Archive

Abstract:

Getting the culture right, is challenging – but is well worth for the rewards of success. Every Institution is characterized byits ownculture, which is different from others. Identification of organizational culture in an institution may help in determining if mission, goals, and strategic objectives were being met. This study investigated academic staff members’ perceptions of organizational culture inselected institutions of higher learning. Faculty’s views about different dimensions of organizational culture were examined. A total of 100 academic staff members from privateinstitutions of higher learning have completed the Organizational Culture Assessment Instrument. Analysis of data was done by using descriptive statistics explicitly to determine the kind of existing organizational culture in private institutions. Results of this study revealed that private institutions mainly have market culture.

Keywords:

Organizational culture,Private institution,Academic staff members,

Refference:

I. A. Ali and B. Patnaik, “Influence of organizational climate and organizational
culture on managerial effectiveness: an inquisitive study”, The Carrington Rand
Journal of Social Sciences, vol. 1(2), pp: 001-020, 2015.
II. A. Kern, Z. Amod, J. Seabi, and A. Vorster, “International Journal of
Environmental Research and Public Health”, vol. 12, pp. 3042-3059, 2015.
III. A. Morris and J. R. Bloom, “Contextual factors affecting job satisfaction and
organizational commitment in community mental health centers undergoing
system changes in the financing of care”, Mental Health Services Research, vol.
4(2), pp: 71-83, 2002.
IV. A. Sharma and A. Sharma, “Examining the Relationship between Organisational
Culture and Leadership Styles”, Journal of the Indian Academy of Applied
Psychology,vol.36(1), pp: 97-105, 2010.
J. Mech. Cont.& Math. Sci., Vol.-15, No.-1, January (2020) pp 160-167
Copyright reserved © J. Mech. Cont.& Math. Sci.
Navneesh Tyagi et al
167
V. D. Dhanuraj and R. V. Kumar, “Understanding the Status of Higher Education in
India: Challenges and Scepticism towards Serious Investments in the Sector”,
Centre for Public Sector Research, 2015.
VI. J. Kenny, “Efficiency and effectiveness in higher education”, Australian
Universities review, vol. 50, no. 1, 2008
VII. J. O. Nunally, “Psychometric Theory”, New York: McGraw-Hill, 1978.
VIII. K. S. Cameron and R. E. Quinn, “Diagnosing and Changing Organizational
Culture: Based on the Competing Values Framework”, Reading, MA: Addison-
Wesley, 1999.
IX. K. S. Cameron and R. E. Quinn, “Diagnosing and changing organizational culture:
Based on the competing values framework”, Jossey-Bass, 2006.
X. L. L. Salonda, “Exploration of a university culture: A Papaua New Guinea case
study, Unpublished Doctoral Thesis, Victory University of Technology, 2008.
XI. M. Antic and A. Ceric, “Organizational culture of faculty of civil engineering”,
8th International Conference, Organization, Technology, Management in
Construction, University of Zagreb, Zagreb, 2008. Available at:
http://crosbi.znanstvenici.hr/datoteka/396 954.Antic20Ceric.pdf
XII. N. Tyagi and P. Singh, “Does Fairness Perceptions of Academic Staff Interfere
with the Managerial Effectiveness?” International Journal of Innovative
Technology and Exploring Engineering, vol. 8 (12S), pp: 763-769, 2019.
XIII. N. Tyagi, D. Gupta, and D. B. Moses, “How Self Concept Interfere between
Integrative Leadership and Leadership Effectiveness”, International Journal of
Recent Technology and Engineering, vol. 8(3), pp: 4685-4690, 2019.
XIV. R. E. Quinn and G. M. Spreitzer, “The Psychometrics of the Competing Values
Culture Instrument and an Analysis of theImpact of Organizational Culture on
Quality of Life”, Research in Organizational Change and Development, vol. 5, pp:
115-142, 1991.
XV. R. Gupta, “A study of managerial correlates in selected Indian organizations”
(unpublished PhD Thesis) Rohtak, MDU, 2012. Available at:
shodhganga.inflibnet.ac.in/handle/ 10603/7901.
XVI. R. Likert, “New Patterns of Management”, New York: McGrawHill, 1961.

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Abstract:

In this paper, the effect of dynamic coefficients of different bearings types on the dynamic response and the frequency of maximum response of rotor bearing system have been studied. The ANSYS Mechanical APDL 18.0 was used to model rotor with different bearings types. MATLAB software has been used to achieve the analytical solution. The results showed that the using of ball bearing, roller bearing or self aligning bearing with fluid film journal bearing strongly increasing the dynamic response amplitude and slightly increasing frequency of maximum response compared with the using of journal bearing to support rotor while using ball bearing with roller bearing have insignificant effect on the dynamic response and frequency of maximum response also using of self aligning bearing with ball bearing or roller bearing strongly decreasing the dynamic response and slightly decreasing the frequency of maximum response and using the self aligning bearing with others bearings types give the misalignment self-overcoming feature.

