter to manage the dual supply voltage and
provide a non-overlapping ring oscillator to provide the clock to the circuit to
operate at high speed. CMOS clocked circuit is required to work in sufficient voltage
level pushup up to end level. Thus, In this paper, the actual simulation results using
the CMOS 180nm technology along with each block are shown. Along with this, good
brief discussion on each block has also been done.

Keywords:

Charge Pump,Ring Oscillator,NOC clock generator,clocked D-FF,CMLS level shifter,

Refference:

I. Louie Pylarinos. “Proceedings of the IEEE International Symposium on
Circuits andSystems.1-7.citeseerx.ist.psu.edu/viewdoc/download
http://citeseerx.ist.psu.edu/viewdoc/versions?doi=10.1.1.128.4085, 2003
II. Feng pan and Tapan Samaddar, “Charge pump circuit
designconc”,McGraw-Hill Electronic Engineering :ISBN-13: 978-
007147045, 2006.
III. Moisiadisa Y, Bourasc I, and A. Arapoyanni, “Charge Pump Circuits for
Low-voltage Applications”, Taylor and Francis VLSI Design. Vol 15, pp:
477–483, 2002
IV. Wu, J.T. and L.K. Chang, “MOS Charge pump for Low Voltage
Operation”,.IEEE Journal of solid-state circuits.vol 33 pp: 592-597,1998
V. Dickson, J.F. On-chip high voltage generation in NMOS integrated circuits
using an improved voltage multiplier technique. IEEE journal of Solid-State
circuits, vol 11, pp: 374-378, 1976
VI. G. Palumbo, D. Pappalardo, M. Gaibotti, “Charge Pump Circuits- Power
Consumption Optimization”, IEEE Trans. Circuits and Systems I:
Fundamental Theory and Applications. Vol 49, pp: 1535–1542, 2002

VII. R. L. Geiger, P. E. Allen, and N. R. Strader, “VLSI-Design Techniques for
Analog and Digital Circuits”, McGraw-Hill Publishing Co. ISBN 0-07-
023253-9, 1990
VIII. Prabhakar Gyan., Singh R.K., Vikram A, “Boosted Clock Generator Using
NAND Gate for Dickson Charge Pump Circuit”,Smart Innovation, Systems
and Technologies(Springer, Singapore.), vol 2(107), pp: 39-49,2019
IX. Jacob Baker, Harry W. Li and David E. Boyce, CMOS Circuit Design,
Layout, and Simulation Department of Electrical Engineering
Microelectronics Research Center the University of Idaho.
X. Retdian, N., Takagi, S. and Fujii, N, “Voltage controlled ring oscillator with
wide tuning range and fast voltage swing IEEE Asia- Pacific Conference”,
ASIC on. Proceedings: pp: 201-204. 2002
XI. Yongbo Liua, b, Zhengyong Zhua, Huilong Zhua, “Charge pumps, test
technique using CMOS ring oscillator on the leakage issue”,
Microelectronics Journal vol 68, pp: 40–45, 2017
XII. Vladimir Stojanovic and Vojin G.(1999).„Comparative Analysis of Master–
Slave Latches and Flip-Flops for High-Performance and Low-Power
Systems. IEEE Journal of Solid-State Circuits. 34(4):536-548, 1999
XIII. http://ece-research.unm.edu/jimp/vlsi/slides/chap5_2.html
XIV. G. Prabhakar, A. Vikram , Rajendra Pratap, R.K. Singh, “Current
contention Problem in Level shifter”,International Journal of Recent
Technology and Engineering, Vol 8, pp: 11699-11703, 2019
XV. Mohammad Torikul Islam Badal, Mamun Bin Ibne Reaz, Araf Farayez, Siti
A. B. Ramli, and Noorfazila Kamal., “Design of a Low-power CMOS Level
Shifter for Low-delay SoCs in Silterra 0.13 μm CMOS Process”, Journal of
Engineering Science and Technology Review , vol 4 pp: 10-15, 2017
XVI. Prabhakar Gyan, Rabindra Kumar Singh and Abhishek Vikram, “Improved
Efficiency and Voltage Gain Conversion Ratio using Inductor Model based
modified Dickson Charge Pump”, Journal of Engineering Science and
Technology review.vol 11,pp: 1-7.2018.
XVII. Prabhakar Gyan, Rabindra Kumar Singh and Abhishek Vikram, “Inductor-
Based Modified Dickson Charge Pump Boost Voltage Converter with
Higher Efficiency”,. Information and Communication Technology for
Intelligent Systems, Smart Innovation, Systems and Technologies Vol
2(107): pp: 39-49, 2019
XVIII. P. R. Gray and R. G. Meyer,“Analysis and Design of Analog Integrated
Circuits”, 3rd ed., John Wiley and Sons.. ISBN 0-471-57495-3, 1993

