Archive

Abstract:

Designing an electronic circuit with low power and small area are two important concerns for signal processing designers. Though fast emergence of the new technologies and several reviews over signal and speech processing, the difficulty cannot be fulfilled for the hearing impaired people. Many filter bank algorithms have been discussed on the hearing aid design to extend the efficiency. The conventional design of cascaded Direct Truncation (DT) data path is mainly based on the design of Full Precision Static Floating Point. In this paper, we introduce Static Floating Point Sample Rate Converter (SFP-SRC) with Linear Phase Finite Impulse Response (LPFIR) for hearing aid applications. The Sampling Rate Conversion is done before or after the LPFIR filter with upsampling and downsampling factors. In order to increase efficiency of DSP systems, filter bank algorithms need more than one sampling rate. The proposed method provides minimum delay and excellent Signal to Noise Ratio (SNR) performance when compared to Post Truncation (PT) data path. In order to obtain better performance, many experiments have been conducted. The proposed SFP-SRC is suitable for hearing assistance applications. Hence, it is implemented on 1/3 octave analysis filter bank with umc-90nm CMOS technology at 24 KHz.

Keywords:

Finite Impulse Response,Filter bank algorithms,Digital hearing aid,DSP algorithms,

Refference:

I. A. Gaffar, O. Mencer, W. Luk, and P. Cheung, “Unifying Bit-width Optimization for Fixed-point and Floating-point Designs,” in Proceedings of IEEE Symposium on Field-Programmable Custom Computing, March 2004, pp. 79–88.
II. C. Shi and R. Brodersen, “Automated Fixed-point Data-type Optimization Tool for Signal Processing and Communication Systems,” in Proceedings of Design Automation Conference, 2004, Page Number. 478–483.
III. Chih-Wei Liu, Kuo-Chiang Chang, Ming-Hsun Chuang, and Ching-Hao Lin, “10-ms 18-Band Quasi-ANSI S1.11 1/3-Octave Filter Bank for Digital Hearing Aids.” IEEE Transactions on Circuits And Systems—I: Regular Papers, Volume 60, No. 3, Page Numbrer. 638-649, March 2013
IV. Collette, J.-L.; Barret, Michel; Duhamel, P.; Oksman, J., “On Hybrid Filter Bank A/D Converters with Arbitrary Over-Sampling Rate”, in Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, Page Number. 157-160, 15-17 September 2005
V. D. Menard, R. Rocher, and O. Sentieys, “Analytical fixed-point accuracy evaluation in linear time-invariant systems,” in Proceedings of IEEE Transactions on Circuits and Systems I, Volume. 55, Number. 10, Page Number. 3197–3208, November 2008
VI. Gustafsson, O.; Johansson, H., “Efficient Implementation of FIR Filter Based Rational Sampling Rate Converters Using Constant Matrix Multiplication” , in Proceedings of Fortieth Asilomar Conference on Signals, Systems and Computers, Page Number. 888-891, October 29 2006
VII. H. Li, G. A. Jullien, V. S. Dimitrov, M. Ahmadi, and W. Miller, “ A 2- Digit Mulltidimensional Logarithmic Number System Filter Bank for a Digital Hearing Aid Architecture,” in Proceedings of IEEE International Symposium Circuits Systems, AZ, 2002, Page Number: II 271–350.
VIII. K. S. Chong, B. H. Gwee, and J. S. Chang, “A 16-Channel Low-Power Nonuniform Spaced Filter Bank Core for Digital Hearing Aid,” IEEE Transactions on Circuits Systems I, Volume 53, No. 9, Page Number: 853–857, September 2006.
IX. K.-C.Chang,Y.-W.Chen,Y.-T.Kuo,C.-W.Liu, “A Low Power Hearing Aid Computing Platform using Light Weight Processing Elements, in Proceedings of IEEE International Symposium on Circuits and Systems, Page Number. 2785–2788, May 2012.
X. Kyung-Ju Cho; Ji-Suk Park; Byeong-Kuk Kim; Jin-Gyun Chung; Parhi. K.K., “Design of a Sample-Rate Converter From CD to DAT using Fractional Delay All-pass Filter”, in IEEE Transactions on Circuits and Systems II: Express Briefs, , Volume. 54, Number.1, Page Number. 19-23, January 2007
XI. L.S. Nielsen and J. Sparso, “Designing Asynchronous Circuits for Low Power: An IFIR Filter Bank for a Digital Hearing Aid,” Proeeiding of IEEE , Volume 87, No.2, Page Number: 268-281, February 1999.
XII. N. Ito and T.-L. Deng, “Variable-Bandwidth Filter-bank for Low-Power hearing Aids,” in Proceedings of 3rd International Conference on Image Signal Processing , Page Number. 3207–3201, 2010.
XIII. N. Subbulakshmi, R. Manimegalai. “A survey of filter bank algorithms for biomedical applications” , 2014 International Conference on Computer Communication and Informatics, 2014O. Sarbishei and K. Radecka, “On the Fixed-point Accuracy Analysis and Optimization of FFT units with CORDIC Multipliers”, in Proceedings of IEEE Symposium on Computing Arithmetic (ARITH), August 2011, Page Number. 62–69.
XIV. O. Sarbishei and K. Radecka, “On the Fixed-point Accuracy Analysis and Optimization of FFT units with CORDIC Multipliers”, in Proceedings of IEEE Symposium on Computing Arithmetic (ARITH), August 2011, Page Number. 62–69.
XV. O. Sarbishei, Y. Pang, and K. Radecka, “Analysis of Range Precision for Fixed –Point Linear Arithmetic Circuits with Feedbancks”, in Proceesings of High Level Design Validation and Test Workshop, Page Number. 25-32 2010
XVI. P. P. Vaidyanathan, “Multirate Systems and Filter Banks” , New Jersey: Prentice Hall, 1993.
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XX. Shih-Hao Ou, Kuo-Chiang Chang, Chih-Wei Liu, “ An Energy-efficient, High-Precision SFP LPFIR Filter Engine for Digital Hearing Aids”, in Proceedings of Elsevier on Integration, the VLSI Journal, Volume. 48, Page Number. 230-238, 2015