Keywords:

Rotor,Dynamic Response,Journal Bearing,Roller Bearing,Ball Bearing,

Refference:

I. ANSYS Help Topics Mechanical APDL Documentation Version 18.0. 2018
II. Chong W. L., “Vibration Analysis of Rotors”, (Dordrecht: Springer Science
+ Business Media), 1993
III. Kramer E. , “ Dynamics of Rotors and Foundations”, New York: Springer-
Verlag Berlin, 1993
IV. Michael I Friswell, John E T Penny, Seamus D Garvey and Arthur W Lees
“Dynamics of Rotating Machines”, London: Cambridge University Press,
2010
V. Rao J. S., “Rotor Dynamics”, New Delhi: New Age International Publishers,
1996
VI. Tedric A Harris, “Rolling Bearing Analysis”, New York: Wiley –
Interscience, 2001
VII. Wen J Chen and Edgar J. Gunter, “Introduction to Dynamics of Rotor-
Bearing Systems”, Victoria: Trafford, 2007

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Abstract:

Segmentation of MR brain image is quite useful in detection of tumors and further diagnosis. However, precise segmentation of tumors plays a significant role in diagnosing the patient more effectively. Previously, there are plenty of approaches was implemented and however they were failed to detect the exact tumor which led to the failure diagnosis. Therefore, an accurate detection of tumor is required for effective diagnosis. Here, this article presented an efficient segmentation of MR brain image tumors. Our approach includes a hybrid clustering mechanism with pre-processed by savitzky-golay filter (SGF). In addition, tumor area also estimated for better diagnosis of patient. Simulation results disclosed the superiority of proposed hybrid approach over conventional segmentation algorithms in terms of computational complexity and segmentation accuracy.

Keywords:

Magnetic resonance imaging,Brain tumor,Thresholding,Fuzzy C-means,K-means,Hybrid clustering,

Refference:

I. A.M. Usó, F. Pla and P.G. Sevila, “Unsupervised Image Segmentation
Using a Hierarchical Clustering Selection Process”, Structural,
Syntactic, and Statistical Pattern Recognition, vol. 4109, pp. 799-807,
2006.
II. A. R. Barakbah and Y.Kiyoki. “A Pillar algorithm for K-means
Optimization by Distance Maximization for Initial Centroid
Designation”, IEEE Symposium on Computational Intelligence and
Data Mining, pp. 61-68, 2009.
III. A.Z. Arifin and A. Asano, “Image segmentation by histogram
thresholding using hierarchical cluster analysis”, Pattern Recognition
Letters, vol. 27, no. 13, pp. 1515-1521, 2006.
IV. A. Sehgal, et. al, “Automatic Brain Tumor Segmentation and
Extraction in MR Images”, In Proc. of Inter. Conf. on Adv. in
Sig.Proces., Pune, India, pp. 104-107, 2016.
V. E. A. Maksoud, M.Elmogy and R.A. Awadhi, “Brain Tumor
Segmentation based on a Hybrid Clustering Technique”, Egyptian
Informatics Journal, vol. 16, no. 1, 2015.
VI. H. P. A. Tjahyaningtijas, “Brain Tumor image segmentation in MRI
images”, IOP Conf. Series: Materials Science and Engineering, vol.
336, 012012, 2018.
VII. http://www.unitconversion.org/typography/millimeters-to-pixels-xconversion.
html
VIII. J. E. A. L. Kostka, “A review of the medical image segmentation
algorithms”, In: Peng SL., Dey N., Bundele M. (eds) Computing and
Network Sustainability, Lecture Notes in Networks and Systems, vol
75, Springer, Singapore, May 2019.
IX. J. Selvakumar, A. Lakshmi and T. Arivoli, “Brain Tumor segmentation
and its area Calculation in Brain MR images using K-means Clustering
and Fuzzy C-means algorithm”, International Conference on Advances
in Engineering, Science and Management,pp. 186-190, 2012.
X. M.H. F.Zarandia, M. Zarinbala and M. Izadi, “Systematic image
processing for diagnosing brain tumors: A Type-II fuzzy expert system
approach”, Applied soft computing, pp. 285-294, 2011.
XI. N. Dhanachandra, K. Maglem and Y. J. Chanu, “Image segmentation
using K-means and subtractive clustering algorithm”, Procedia
Computer Science, vol 54, pp. 764-771, 2015.
XII. T. Shen and Y. Wang, “Medical image segmentation based on
improved watershed algorithm”, In: Proc. of 3rd Advanced Information
J. Mech. Cont.& Math. Sci., Vol.-15, No.-1, January (2020) pp 182-191
Copyright reserved © J. Mech. Cont.& Math. Sci.
Bhasker Dappuri et al
191
Technology, Electronic and Automation Control, Chongqing, China,
IEEE, Oct. 2018.
XIII. T. W. Chen, Y.-L. Chen and S.-Y.Chien, “Fast Image Segmentation
Based on K-Means Clustering with Histograms in HSV Color Space”,
Journal of Scientific Research, vol. 44, no.2, pp.337-351, 2010.
XIV. Z. Beevi and M.Sathik, “An effective approach for segmentation of
MRI images: combining spatial information with fuzzy c-means
clustering”, European Journal of Scientific Research, vol. 41, no.3,
pp.437-451, 2010.

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Abstract:

Multilevel inverters are superior to the two-level inverters to meet medium and high - power applications. A seven-level diode-clamped multilevel topology (7LDCMT) proposed, and compare the advantages and disadvantages of various control strategies such as multilevel space vector pulse width modulation (MSVPWM) with multicarrier sinusoidal pulse width modulations (MCSPWM) named as level shift and phase shift PWM based on the position of carriers at certain frequency. In level shift is further divided and compare the Inphase disposition sinusoidal pulse width modulation (IPD-SPWM), phase opposition and disposition-sinusoidal pulse width modulation (POD-SPWM), alternate phase opposition and disposition-sinusoidal pulse width modulation (APOD-SPWM) and carrier phase displacement sinusoidal pulse width modulation (CPD-SPWM). The 7L-DCMT designed with MATLAB/SIMULINK and the performance can analyses based on the observing total harmonic distortion (THD) at different control strategies.