View Download

Abstract:

This work studies the trajectory tracking of a non-holonomic WMR. A type of
back stepping method in conjunction with Lyapunov method were used for deriving
two controllers. But, in non-linear systems controllers may not be enough to reach a
good performance. Different cases of trajectory where studied such as (straight line,
circular, elliptical, sinusoidal, and infinity shape trajectory) to examine the WMR
control system utilizing MATLAB (R2018a)/Simulink to simulate the results. In
addition, particle swarm optimization technique was utilized to determine the
controllers’ parameters by implementing the summation absolute compound error for
the position (x, y), the orientation ?, the linear and angular velocity (??,?? ), and the
energy. Results showed a very good matching between simulation and the desired
trajectory where all errors converge to zero.

Keywords:

Mobile robot,Nonholonomic,DDWMR,Optimum,PSO,control,

Refference:

I. A. Sofi, B. R. Phanikumar, “An experimental investigation on flexural
behaviour of fibre-reinforced pond ash-modified concrete”, Ain Shams
Engineering Journal. vol. 6, pp: 1133-1142, 2015.
II. Fareh R, Saad M, Khadraoui S, and Rabie T 2016. Lyapunov-based
tracking control for nonholonomic wheeled mobile robot. J. International
Journal of Electrical and Computer Engineering. V10. PP1042-1047.
III. García-Sánchez J, et al 2017 Mexico. Tracking control for mobile robots
considering the dynamics of all their subsystems: experimental
implementation.P1-18.
IV. Hadi N 2005 Iraq. Fuzzy control of mobile robot in slippery environment.
J. Al-Khwarizmi Engineering Journal. V1.pp41-51.Bakharev T.,
“Durability of geopolymer materials in sodium and magnesium sulfate
solutions”, Cement and Concrete Research. vol. 35, no. 6, pp: 1233-1246,
2005

V. Han P. et al 2016 USA. “Path planning for a mobile robot using ant colony
optimization and the influence of critical obstacle”. C. International
Conference on Industrial Engineering and Operations Management Detroit.
VI. Klancar G. et al. 2017 UK, USA. B. “WHEELED MOBILE ROBOTICS”
VII. Leenaa N. and Sajub K.K. 2016 India. “Modelling and trajectory tracking
of wheeled mobile robots”. J. Procedia Technology. V24. PP538 – 545.
VIII. Maghenem M. et al 2017 France. “Global tracking-stabilization control of
mobile robots with parametric uncertainty”. J. International Federation of
Automatic Control. V50. PP4114–4119.
IX. Nurmaini S. et al 2017. “Differential-Drive Mobile Robot Control Design
based-on Linear Feedback Control Law”. C. IAES International Conference
on Electrical Engineering, Computer Science and Informatics. S755. PP1-7
X. Obaid M, Husain A, Al-Kubati A 2016. Robust backstepping tracking
control of mobile Robot Based on Nonlinear Disturbance Observer. J.
International Journal of Electrical and computer engineering (IJECE) .V6.
PP901-908.
XI. Pandey A. and Parhi D. R. 2017 India. “Optimum path planning of mobile
robot in unknown static and dynamic environments using Fuzzy-Wind
Driven Optimization algorithm”. J. Defence Technology. V13. PP47-58.
XII. Saleh A. L. et al, 2018 Iraq. “Design fuzzy neural petri net controller for
trajectory tracking control of mobile robot”. J. International Journal of
Engineering & Technology. V7. PP1-13
XIII. Shih C, and Lin L 2017 Taiwan. Trajectory planning and tracking control
of differential drive mobile robot in a picture drawing application.P1-15.
XIV. Tawfik M, Abdulwahb E, and Swadi S 2014 Iraq. Trajectory tracking
control for a wheeled mobile robot using fractional order PID controller. J.
Al-Khwarizmi Engineering Journal. V10. PP39- 52
XV. Tzafestas S 2014 Greece. B. Introduction to mobile robot control.
XVI. Younus K. K. and Hadi N. H. 2019 Iraq. “Path tracking and backstepping
control for a wheeled mobile robot (WMR) in a slipping environment”. C.
3,d International Conference on Engineering Sciences: ICES

View Download

Abstract:

This paper proposes a novel precoding method using the pruned DFT
(pDFT) spread FBMC along with the Poly-phase network (PPN). This method
outperforms the pruned DFT spread FBMC in many aspects and also avoids Inter
symbol Interference. This technique has advantages of both FBMC-Offset Quadrature
amplitude modulation (OQAM) and Single carrier Frequency division multiple
access (SC-FDMA).Proposed technique has same PAPR as SC-FDMA and has very
low out-of-band emissions and does not need cyclic-prefix. This method reduces
latency, computational complexity and complex orthogonality is restored. A
comparative performance is also evaluated between pDFT-FBMC PPN and other
multicarrier schemes and we observe that pDFT-FBMC PPN is better than other
schemes. Simulation is performed by using Matlab.