XXI. Subbulakshmi N &Manimegalai R,‘NLFB: Novel Lifting based Filter Bank for Hearing Aid’, Journal of Computational and TheoriticalNanoscience, vol. 14, no. 2, pp. 1410-1416, June 2017.
XXII. T.-J.Lin,H.-Y.Lin,C.-M.Chao,C.-W.Liu,C.-W.Jen, “A Compact DSP core with Static Floating-Point Arithmetic”, Journal of VLSI Signal Processing, System Signal Image Video, Page Number: 127–138.
XXIII. TianShen and Yong Ching Lim, “Low complexity Frequency-Response Masking Filters using Modified Structure Based on Serial Masking”, 9th European Signal Processing Conference (EUSIPCO 2011) Barcelona, Spain, Page Number: 1400-1404, August 29 – September 2, 2011
XXIV. Urkowitz, Harry, “Parallel Realizations of Digital Interpolation Filters for Increasing the Sampling Rate”, in IEEE Transactions on Circuits and Systems, Volume. 22, Number. 2, Page Number. 146-154, Febraury 1975
XXV. Y. C. Lim, “Frequency-Response Masking Approach for the Synthesis of Sharp Linear Phase Digital Filters,” IEEE Transactions on Circuits and Systems, Volume. CAS-33, PP: 357–364, April 1986.
XXVI. Y. Lian and Y.Wei, “A computationally Efficient Nonuniform FIR Digital Filter Bank for Hearing Aids,” IEEE International Symposium Circuits Systems I Volume 52 No:12, Page Number:2754-2762, December 2005.
XXVII. Y. T. Kuo, T. J. Lin, Y. T. Li, and C. W. Liu, “Design and Implementation of Low Power ANSI S1.11 Filter Bank for Digital Hearing aids,” IEEE Transaction on Circuits and Systems, Volume 57, No.7, Page number 1684-1696, July 2010
XXVIII. Y. Wei and Y. Lian, “A 16-Band Nonuniform FIR Digital Filterbank for Hearing Aid,” in Proeedins of IEEE Biomedical Circuits System, Conference., Page Number:186-189,2006.
XXIX. Ying Wei and Debao Liu , “ Digital FIR Filter Banks with Adjustable Sub-band Distribution for Hearing Aid Systems”, in Proceedings of 8th International Conference on Information and Communication Signal Processing, Singapore, December 2011
XXX. Ying Wei and Debao Liu , “A Reconfigurable Digital Filter Bank for Digital Hearing Aid systems with Variety of Sound Wave Decomposition Plans”, IEEE transactions on Biomedical Engineering,Volume 60, Page Number 6, June 2013
XXXI. Yongzhi Liu, and Zhiping Lin, “Optimal Design of Frequency-Response Masking Filters With Reduced Group Delays”, IEEE Transactions on Circuits And Systems—I: Regular Papers, Volume. 55, No. 6, Page Number: 1560-1570,JULY 2008

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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
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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
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Centre for Public Sector Research, 2015.
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VIII. K. S. Cameron and R. E. Quinn, “Diagnosing and Changing Organizational
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study, Unpublished Doctoral Thesis, Victory University of Technology, 2008.
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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-
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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.
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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.
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Extraction in MR Images”, In Proc. of Inter. Conf. on Adv. in
Sig.Proces., Pune, India, pp. 104-107, 2016.
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Segmentation based on a Hybrid Clustering Technique”, Egyptian
Informatics Journal, vol. 16, no. 1, 2015.
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images”, IOP Conf. Series: Materials Science and Engineering, vol.
336, 012012, 2018.
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75, Springer, Singapore, May 2019.
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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.
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processing for diagnosing brain tumors: A Type-II fuzzy expert system
approach”, Applied soft computing, pp. 285-294, 2011.
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using K-means and subtractive clustering algorithm”, Procedia
Computer Science, vol 54, pp. 764-771, 2015.
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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
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MRI images: combining spatial information with fuzzy c-means
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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.
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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,
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MD Mobin Akhtar et al
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XIII. M. Minelli, M. Chambers and A. Dhiraj, Big Data, Big Analytics: Emerging
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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.
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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.
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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.
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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.
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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,

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