Keywords:

Seven level diode-clamped multilevel inverter,Multicarrier sinusoidal pulse width modulation,level shift,phase shift,Multilevel Space vector pulse width modulation,

Refference:

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Abstract:

The recent expansion of research into big data has set an exciting goal for mathematicians, Computer scientists as well as business professionals. Though, the absence of a Sound architecture of mathematics presents itself by way of a actual experiment in the Big Data advancement community. The paper's goal is to propose a possible theory of mathematical structure as per a basis of research into big data. The analysis of the application a mathematical modelling can be strongly wellthought- out as a theory of the Big data transforming technologies, systems, data management and processing tools. In amassing, the premise of big data's inanity is constructed on the calculus & principle and set theory. Its suggested method in this paper, encourage and open up more open doors for large information research and advancements on Big data information knowledge, business analytics, big data information investigation, big data Computing information technology as well as big data Computer science.

Keywords:

Big data,mathematical modelling,big data analysis,big data computing,

Refference:

I. Gandomi and M. Haider, Beyond the hype: Big data concepts, methods, and
analytics, International Journal of Information Management 35 (2015) 137-144.
II. A. McAfee and E. Brynjolfsson, Big data: The management revolution, Harvard
Business Review 90(10) (2012)
III. C. K. Chui and Q. Jiang, Applied Mathematics: Data Compression, Spectral
Methods, Fourier Analysis, Wavelets, and Applications (Springer, 2013).
IV. C. K. Chui and Q. Jiang, Applied Mathematics: Data Compression, Spectral
Methods, Fourier Analysis, Wavelets, and Applications (Springer, 2013).
V. C. P.iChen and C.-Y. Zhang, Data-intensive applications, challenges,techniques
and technologies: A survey on Big Data, Information Sciences 275 (2014).
VI. C. iCoronel, S. Morris and P. Rob, Database Systems: Designs, Implementation,
and Management, 11th edn. (Course Technology, Cengage Learning, Boston,
2015).
VII. C. P.iChen and C.-Y. Zhang, Data-intensive applications, challenges,techniques
and technologies: A survey on Big Data, Information Sciences 275 (2014).
VIII. IBM, The Four V’s of Big Data (2015),ii
http://www.ibmbigdatahubi.com/infographic/ four-vs-bigdata.
IX. L. A. Zadeh, Fuzzy sets and information granularity, in Advances in Fuzzy Sets
Theory and Applications, eds. M. Gupta, R. K. Ragade and R. R. Yager (North-
Holland, New York, 1979),
X. L. A. Zadeh, Fuzzy sets, Information and Control 8(3) (1965)
XI. M. Kantardzic, Data Mining: Concepts, Models, Methods, and Algorithms
(Wiley & IEEE Press, Hoboken, 2011).
XII. M. Minellii, M. Chamber and A. Dhiras, Big Data, Big Analytics: Emerging
Business Intelligence and Analytic Trends for Today’s Businesses (John Wiley
and Sons, 2013).
J. Mech. Cont.& Math. Sci., Vol.-15, No.-1, January (2020) pp 209-216
Copyright reserved © J. Mech. Cont.& Math. Sci.
MD Mobin Akhtar et al
216
XIII. M. Minelli, M. Chambers and A. Dhiraj, Big Data, Big Analytics: Emerging
Business Intelligence and Analytic Trends for Today’s Businesses, Chinese edn.
(Wiley & Sons, 2013)
XIV. R. Larson and B. H. Edwards, Calculus, 9th edn. (Brooks Cole Cengage
Learning, 2010).
XV. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 3rd edn.
(Prentice Hall, Upper Saddle River, 2010).
XVI. Z. Sun and G. Finnie, Experience management in knowledge management, in
KES 2005: Knowledge-Based Intelligent Information and Engineering Systems,
LNCS, Vol. 3681 (Springer-Verlag, Berlin, 2005)
XVII. Z. Sun and J. Xiao, Essentials of Discrete Mathematics,i Problems and
Solutionsi (Hebei University Press, Baoding, 1994).

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Abstract:

This research paper gives the idea of 'non-overlapping n-ary codons' is suggested as aninnovative way to deal with the investigation of genome groupings in the system of analytical software engineering. Given a genome succession of length N, and one can have (N/n) non-overlapping n-ary codons with 0 or 1 or up to n-1 untouched nucleotides left in the arrangement. Fresh or unused nucleotides are not advised in the plan of genetic code.

Keywords:

Non-Overlapping,Linear Prediction,n-aryCodons (n-codons),Genome Sequences,

Refference:

I. Doolittle, W. Ford (2013). “Is junk DNA bunk? A critique of ENCODE”.
Proc Natl Acad Sci USA110 (14): 5294–5300.
Bibcode:2013PNAS..110.5294D. doi:10.1073/pnas.1221376110.
PMC 3619371. PMID 23479647.
II. Ohno, Susumu (1972). H. H. Smith, ed. So Much “junk” DNA in Our
Genome. Gordon and Breach, New York. pp. 366–370. Retrieved 2013-05-
15.
III. Palazzo, Alexander F.; Gregory, T. Ryan (2014). “The Case for Junk DNA”.
PLoS Genetics10 (5): e1004351. doi:10.1371/journal.pgen.1004351.
ISSN 1553-7404.
IV. Petrov DA, Hartl DL; Hartl (2000). “Pseudogene evolution and natural
selection for a compact genome”. J. Hered. 91 (3): 221–7.
V. Sean Eddy (2012) The C-value paradox, junk DNA, and ENCODE, Curr Biol
22(21):R898–R899.
VI. Tutar, Y. (2012). “Pseudogenes”. Comp Funct Genomics 2012: 424526.
doi:10.1155/2012/424526. PMC 3352212. PMID 22611337.
VII. Waterhouse, Peter M.; Hellens, Roger P. (25 March 2015). “Plant biology:
Coding in non-coding RNAs”. Nature 520 (7545): 41–

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Abstract:

Huge amount of genomic and related data is available in public domain, but they are not manageable. So, it has become the need of the hour to search for faster and reliable algorithms to work on such large genomic databases. Generally, the genomic data comes under ‘Big Data’ and the implementation of the huge data is a hard task. In this case, the public who are working in the field of data mining and pattern recognition understood the emphasis of ‘Machine learning’ capability in evaluating such big data. In this connection, this paper recommends a novel procedure of ‘Supernatural classification of genomic strings’ for DNA analysis scheme.