Keywords:

FBMC,Poly-phase network,FBMC-OQAM,

Refference:

I. K.Mahender,T.Anilkumar, K.S.Ramesh, “AN EFFICIENT FBMC BASED
MODULATION FOR FUTUREWIRELESS COMMUNICATIONS”,ARPN
Journal of engineering and applied science, ISSN 1819-6608,vol
13,no.24,DEC-2018
II. K.Mahender, T. Anilkumar, K.S.Ramesh, “PAPR analysis of fifth generation
multiple access waveforms for advanced wireless
communication”,International journal of engineering and technology, Vol
7,No.(3.34) (2018) 487-490
III. K.Mahender, T. Anilkumar, K.S. Ramesh, “An Efficient OFDM system with
reduced PAPR for combating multipath fading”, Journal of advanced
research in dynamical and control systems.9: 1939-1948.
IV. K.Mahender, T. Anilkumar, K.S. Ramesh, “SER and BER Performance
analysis of digital modulation scheme over multipath fading channel”,Journal
of Advanced Research in Dynamical and Control Systems, vol 9,issue 2,pp
287-291
V. K.Mahender, T. Anilkumar, K.S. Ramesh, “Analysis of Multipath Channel
Fading Techniques in Wireless Communication systems”, American Institute
of Physics,AIP Conference Proceedings1952, 020050; doi:
10.1063/1.5032012.
VI. K.Mahender, T.Anilkumar, K.S. Ramesh. “Simple Transmit Diversity
Techniques for Wireless Communications”, Smart Innovations in
Communication and Computational Sciences, Advances in Intelligent
Systemsand Computing 669, https://doi.org/10.1007/978-981-10-8968-8_28,
pp. 329-342,2019
VII. K.Mahender, T. Anilkumar, K.S. Ramesh, “Performance study of OFDM over
Multipath Fading channels for next Wireless communications”, International
journal of applied engineering research , ISSN 0973-4562, 12(20): 10205-
10210.
VIII. R.Nisseland,M.Rupp,“PrunedDFTspreadFBMC-OQAM:Low-
PAPR,lowlatency, highspectralefficiency,”Submitted to IEEE
TransactionsonCommunications,2018.
IX. R. Nissel, J. Blumenstein, and M. Rupp, “Block frequency spreading: A
method for low-complexityMIMOinFBMC-OQAM,”in IEEE Workshop on
Signal Processing Advances in Wireless Communications (SPAWC),
Hokkaido, Japan, July2017.
X. R. Nissel, M. Rupp, and R. Marsalek, “FBMC-OQAM in doubly-selective
channels: A new perspective on MMSE equalization,” in IEEE Workshop on
Signal Processing Advances in Wireless Communications (SPAWC),
Hokkaido, Japan, July2017.
XI. R. Nissel, E. Zöchmann, and M. Rupp, “On the influence of doublyselectivity
in pilot-aided channel estimation for FBMC-OQAM,” in IEEE
Vehicular Technology Conference (VTC Spring), Sydney, Australia,
June2017.

View Download

Abstract:

India is a country of having versatile language and culture. Here, people
speak in 22 different languages. With the help of Google Indic keyboard people can
express their sentiments about any product, news, incidents, laws, games etc. over the
social media in their native languages from individual smart phones, tablets or
laptops. Sentiment analysis (SA) itself is a tough job, while multilingual SA is even
harder as the style of expression varies in different languages. Among the existing
approaches of SA till now the machine learning approach through neural network
has overcome the limitations of others. The main aim of this paper is to represent a
detailed study of the outputs generated from three different models implemented using
Convolution Neural Network(CNN), Simple Recurrent Neural Network(RNN) and an
amalgamated model of CNN and Long Short Term Memory (LSTM) without worrying
about versatility of multilingualism using 2600 sample reviews in Hindi and Bengali.
It is anticipated that the experimental results on these realistic reviews will prove to
be effective for further research work.