Keywords:

Supernatural classification,pattern recognition,Big data,Genome Analysis,

Refference:

I. Andrew Webb, “Statistical Pattern Recognition”, 2nd Edition, Wiley 2002.
II. Communication Engineering, Regional Engineering College, Warangal, 1997.
III. Chou-Ting Hsu and Ja-ling Wu, Hidden Digital Watermarks in Images, Senior Member, IEEE. IEEE Transactions on Image Processing.
IV. Dr. E. G. Rajan, Symbolic Processing of Signals and Images. First edition, 2003.
V. Don Pearson, Image Processing, Tata McGraw Hill, U.K. 1991.
VI. J. P margues de sa, “Pattern Recognition Concepts, Methods and Applications, Spinger, May 2001, Prtugal.
VII. Julius J. Tourafael C. Gonzales, Pattern Recognition Principles, Addison Wesley 1974.
VIII. Kishore K, Subramanyam J. V., and Rajan E. G., “Thinning Lattice Gas Automation Model for Solidification Processes, National Conference by ASME, USA, Hilton Hotel, California.
IX. Markov A A, Theory of Algorithms, Israel Program for scientific Tranlation, DC, 1951.
X. Robert J, Schalkoff, Pattern Recognition: Statistical, structural and Neural approaches. ISBN: 0-0471-52974-5, June 1991.

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Abstract:

To depict the hyperspectral data, here a sturdy mixing model is implemented by employing various perfect spectral signatures mixture, which enhances the generally utilized linear mixture model (LMM) by inserting an extra term that describes the potential nonlinear effects (NEs), which are addressed as additive nonlinearities (NLs) those are disseminated without dense. Accompanying the traditional nonnegativity and sum-to-one restraints underlying to the spectral mixing, this proposed model heads to a novel pattern of sturdy nonnegative matrix factorization (S-NMF) with a term named group sparse outlier. The factorization is presented as an issue of optimization which is later dealt by an iterative blockcoordinated descent algorithm (IB-CDA) regarding the updates with maximationminimization. Moreover, distinctive hyperspectral mixture models also presented by adopting the considerations like NEs, mismodelling effects (MEs) and endmember variability (EV). The extensive simulation analysis by the implementation of proposed models with their estimation approaches tested on synthetic images. Further, it is also shown that the comparative analysis with the conventional approaches.

Keywords:

Hyperspectral images,spectral unmixing,linear mixture models,nonlinear mixture models,nonlinear spectral unmixing,

Refference:

I. A. A. Kalaitzis and N. D. Lawrence, “Residual component analysis”, In:
Proc. of ICML, 2012, pp. 1–3.
II. A. Gowen, et al., “Hyperspectral imaging: an emerging process analytical
tool for food quality and safety control”, Trends in Food Science and
Technology, vol. 18, no. 12, pp. 590–598, 2007.
III. A. Halimi and P. Honeine, “Hyperspectral unmixing in presence of
endmember variability, nonlinearity and mismodelling effects”, IEEE Trans.
Imag. Proc., vol. 25, no. 10, pp. 4565-4579, Oct. 2016.
IV. A. Halimi, C. Mailhes, J.-Y. Tourneret, and H. Snoussi, “Bayesian estimation
of smooth altimetric parameters: Application to conventional and
delay/Doppler altimetry”, IEEE Transactions on Geoscience and Remote
Sensing, vol. 54, no. 4, pp. 2207–2219, Apr. 2016.
V. A. Halimi, N. Dobigeon, and J.-Y. Tourneret, “Unsupervised unmixing of
hyperspectral images accounting for endmember variability”, IEEE
Transaction on Image Processing, vol. 24, no. 12, pp. 4904–4917, Dec. 2015.
VI. A. Halimi, Y. Altmann, N. Dobigeon, and J.-Y. Tourneret, “Nonlinear
unmixing of hyperspectral images using a generalized bilinear model,” IEEE
Transactions on Geoscience and Remote Sensing, vol. 49, no. 11, pp. 4153–
4162, Nov. 2011.
VII. A. Halimi, Y. Altmann, N. Dobigeon, and J.-Y. Tourneret, “Unmixing
hyperspectral images using the generalized bilinear model”, In: Proceedings
of International Geoscience and Remote Sensing Symposium, 2011, pp.
1886–1889.
VIII. A. Lee, F. Caron, A. Doucet, and C. Holmes, “A Hierarchical Bayesian
Framework for Constructing Sparsity-inducing Priors,” arXiv.org, Sept.
2010.
IX. A. Zare and K. Ho, “Endmember variability in hyperspectral analysis:
Addressing spectral variability during spectral unmixing”, IEEE Signal
Processing Magazine, vol. 31, no. 1, pp. 95–104, Jan. 2014.
X. A. Zare, P. Gader, and G. Casella, “Sampling piecewise convex unmixing
and endmember extraction”, IEEE Transactions on Geoscience and Remote
Sensing, vol. 51, no. 3, pp. 1655–1665, Mar. 2013.
XI. B. Somers, et al., “Nonlinear hyperspectral mixture analysis for tree cover
estimates in orchards”, Remote Sensing Environment, vol. 113, pp. 1183–
1193, Feb. 2009.
XII. B. Somers, G. P. Asner, L. Tits, and P. Coppin, “Endmember variability in
spectral mixture analysis: A review”, Remote Sensing Environment, vol. 115,
no. 7, pp. 1603–1616, 2011.
XIII. B. Somers, L. Tits, and P. Coppin, “Quantifying nonlinear spectral mixing in
vegetated areas: computer simulation model validation and first results”,
IEEE Journal of Selected Topics on Applied Earth Observations and Remote
Sensing, vol. 7, no. 6, pp. 1956–1965, June 2014.
J. Mech. Cont.& Math. Sci., Vol.-15, No.-1, January (2020) pp 248-262
Copyright reserved © J. Mech. Cont.& Math. Sci.
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XIV. B. Somers, M. Zortea, A. Plaza, and G. Asner, “Automated extraction of
image-based endmember bundles for improved spectral unmixing”, IEEE
Journal of Selected Topics on Applied Earth Observations and Remote
Sensing, vol. 5, no. 2, pp. 396–408, Apr. 2012.
XV. C. A. Bateson, G. P. Asner, and C. A. Wessman, “Endmember bundles: A
new approach to incorporating endmember variability into spectral mixture
analysis”, IEEE Transactions on Geoscience and Remote Sensing, vol. 38,
no. 2, pp. 1083–1094, Mar. 2000.
XVI. D. A. Roberts, et al., “Mapping chaparral in the Santa Monica Mountains
using multiple endmember spectral mixture models,” Remote Sensing
Environment, vol. 65, no. 3, pp. 267–279, Sep. 1998.
XVII. D. C. Heinz and C.-I. Chang, “Fully constrained least-squares linear spectral
mixture analysis method for material quantification in hyperspectral
imagery”, IEEE Transactions on Geoscience and Remote Sensing, vol. 29,
no. 3, pp. 529–545, Mar. 2001.
XVIII. D. Stein, “Application of the normal compositional model to the analysis of
hyperspectral imagery”, In Proc. of IEEE Workshop Adv. Techn. Anal.
Remotely Sensed Data, Oct. 2003, pp. 44–51.
XIX. ENVI User’s Guide Version 4.0, Boulder, CO, USA, RSI Research Systems
Inc., Sep. 2003.
XX. G. A. Shaw and H.-H. K. Burke, “Spectral imaging for remote sensing”,
Lincoln Lab. J., vol. 14, no. 1, pp. 3–28, 2003.
XXI. G. P. Asner and K. B. Heidebrecht, “Spectral unmixing of vegetation, soil
and dry carbon cover in arid regions: comparing multispectral and
hyperspectral observations”, International Journal of Remote Sensing, vol.
23, no. 19, pp. 3939–3958, 2002.
XXII. I. Meganem, et al., “Linear-quadratic mixing model for reflectances in urban
environments”, IEEE Transactions on Geoscience and Remote Sensing, vol.
52, no. 1, pp. 544–558, Jan. 2014.
XXIII. J. B. Dias and M. A. T. Figueiredo, “Alternating direction algorithms for
constrained sparse regression: Application to hyperspectral unmixing”, In
Proc. of 2nd Workshop on Hyperspectral Image and Signal Processing:
Evaluation in Remote Sensing, Jun. 2010, pp. 1–4.
XXIV. J. Chen, C. Richard, and P. Honeine, “Nonlinear unmixing of hyperspectral
data based on a linear-mixture/nonlinear-fluctuation model”, IEEE Trans.
Signal Process., vol. 61, no. 2, pp. 480–492, Jan. 2013.
XXV. J. M. B. Dias, et al., “Hyperspectral unmixing overview: Geometrical,
statistical, and sparse regression-based approaches”, IEEE Journal of Selected
Topics in Applied Earth Observations and Remote Sensing, vol. 5, no. 2, pp.
354–379, May 2012.
XXVI. J. M. P. Nascimento and J. M. B. Dias, “Nonlinear mixture model for
hyperspectral unmixing”, In: Proceedings of Image and Signal Processing for
Remote Sensing, vol. 7477, Berlin, Germany, 2009.
J. Mech. Cont.& Math. Sci., Vol.-15, No.-1, January (2020) pp 248-262
Copyright reserved © J. Mech. Cont.& Math. Sci.
M. Venkata Sireesha et al
261
XXVII. J. M. P. Nascimento and J. M. Bioucas Dias, “Does independent component
analysis play a role in unmixing hyperspectral data”, IEEE Transactions on
Geoscience and Remote Sensing, vol. 43, no. 1, pp. 175–187, Jan. 2005.
XXVIII. J. M. P. Nascimento and J. M. Bioucas-Dias, “Vertex component analysis: A
fast algorithm to unmix hyperspectral data”, IEEE Transactions on
Geoscience and Remote Sensing, vol. 43, no. 4, pp. 898–910, Apr. 2005.
XXIX. J. Sigurdsson, M. O. Ulfarsson, and J. R. Sveinsson, “Hyperspectral
unmixing with l􀭯-regularization”, IEEE Transactions on Geoscience and
Remote Sensing, vol. 52, no. 11, pp. 6793–6806, Nov. 2014.
XXX. K. E. Themelis, et al., “On the unmixing of MEx/OMEGA hyperspectral
data,” Planetary and Space Science, vol. 68, no. 1, pp. 34–41, 2012.
XXXI. M. A. Veganzones et al., “A new extended linear mixing model to address
spectral variability”, In: Proc. of IEEE Workshop Hyperspectral Image Signal
Process., Evol. Remote Sens. (WHISPERS), Lausanne, Switzerland, Jun.
2014, p. n/c.
XXXII. M. Goenaga, M. Torres-Madronero, M. Velez-Reyes, S. J. Van Bloem, and J.
D. Chinea, “Unmixing analysis of a time series of hyperion images over the
Guánica dry forest in Puerto Rico”, IEEE Journal of Selected Topics on
Applied Earth Observations and Remote Sensing, vol. 6, no. 2, pp. 329–338,
Apr. 2013.
XXXIII. M. Winter, “Fast autonomous spectral end-member determination in
hyperspectral data”, In: Proc. of 13th Int. Conf. Appl. Geologic Remote Sens.,
vol. 2. Vancouver, BC, Canada, Apr. 1999, pp. 337–344.
XXXIV. N. Dobigeon and N. Brun, “Spectral mixture analysis of EELS spectrum
images,” Ultramicroscopy, vol. 120, pp. 25–34, Sept. 2012.
XXXV. N. Dobigeon, et al., “A comparison of nonlinear mixing models for vegetated
areas using simulated and real hyperspectral data”, IEEE Journal of Selected
Topics on Applied Earth Observations and Remote Sensing, vol. 7, no. 6, pp.
1869–1878, June 2014.
XXXVI. N. Dobigeon, et al., “Nonlinear unmixing of hyperspectral images: Models
and algorithms,” IEEE Signal Processing Magazine, vol. 31, no. 1, pp. 82–94,
Jan. 2014.
XXXVII. N. Dobigeon, et al., “Nonlinear unmixing of hyperspectral images: Models
and algorithms”, IEEE Signal Processing Magazine, vol. 31, no. 1, pp. 89–94,
Jan. 2014.
XXXVIII. N. Keshava and J. F. Mustard, “Spectral unmixing”, IEEE Signal Processing
Magezine, vol. 19, no. 1, pp. 44–57, Jan. 2002.
XXXIX. O. Eches, N. Dobigeon, C. Mailhes, and J.-Y. Tourneret, “Bayesian
estimation of linear mixtures using the normal compositional model.
Application to hyperspectral imagery”, IEEE Trans. Image Process., vol. 19,
no. 6, pp. 1403–1413, Jun. 2010.
J. Mech. Cont.& Math. Sci., Vol.-15, No.-1, January (2020) pp 248-262
Copyright reserved © J. Mech. Cont.& Math. Sci.
M. Venkata Sireesha et al
262
XL. P.-A. Thouvenin, N. Dobigeon, and J.-Y. Tourneret, “Hyperspectralunmixing
with spectral variability using a perturbed linear mixingmodel”, IEEE Trans.
Signal Process., vol. 64, no. 2, pp. 525–538, Jan. 2016.
XLI. R. Close, P. Gader, J. Wilson, and A. Zare, “Using physics-based
macroscopic and microscopic mixture models for hyperspectral pixel
unmixing”, In: Proc. of SPIE Algorithms and Technologies for Multispectral,
Hyperspectral, and Ultraspectral Imagery XVIII, S. S. Shen and P. E. Lewis,
Eds., vol. 8390. Baltimore, Maryland, USA: SPIE, May 2012.
XLII. R. Heylen, M. Parente, and P. Gader, “A review of nonlinear hyperspectral
unmixing methods”, IEEE Journal of Selected Topics on Applied Earth
Observations and Remote Sensing, vol. 7, no. 6, pp. 1844–1868, Jun. 2014.
XLIII. R. Heylen, M. Parente, and P. Gader, “A review of nonlinear hyperspectral
unmixing methods”, IEEE Journal of Selected Topics on Applied Earth
Observations and Remote Sensing, vol. 7, no. 6, pp. 1844–1868, June 2014.
XLIV. W. Fan, B. Hu, J. Miller, and M. Li, “Comparative study between a new
nonlinear model and common linear model for analysing laboratory
simulated-forest hyperspectral data”, International Journal of Remote
Sensing, vol. 30, no. 11, pp. 2951–2962, Jun. 2009.
XLV. X. Du, A. Zare, P. Gader, and D. Dranishnikov, “Spatial and spectral
unmixing using the beta compositional model”, IEEE Journal of Selected
Topics on Applied Earth Observations and Remote Sensing, vol. 7, no. 6, pp.
1994–2003, Jun. 2014.
XLVI. Y. Altmann, A. Halimi, N. Dobigeon, and J.-Y. Tourneret, “Supervised
nonlinear spectral unmixing using a post nonlinear mixing model for
hyperspectral imagery”, IEEE Transactions on Image Processing, vol. 21, no.
6, pp. 3017–3025, Jun. 2012.
XLVII. Y. Altmann, A. Halimi, N. Dobigeon, and J.-Y. Tourneret, “Supervised
nonlinear spectral unmixing using a post-nonlinear mixing model for
hyperspectral imagery”, IEEE Transactions on Image Processing, vol. 21, no.
6, pp. 3017–3025, June 2012.
XLVIII. Y. Altmann, M. Pereyra, and S. McLaughlin, “Bayesian nonlinear
hyperspectral unmixing with spatial residual component analysis”, IEEE
Trans. Image Process., vol. 1, no. 3, pp. 174–185, Sep. 2015.
XLIX. Y. Altmann, N. Dobigeon, and J.-Y. Tourneret, “Bilinear models for
nonlinear unmixing of hyperspectral images”, In: Proceedings of 3rd
Workshop on Hyperspectral Image and Signal Processing: Evaluation in
Remote Sensing, IEEE, Lisbon, Portugal, Nov. 2011, pp. 1–4.
L. Y. Altmann, S. McLaughlin, and A. Hero, “Robust linear spectral unmixing
using anomaly detection”, IEEE Transactions on Computational Imaging,
vol. 1, no. 2, pp. 74–85, Jun. 2015.