Keywords:

Machine learning,Neural Network,Sentiment Analysis,Multilingual Tweets,

Refference:

I. A. B. Goldberg, and X. Zhu, “Seeing stars when there aren’t many stars:
graph-based semi- supervised learning for sentiment categorization” In
Proceedings of the First Workshop on Graph Based Methods for Natural
Language Processing, Association for Computational Linguistics, pp. 45-
52,2006.
A. Hassan, N. Mohammed, and A. K. A. Azad, “Sentiment analysis on bangle and
romanized bangla text (BRBT) using deep recurrent models,” Computing Research
Repository (CoRR), vol. bs/1610.00369, 2016. [Online]. Available:
http://arxiv.org/abs/1610.00369.
A. Trivedi, A. Srivastava, I. Singh, K. Singh, and S. K. Gupta, “Literature Survey on
Design and Implementation of Processing Model for Polarity Identification on
Textual Data of English.” IJCSI,2011.
B. G. Patra, D. Das,, A. Das, and R. Prasath, “Shared task on sentiment analysis in
indian languages (sail) tweets-an overview”, In International Conference on Mining
Intelligence and Knowledge xploration, pp. 650-655,Springer International
Publishing, December, 2015.
B. Pang, and L. Lee, “Opinion Mining and sentiment analysis,”, Foundations and
trends in information retrieval, 2(1-2), 1-135, 2008
B. Pang, L. Lee, “A sentimental education: Sentiment analysis using subjectivity
summarization based on minimum cuts” In Proceedings of the 42nd annual meeting
on Association for Computational Linguistics (p. 271),2004.
J. Mech. Cont.& Math. Sci., Vol.-15, No.-1, January (2020) pp 108-117
Copyright reserved © J. Mech. Cont.& Math. Sci.
Koyel Chakraborty et al
116
B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up?: sentiment classification using
machine learning techniques”. In Proceedings of the ACL-02 conference on
Empirical methods in natural language processing, Association for Computational
Linguistics, Vol. 10, pp. 79- 86,2002
Bullinaria, J.A., “Recurrent neural networks”. Neural Computation: Lecture, 2013.12.
E. Riloff, and J. Wiebe “Learning extraction patterns for subjective expressions” In
Proceedings of the 2003 conference on Empirical methods in natural language
processing, Association for Computational Linguistics, pp. 105-112,2003.
F. Zhu, and X. Zhang, “Impact of online consumer reviews on sales: The moderating
role of product and consumer characteristics” Journal of Marketing, 74(2), 133-
148,2010.
Gers, F.A., J. Schmidhuber, and F. Cummins, “Learning to forget: Continual
prediction with LSTM”. Neural computation, 2000.12(10): p.2451-2471.
Hochreiter, S. and J. Schmidhuber, “Long short-term memory”. Neural
computation,1997. 9(8): p. 735-1780.22.
J. Kim, G. Chern, D. Feng, E. Shaw, and E Hovy, “Mining and assessing discussions
on the web through speech act analysis” In Proceedings of the Workshop on Web
Content Mining with Human Language Technologies at the 5th International
Semantic Web Conference,2006.
J. Zhao, K. Liu, and G. Wang “Adding redundant features for CRFs based sentence
sentiment classification” In Proceedings of the conference on empirical methods in
natural language processing Association for Computational Linguistics, pp. 117-
126,2008.
K. Dave, S. Lawrence, and D. M. Pennock, “Mining the peanut gallery: Opinion
extraction and semantic classification of product reviews” In Proceedings of the 12th
international conference on World Wide Web, ACM, pp. 519-528,2003.
Kamal Sarkar, “Sentiment Polarity Detection in Bengali Tweets Using Deep
Convolutional Neural Networks”, https://doi.org/10.1515/jisys-2017-0418.
K Sarkar, M Bhowmick, “Sentiment polarity detection in bengali tweets using
multinomial Naïve Bayes and support vector machines”, – 2017 IEEE Calcutta
Conference (CALCON), 2017.
L-W Ku, Y.T. Liang, and H-H Chen, “Opinion Extraction,Summarization and
Tracking in News and Blog Corpora” In AAAI Spring Symposium: Computational
Approaches to AnalyzingWeblogs,2006.
M. Wiegand, A. Balahur, B. Roth, D. Klakow, and A. Montoyo, “A survey on the
role of negation in sentiment analysis” In Proceedings of the workshop on negation
J. Mech. Cont.& Math. Sci., Vol.-15, No.-1, January (2020) pp 108-117
Copyright reserved © J. Mech. Cont.& Math. Sci.
Koyel Chakraborty et al
117
and speculation in natural language processing, Association for Computational
Linguistics, pp. 60-68, 2010.
Q. Miao, Q. Li, and D. Zeng, “Fine grained opinion mining by integrating multiple
review sources” Journal of the American society for information science and
technology, 61(11), 2288-2299,2010.
Rajaram, Santhoshkumar & Geetha, M.. (2019). “Deep Learning Approach for
Emotion Recognition from Human Body Movements with Feed forward Deep
Convolution Neural Networks”. Procedia Computer Science. 152. 158-165.
10.1016/j.procs.2019.05.038.
R. Narayanan, B. Liu, and A. Choudhary, “Sentiment analysis of conditional
sentences” In Proceedings of the 2009 Conference on Empirical Methods in Natural
Language Processing, Association for Computational Linguistics, Vol. 1, pp. 180-
189,2009.
R. Prabowo, & M. Thelwall, “Sentiment analysis: A combined approach” Journal of
Informetrics, 3(2), 143-157, 2009.
R. Socher, A. Perelygin, J. Y. Wu, J. Chuang, C. D. Manning, A. Y. Ng and C. Potts,
“Recursive deep models for semantic compositionality over a sentiment Treebank”,
in: Proceedings of the Conference on Empirical Methods in Natural
LanguageProcessing (EMNLP), vol. 1631, p. 1642, ACL, Seattle, USA,2013.
S. Chowdhury and W. Chowdhury, “Performing sentiment analysis in bangla
microblog posts,” 2014 International Conference on Informatics, Electronics &
Vision (ICIEV), vol. 00, pp. 1–6,2014.
T. Joachims, “Making large scale SVM learning practical”,1999.
T. Mullen, and N. Collier, “Sentiment Analysis using Support Vector Machines with
Diverse Information Sources” In EMNLP, Vol. 4, pp.412-418, 2004.
Y. Kim, “Convolutional neural networks for sentence classification,” in the
Proceedings of the 2014 Conference on Empirical Methods in Natural Language
Processing, pp. 1746– 1751,2014.
Y. Zhang and B. C. Wallace, “A sensitivity analysis of (and practitioners’ guide to)
convolutional neural networks for sentence classification,” Computing Research
Repository (CoRR), vol. abs/1510.03820, 2015. [Online]. Available:
http://arxiv.org/abs/1510.03820.