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Abstract:

Wireless sensor nodes have deployed with limited energy sources. The lifetime of a node usually depends on its energy source. The main challenging design issue of the wireless sensor network is to prolong the network lifetime and prevent connectivity degradation by developing an energy-efficient routing protocol. Many research works are done to extend the network lifetime, but still, it is a problem because of the impossibility of recharging. In this paper, we present a hierarchical clustering technique for wireless sensor network called Clustering with Residual Energy and Neighbors (CREN). It is based on two basic parameters, e.g., number of neighbors of a node and its residual energy. We use these properties as a weighted factor to elect a node as a cluster head. A well-known method, LEACH had a high performance in energy saving and the quality of services in the wireless sensor network. Like Low-Energy Adaptive Clustering Hierarchy (LEACH), CREN rotates the cluster head among the sensor nodes to balance the energy consumption. The simulation result shows the proposed technique achieves much higher performance and energy efficiency than LEACH.

Keywords:

Wireless Sensor Networks,Clustering Algorithm,Cluster Head,Energy-efficiency,Residual Energy,LEACH,

Refference:

I. A. John, and K. V. Babu (2017). Two Phase Dynamic Method for Clustering Head Selection in Wireless Sensor Network for Internet of Things Applications, pp: 1228-1232, IEEE WiSPNET conference.
II. C. Li, M. Ye, G. Chen, J. Wu (2005). An energy-efficient unequal clustering mechanism forwireless sensor networks, Proceedings of the 2nd IEEE international conference on mobile ad-hoc and sensor systems (MASS’05).
III. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci. (2002). A survey on sensor networks, IEEE Communications Magazine, 40(8):102–14.

IV. G. H. Raghunandan, Dr. A. S. Rani,S. Y. Nanditha, G. Swathi (2017). Hierarchical Agglomerative Clustering based Algorithm for Overall Efficiency of Wireless Sensor Network, pp: 1290-1293, International Conference on Intelligent Instrumentation and Control Technologies (ICICICT), IEEE.
V. Huamei Qi, Fengqi Liu, Tailong Xiao , and Jiang Su (2018). A Robust and Energy-Efficient Weighted Clustering Algorithm on Mobile Ad Hoc Sensor Networks, MDPI,Algorithms, 11, 116; doi:10.3390/a11080116. J. Mech. Cont.& Math. Sci., Vol.-15, No.-1, January (2020) pp 263-274
Copyright reserved © J. Mech. Cont.& Math. Sci.
Md. Shamim Hossain et al
274
VI. H. S. Lee, K. T. Kim, H. Y. Youn (2006). A new cluster head selection
scheme for long lifetimeof wireless sensor networks, Lecture Notes in
Computer Science, 3983, 519–528.
VII. H. Ayadi, A. Zouinkhi, T. Val, A. van den Bossche, and M. N. Abdelkrim,
“Network Lifetime Management in Wireless Sensor Network,” IEEE Sensors
Journal, 2018.
VIII. K. Akkaya, M. Younis (2004). A survey on Routing Protocols for Wireless
Sensor Networks, Computer Networks (Elsevier) Journal.
IX. K. Akkaya, M. Demirbas, R.S. Aygun (2006). The impact of data
aggregation on the performance of wireless sensor networks: a survey, Wiley
Journal of Wireless Communications and Mobile Computing.
X. O. Younis, S. Fahmay, “HEED: a hybrid, energy-efficient,
distributedclustering approach for ad hoc sensor networks”, IEEE
Transactions on Mobile Computing 3 (4), pp. 366–379, 2004.
XI. S. Selvakennedy, S. Sinnappan, Y. Shang, “A biologically-inspired clustering
protocol for wireless sensor networks”,Computer Communications, 30, pp.
2786–2801, 2007.
XII. S. K. M. Yendamuri, A. Singh, Dr. J. P. Priyadarsini M (2018). An Improved
Three-Layer Clustering Hierarchy for Wireless Sensor Networks: A Proposed
Framework, 9th ICCCNT2018, July 10-12, 2018, IISC, Bengaluru, India.
XIII. T. Gao, R. C. Jin, J. Y. Song, T. B. Xu · Li D. Wang (2010). Energy-Efficient
Cluster Head Selection Scheme Based on Multiple Criteria Decision Making
for Wireless Sensor Networks, Wireless Personal Communication, Springer.
XIV. T. Shu, M. Krunz, S. Vrudhula (2005). “Power balanced coverage-time
optimization for clustered wireless sensor networks”, in Proceedings of ACM
MobiHoc’05, pp. 111–120.
XV. W. Heinzelman, A. Chandrakasan, and H. Balakrishnan (2000). Energyefficient
communication protocol for wireless sensor networks, in the
Proceeding of the Hawaii International Conference System Sciences, Hawaii.
XVI. Z. Cheng, L. Hongbing and H. Yi (2018). Mechanism of immune system
based topology control clustering algorithm in wireless sensor networks,
IEEE.

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Abstract:

Flooding is considered to be one of the worst natural catastrophes effecting million of people throughout the world. Flooding is referred to as potentially destructive abundance of water in a normally dry location. Flooding occurs when water inundate the areas adjacent to the river channel called as the floodplain, causing potential damage to the inhabitants of that area. Thus, a proper flood forecasting system including the development of flood zoning maps, the right of river bed and extent of inundation of floodplain are required for these areas. A composite river hydraulic model provide basis for the development of forecasting system providing timely management of future flood events. Several computer programs are used for the simulation of these models based on either one- or two-dimensional modelling approach. As there are variety of performance capabilities and access to the data required for the development of these hydraulic models, thus it is essential to choose the best software related to those models. A review of various wellknownmodels developed on different software for flood forecasting has been presented in this paper that address the performance of software and the analysis techniques adopted to produce final results.