View Download

Abstract:

It has been observed that the extreme rainfall is anunusual and very essential
hydrological parameter therefore probabilistic modeling is important for the analysis of
such extreme weather events. Extreme rainfall analysis has much importance for a civil
engineer and planning division of a country to take into account the capability of
building structures for extreme weather conditions. To understand the extreme behavior
of Khyber Pakhtunkhwa we use yearly maximum rainfall of four major cities of this
province from 1960 to 2010. In this study, we have estimated the parameters of
Generalized Extreme Value (GEV) distribution by using Bayesian approach. The Akaike
Information Criteria and Acceptance Rate are used to check the reliability of the model.
After getting ensured the reliability we find return levels against different return periods
(10, 25, 50, 75 and 100 years) of Meteorological stations Peshawar, Dir, Parachinar
and D I Khan of KPK province of Pakistan. Our result will be useful for policy makers,
civil engineers, planning division, agricultural departments and research scholars,
formers for irrigation system and civil society of KPK, Pakistan for precautionary
measures.

Keywords:

Extreme Rainfall,Bayesian approach,Return period,Return levels,

Refference:

I. Ali, M., and M. J. Iqbal.: “A probabilistic approach for estimating return periods
of extreme annual rainfall in different cities of Khyber Pakhtunkhwa (KPK),
Pakistan.” Nucleus, 49(2), 107-114, 2012.
II. Bansod, S. D., Singh, H. N., Patil, S. D., & Singh, N.: Recent changes in the
circulation parameters and their association with Indian summer monsoon
rainfall. Journal of Atmospheric and Solar-Terrestrial Physics, 77, 248-253,
2012.
III. Coles, S. G., &Tawn, J. A.: A Bayesian analysis of extreme rainfall data.
Applied statistics, 463-478, 1996.
IV. Coles, Stuart G., and Jonathan A. Tawn: Applied Statistics, 45.4, 463-478,
1996.
V. Cotton, James, and Chris Ferro.: Bayesian Priors and Estimation of Extreme
Rainfall in Camborne, 2008.
VI. Dyson, L. L.: Heavy daily-rainfall characteristics over the Gauteng
Province. Water Sa, 35(5), 2009.
VII. El Adlouni, S., Ouarda, T. B. M. J., Zhang, X., Roy, R., &Bobée, B.:
Generalized maximum likelihood estimators for the nonstationary generalized
extreme value model. Water Resources Research, 43(3), 2007.
VIII. Eli, Annazirin, MardhiyyahShaffie, and Wan Zawiah Wan Zin.: Preliminary
Study on Bayesian Extreme Rainfall Analysis: A Case Study of AlorSetar,
Kedah, Malaysia. SainsMalaysiana, Vol41.11, pp. 1403-1410, 2012.
IX. Gelfand, A. E., & Smith, A. F.: Sampling-based approaches to calculating
marginal densities. Journal of the American statistical association, 85(410), 398-
409, 1990.
X. Haigh, J.D.: The Sun and the Earth’s climate. Living Reviews in Solar Physics
4, 2007.
XI. Huang, W., Xu, S., & Nnaji, S.: Evaluation of GEV model for frequency
analysis of annual maximum water levels in the coast of United States. Ocean
Engineering, 35(11), 1132-1147, 2008.
XII. IPCC, Climate Change.: The Physical Science Basis, summary for
policymakers. Contribution of Working Group I to the Fourth Assessment
Report, Paris, 2007.
XIII. Jenkinson, A. F.: The frequency distribution of the annual maximum (or
minimum) values of meteorological elements. Quarterly Journal of the Royal
Meteorological Society, 81(348), 158-171, 1955.
J. Mech. Cont.& Math. Sci., Vol.-15, No.-1, January (2020) pp 118-127
Copyright reserved © J. Mech. Cont.& Math. Sci.
Muhammad Ali et al
127
XIV. Madsen, H., Rasmussen, P. F., &Rosbjerg D.: Comparison of annual maximum
series and partial duration series methods for modeling extreme hydrologic
events: 1. At‐site modeling. Water resources research, 33(4), 747-757, 1997.
XV. Martins, E. S., &Stedinger, J. R.: Generalized maximum‐likelihood generalized
extreme‐value quantile estimators for hydrologic data. Water Resources
Research, 36(3), 737-744,2000.
XVI. Muller, A., Bacro, J. N., & Lang, M.: Bayesian comparison of different rainfall
depth–duration–frequency relationships. Stochastic Environmental Research
and Risk Assessment, 22(1), 33-46, 2008.
XVII. Pickands III, J.: Bayes quantile estimation and threshold selection for the
generalized Pareto family. In Extreme Value Theory and Applications, 123-138,
1994.
XVIII. Rampelotto, P. H., Rigozo, N. R., da Rosa, M. B., Prestes, A., Frigo, E., Souza
Echer, M. P., &Nordemann, D. J. R.:. Variability of rainfall and temperature
(1912–2008) parameters measured from Santa Maria (29° 41′ S, 53° 48′ W) and
their connections with ENSO and solar activity. Journal of Atmospheric and
Solar-Terrestrial Physics, 77, 152-160, 2012.
XIX. Renard, B., Lang, M., & Bois, P.:Statistical analysis of extreme events in a nonstationary
context via a Bayesian framework: case study with peak-overthreshold
data. Stochastic environmental research and risk assessment, 21(2),
97-112,2006.
XX. Siliverstovs, B., Ötsch, R., Kemfert, C., Jaeger, C. C., Haas, A., &Kremers, H.:
Climate change and modeling of extreme temperatures in Switzerland.
Stochastic Environmental Research and Risk Assessment, 24(2), 311-326, 2009.
XXI. Smith, R. L., & Naylor, J. C.: A comparison of maximum likelihood and
Bayesian estimators for the three-parameter Weibull distribution. Applied
Statistics, 358-369, 1987.
XXII. Zin, W.Z.W., Jemain, A.A., Ibrahim, K., Jamaludin, S. &Deni, S.M.: A
comparative study of extreme rainfall in Peninsular Malaysia: with reference to
partial duration and annual extreme series. SainsMalaysiana 38: 751-760, 2009.

View Download

Abstract:

Multi-View Human Action Recognition, as a hot research area in computer
vision, has many more applications in various fields. Despite its popularity, more
precise recognition still remains a major challenge due to various constraints.
Extracting the robust and discriminative feature from video sequence is a crucial step
in the Human Action Recognition system. In this paper, a new feature extraction
technique is proposed based on the integration of three different features such as
intensity, Orientation and Contour features. Unlike the earlier approaches which
applied feature extraction directly over actions videos, this approach applies the
feature extraction only over key frames which are extracted from a large set of
frames. The key frames selection is accomplished based on a new mechanism, called
Gradient Self-Similarity Matrix (GSSM). GSSM is proposed as an extension to the
most popular Self-Similarity Matrix (SSM) by evaluating the gradients of actions
frames before SSM accomplishment. Once the key frames are extracted, the hybrid
feature extraction mechanism is applied and the obtained features are processed for
classification through Support Vector Machine Classifier. The proposed framework is
systematically evaluated on IXMAS dataset and NIXMAS dataset. Experimental
results enumerate that our method outperforms the conventional techniques in terms
of recognition accuracy.