Keywords:

Flooding,Flood Forecasting,Floodplain Zoning,Hydraulic Model,Dimensional Approach,HEC RAS,MIKE,

Refference:

I. Abdollahi, A., Bajestan, M. S., Hasounizadeh, H. & Rostami, S. 2007.
Comparing the results of Hec-Ras & Mike 11 models in a Segment of
Karoon River. 7th International River Engineering Conference. Shahid
ChamranUniversity, Ahwaz.
II. Aerts, J.C.J.H., Major, D., Bowman, M. and Dircke, P., 2009, Connecting
Delta Cities: Coastal Cities, Flood Risk Management and Adaptation to
Climate Change, VU University Press, Amsterdam p. 96.
III. Ahmad ShahiriParsa, Mohammad Heydari and Noor FarahainbtMohd
Amin., 2013, Introduction to floodplain zoning simulation models through
dimensional approach: International Journal of Advancements Civil
Structural and Environmental Engineering – IJACSE v. 1, p. 20-23
IV. Aronica, G., B. Hankin, and K. Beven. “Uncertainty and equifinality in
calibrating distributed roughness coefficients in a flood propagation model
with limited data.” Advances in Water Resources, 1998: 349-365.
V. Aronica, G., B. Hankin, and K. Beven. “Uncertainty and equifinality in
calibrating distributed roughness coefficients in flood propagation model
with limited data.” Advances in Water Resources, 1998: 349-365.
VI. Bales, J.D., and C.R. Wagner. “Source of uncertainty in flood inundation
maps.” Journal of Flood Risk Management, 2009: 139-147.
VII. Barkhordar, M. &Chavoshian, S. A. 2001. Floodplain zoning. Technical
Workshop on Nonstructural flood management.
VIII. Bemani, M., Torani, M. &Chezgheh, S. 2012. Determination of floodplain
zoning by HEC-RAS Model. Journal of Geography and Environmental
Hazards, No. I, 16.
IX. Booij, M.J., 2005, Impact of climate change on river flooding assessed with
different spatial model resolutions: Journal of Hydrology, v. 303, p. 176–
198.
X. Bouwer, L.M., Crompton, R.P., Faust, E., Höppe, P. and Pielke, Jr., R.A.,
2007, ‘Confronting Disaster Losses’, Science, (318) 753.
XI. Bronstert, A., 2003, Floods and climate change: interactions and impacts:
Risk Analysis, v. 23(3), p. 545-557.
XII. Burby, R.J., 2001, ‘Flood insurance and floodplain management: The US
experience’, Environmental Hazards, (3/3–4) 111–122.
XIII. DHI. MIKE 21 Flow Model: Hydrodynamic Module Scientific
Documentation. MIKE by DHI, 2009.
J. Mech. Cont.& Math. Sci., Vol.-15, No.-1, January (2020) pp 275-282
Copyright reserved © J. Mech. Cont.& Math. Sci.
Engr Uzair Ali et al
282
XIV. Fayazi, M., Bagheri, A., Sedghi, H., Keyhan, K. & Kaveh, F. 2010 Flood
plains simulation of Kashkanriver, Lorestan, Iran with MIKE11& MIKE
FLOOD. 8th International River Engineering Conference. Shahid
ChamranUniversity, Ahwaz.
XV. Fleenor, W. E. Evaluation of Numerical Models… HEC-RAS and
DHIMIKE 11.
XVI. Frank, E., A. Ostan, M. Coccato, and G.S. Stelling. “Use of an integrated
one-dimensional/two-dimensional hydraulic modelling approach for flood
hazard and risk mapping.” In River Basin Management, by R.A. Falconer
and W.R Blain, 99-108. Southhampton, UK: WIT Press, 2001.
XVII. Garrote, L. and Bras, R.L., 1995. A distributed model for real-time flood
forecasting using digital elevation models. Journal of Hydrology, 167(1-4):
279-306.
XVIII. Lin, B., J.M. Wicks, R.A. Falconer, and K. Adams. “Integrating 1D and 2D
hydrodynamic models for flood simulation.” Preceedings of the Institution
of Civil Engineers. Water Management Incorporated, 2006. 19-25.
XIX. Mashhadi, S. S., Rad, M. A., Memari, A. R. & Pour, S. J. 2012.
Determining of limits of river bed and its flow by using HEC-HMS 3.1.0
and Arcview 3.3 software (case study: Kakhk river in Gonabad). The first
National Conference on Desertification.
XX. Mason, D.C., D.M. Cobby, M.S. Horritt, and P.D. Bates. “Floodplain
friction parameterization in two-dimensional river flood models using
vegetation heights derived from airborne scanning laser altimetry.”
Hydrological Processes, 2003: 1711-1732.
XXI. National Disaster Management Authority (NDMA), annual report 2010:
Available at http:// www.ndma.gov.pk/.
XXII. Pappenberger, F., K. Beven, M. Horritt, and S. Blazkova. “Uncertainty in
the calibration of effective roughness parameters in HEC-RAS using
inundation and downstream level observations.” Journal of Hydrology,
2004: 46-69.
XXIII. Patro, S., C. Chatterjee, S. Mohanty, R. Singh, and N.S. Raghuwanshi.
“Flood Inundation Modeling using MIKE FLOOD and Remote Sensing
Data.” Journal of the Indian Society of Remote Sensing, 2009: 107-118.
XXIV. S. Néelz and G Pender. 2009, Desktop review of 2D hydraulic modelling
packages.
XXV. S. Néelz and G Pender. 2010, Benchmarking of 2D hydraulic modeling
packages
XXVI. Smemoe, C.M., E.J. Nelson, A.K. Zundel, and A.W. Miller. “Demonstrating
Floodplain Uncertainty Using Flood Probability Maps.” Journal of the
American Water Resources Association, 2007: 359-371.
XXVII. USGS. The National Map Seamless Server. August 19, 2008.
http://seamless.usgs.gov/products/3arc.php (accessed June 2015).
XXVIII. Werner, M.G.F. “Impact of Grid Size in GIS Based FLood Extent Mapping
Using a 1D Flow Model.” Phys. Chem. Earth Part B-Hydrol. Oceans
Atmos., 2001: 517-522.

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