Keywords:

Computer Vision,Human Action Recognition,Multiple Views,Self- Similarity Matrix,Gaussian,Gabor,Wavelet,Accuracy,

Refference:

I. Aryanfar, R. Yakob, and A. A. Halin, “Multi-View Human Action
recognition Using Wavelet data Reduction and Multi-class Classification”,
In: Prof. of international Conf. on Soft Computing and Software Engineering,
Berkeley, Suta, pp.585-592, 2015.
II. A. Cohen, I. Daubechies, and J. Feauveau, “Bi-orthogonal bases of compactly
supported wavelets”, Communications and Pure Applied Mathematics., Vol.
45, No. 5, pp. 485–560, 1992
III. A. Eweiwi, M.S. Cheema, C. Bauckhage, J. Gall, Efficient pose-based action
recognition, in: Asian Conference on Computer Vision, Springer, 2014, pp.
428–443.
IV. A. Farhadi and M. K. Tabrizi, “Learning to recognize activities from the A.
wrong view point,” in Proc. Eur. Conf. Comput. Vis., 2008, pp. 154–166.
V. A. Gilbert, J. Illingworth, and R. Bowden, “Scale invariant action recognition
using compound features mined from dense Spatio-temporal corners,” in
Proc. ECCV, 2008, pp. I: 222–233.
VI. C. H. Lampert, H. Nickisch, S. Harmeling. Learning to detect unseen object
classes by between-class attribute transfer. In CVPR, 2009.
J. Mech. Cont.& Math. Sci., Vol.-15, No.-1, January (2020) pp 128-146
Copyright reserved © J. Mech. Cont.& Math. Sci.
K. Pradeep Reddy et al
144
VII. C. Rao, A. Yilmaz, and M. Shah, View-invariant representation and
recognition of actions, Int. J. Comput. Vis., vol. 50, no. 2, pp. 203–226, 2002.
VIII. C. Schuldt, I. Laptev, and B. Caputo, ‘‘Recognizing human actions: A local
SVM approach,’’ in Proc. 17th Int. Conf. Pattern Recognit., vol. 3. Aug.
2004, pp. 32–36.
IX. D. Weinland, E. Boyer, and R. Ronfard, “Action recognition from arbitrary
views using 3D exemplars,” in Proc. IEEE Int. Conf. Comput. Vis., Oct.
2007, pp. 1–7.
X. D. Weinland, M. Özuysal, and P. Fua, “Making action recognition robust to
occlusions and viewpoint changes,” in Proc. 11th Eur. Conf. Comput. Vis.,
2010, pp. 635–648.
XI. E. Shechtman and M. Irani. Matching local self-similarities across images
and videos. In CVPR, 2007.
XII. H. Wang, A. Klaser, C. Schmid, and C. Liu, “Action recognition by dense
trajectories,” in Computer Vision and Pattern Recognition (CVPR), 2011
IEEE Conference on. IEEE, 2011, pp. 3169–3176.
XIII. I. Junejo, “Self-similarity based action recognition using conditional random
fields,” in Information Retrieval Knowledge Management (CAMP), 2012
International Conference on, march 2012, pp. 254 –259.
XIV. I. N. Junejo, E. Dexter, I. Laptev, and P. Perez, “View-independent action
recognition from temporal self-similarities,” IEEE Transactions on Pattern
Analysis and Machine Intelligence, vol. 99, no. PrePrints, 2010.
XV. Ivo Everts, Jan C. van Gemert and Theo Gevers, Evaluation of Color STIPs
for Human Action Recognition, IEEE Conference on Computer Vision and
Pattern Recognition, Portland, OR, USA, 2013.
XVI. Jing Wang, and Huicheng Zheng, “View-robust action recognition based on
temporal self-similarities and dynamic time warping”, IEEE International
Conference on Computer Science and Automation Engineering (CSAE),
Zhangjiajie, China, 2012.
XVII. J. Wang, Z. Chen, and Y. Wu, “Action recognition with multiscale Spatiotemporal
contexts,” in Computer Vision and Pattern Recognition (CVPR),
2011 IEEE Conference on. IEEE, 2011, pp. 3185–3192.
XVIII. K. Chatfield, J. Philbin, and A. Zisserman. Efficient retrieval of deformable
shape classes using local self-similarities. In NORDIA Workshop at ICCV
2009, 2009.
XIX. K. G. C. Manosha, Ranga Rodrigo, Faster Human Activity Recognition with
SVM, The International Conference on Advances in ICT for Emerging
Regions – ICTER 2012 : 197-203.
XX. K. Huang, Y. Zhang, and T. Tan, “A discriminative model of motion and
cross ratio for view-invariant action recognition,” Image Processing, IEEE
Transactions on, vol. 21, no. 4, pp. 2187–2197, 2012.
XXI. K. Pradeep Reddy, G. Apparao Naidu, B.VishnuVardhan, “View-Invariant
Feature Representation for Action Recognition under Multiple Views”,
International Journal of Intelligent Engineering and Systems, Vol.12, No.6,
2019.
J. Mech. Cont.& Math. Sci., Vol.-15, No.-1, January (2020) pp 128-146
Copyright reserved © J. Mech. Cont.& Math. Sci.
K. Pradeep Reddy et al
145
XXII. L. Gorelick, M. Blank, E. Shechtman, M. Irani, and R. Basri, “Actions as
space-time shapes,” PAMI, vol. 29, no. 12, pp. 2247–2253, December 2007.
XXIII. M. Abd-el-Kader, W. Abd-Almageed, A. Srivastava, and R. Chellappa,
“Silhouette-based gesture and action recognition via modeling trajectories on
Riemannian shape manifolds,” CVIU, vol. 115, no. 3, pp. 439–455, 2011.
XXIV. M. Everingham, L. Van Gool, C. Williams, J. Winn, and A. Zisserman. The
PASCAL Visual Object Classes Challenge 2007.
XXV. M. Stark, M. Goesele, and B. Schiele. A shape-based object class model for
knowledge transfer. In ICCV, 2009.
XXVI. Paul, S.N.; Singh, Y.J. Survey on Video Analysis of Human Walking
Motion. Int. J. Signal Process. Image Process. Pattern Recognit. 2014, 7, 99–
122.
XXVII. P. Matikainen, R. Sukthankar, and M. Hebert, “Feature seeding for action
recognition,” in Computer Vision (ICCV), 2011 IEEE International
Conference on. IEEE, 2011, pp. 1716–1723.
XXVIII. Q. Le, W. Zou, S. Yeung, and A. Ng, “Learning hierarchical invariant Spatiotemporal
features for action recognition with independent subspace analysis,”
in CVPR. IEEE, 2011, pp. 3361–3368.
XXIX. R. Poppe, A survey on vision-based human action recognition, Image Vision
Comput. 28 (6) (2010) 976–990.
XXX. SamySadek, Ayoub Al-Hamadi, Bernd Michaelis, and UsamaSayed, “An
Action Recognition Scheme Using Fuzzy Log-Polar Histogram and
Temporal Self-Similarity”, Hindawi Publishing Corporation EURASIP
Journal on Advances in Signal Processing, Volume 2011, Article ID 540375,
9 pages.
XXXI. S. Maji, L. Bourdev, and J. Malik, “Action recognition from a distributed
representation of pose and appearance,” in Computer Vision and Pattern
Recognition (CVPR), 2011 IEEE Conference on. IEEE, 2011, pp.3177–3184.
XXXII. S. Singh, S. A. Velastin, and H. Ragheb, ‘‘MuHAVi: A multi-camera human
action video dataset for the evaluation of action recognition methods,’’ in
Proc. 17th IEEE Int. Conf. Adv. Video Signal Based Surveill. (AVSS), Sep.
2010, pp. 48–55.
XXXIII. Stephen Karungaru, Masayuki Daikoku, Kenji Terada, “Multi Cameras
Based Indoors Human Action Recognition Using Fuzzy Rules”, JPRR Vol
10, No 1 (2015).
XXXIV. S. Vishwakarma, A. Agrawal, A survey on activity recognition and behavior
understanding in video surveillance, Visual Computer 29 (10) (2013) 983–
1009
XXXV. S. Wu, O. Oreifej, and M. Shah, “Action recognition in videos acquired by a
moving camera using motion decomposition of Lagrangian particle
trajectories,” in Computer Vision (ICCV), 2011 IEEE International
Conference on. IEEE, 2011, pp. 1419–1426.
XXXVI. T. Deselaers and V. Ferrari, “Global and efficient self-similarity for object
classification and detection,” in Computer Vision and Pattern Recognition
(CVPR), 2010 IEEE Conference on. IEEE, 2010, pp. 1633–1640.
J. Mech. Cont.& Math. Sci., Vol.-15, No.-1, January (2020) pp 128-146
Copyright reserved © J. Mech. Cont.& Math. Sci.
K. Pradeep Reddy et al
146
XXXVII. T. SyedaMahmood, M. Vasilescu, and S. Sethi, Recognizing action events
from multiple viewpoints, in Proc. EventVideo, 2001, pp. 64–72.
XXXVIII. X. Wu, D. Xu, L. Duan, and J. Luo, “Action recognition using context and
appearance distribution features,” in CVPR 2011. IEEE, 2011, pp. 489–496.
XXXIX. Yen-Pin HsuChengyin Liu Tzu-Yang Chen Li-Chen Fu, “Online viewinvariant
human action recognition using RGB-D Spatio-temporal matrix”,
Pattern recognition, Volume 60, December 2016, Pages 215-226.

View Download