Journal Vol – 14 No -4, August 2019

Abstract:

This paper presents the design of elliptical slot antenna for multiband applications. The suggested antenna covers L-band, WIMAX, WLAN and X band. By placing inverted T-shaped stub and three reverse U-shape stubs, the resonating characteristics of the antenna are observed. The resonating frequencies are 1.95, 4.14, 5.05, 5.89 and 9.15 GHz respectively. The designed antenna shows good return loss(S11<-10dB) and compact size which is relevant for most of the wireless applications. Antenna possessing maximum gain of 3.35 dB with efficiency more than 82% in the operating bands. H-plane showing omni-directional radiation pattern and E-plane exhibiting bi-directional radiation pattern. The performance of antenna is analyzed by using HFSS tool.

Keywords:

Microstrip Antenna,Multi band,T-slot,U-slot,

Refference:

I. B. Manimegalai, S. Raju, and V. Abhaikumar, “A multifractal cantor antenna for
multiband wireless applications,” IEEE Antennas Wireless Propag. Lett., vol. 8,
pp. 359–362, 2009
II. B. Sadasivarao, “Analysis of Hybrid Slot Antenna based on Substrate
Permittivity”, ARPN Journal of Engineering and Applied Sciences, Vol. 9, No. 6,
pp 885-890, 2014.
III. B T P Madhav, D Lakshmi Kranthi, “A Multiband MIMO Antenna for S and CBand
Communication Applications”, ARPN Journal of Engineering and Applied
Sciences, Vol 10, No 14, pp 6014-6022, 2015.
IV. B.T.P. Madhav, D. Ujwala, Habibulla Khan, “Multiband slot aperture stacked
patch antenna for wireless communication applications”, International Journal of
Computer Aided Engineering and Technology, Vol. 8, No. 4, pp 413-423, 2016.
IV. Chen, W.-S. and K.-Y. Ku, “Band-rejected design of the printed open slot
antenna for WLAN/ WiMAX operation,” IEEE Trans.Antennas Propag., Vol.
56, No. 4, 1163–1169, Apr. 2008.
VI. D. S. Ramkiran, “Pentagonal Shaped Koch Fractal Monopole Slot Antenna for
Multiband Applications”, ARPN Journal of Engineering and Applied Sciences,
Vol. 12, No. 15, 2017.
VII. G. Augustin, P. C. Bybi, V. P. Sarin, P. Mohanan, C. K. Aanandan, and K.
Vasudevan, “A compact dual-band planar antenna for DCS-1900/PCS/PHS,
WCDMA/IMT-2000, and WLAN applications,” IEEE An-tennas Wireless
Propag. Lett., vol. 7, pp. 108–111, 2008.
VIII. Habibulla Khan, “A Cpw Fed U-Slot Multiband Fractal Antenna”, International
Journal of Pure and Applied Mathematics, Vol 115, No. 7, pp 459-463, 2017.
IX. I.-F. Chen and C.-M. Peng, “Microstrip-fed dual-U-shaped printed monopole
antenna for dual-band wireless communication applications,” Electron. Lett., vol.
39, no. 13, pp.955–956, Jun. 2003.

X. K. Phanisrinivas, Habibulla Khan, “Multiband MSP Spiral Slot Antenna with
Defected Ground Structure”, ARPN Journal of Engineering and Applied
Sciences, Vol. 11, No. 15, 2016.
XI. K.Praveen Kumar, Kumaraswamy Gajula “Fractal Array antenna Design for CBand
Applications”, International Journal of Innovative Technology and
Exploring Engineering (IJITEE), Volume-8 Issue-8 June, 2019 (SCOPUS
Indexed)
XII. K.Praveen Kumar, “Active Switchable Band-Notched UWB Patch Antenna”,
International Journal of Innovative Technology and Exploring Engineering
(IJITEE), Volume-8 Issue-8 June, 2019 (SCOPUS Indexed)
XIII. K.Praveen Kumar, “Circularly Polarization of Edge-Fed Square Patch Antenna
using Truncated Technique for WLAN Applications”, International Journal of
Innovative Technology and Exploring Engineering (IJITEE), Volume-8 Issue-8
June, 2019 (SCOPUS Indexed)
XIV. K.Praveen Kumar, “Triple Band Edge Feed Patch Antenna; Design and
Analysis”, International Journal of Innovative Technology and Exploring
Engineering (IJITEE), Volume-8 Issue-8 June, 2019 (SCOPUS Indexed)
XV. K.Praveen Kumar, Dr. Habibulla Khan “Optimization of EBG structure for
mutual coupling reduction in antenna arrays; a comparitive study” International
Journal of engineering and technology, Vol-7, No-3.6, Special issue-06, 2018.
page 13- 20. (SCOPUS Indexed)
XVI. K.Praveen Kumar, Dr. Habibulla Khan “Active PSEBG structure design for low
profile steerable antenna applications” Journal of advanced research in dynamical
and control systems, Vol-10, Special issue-03, 2018. (SCOPUS Indexed)
XVII. K.Praveen Kumar, Dr. Habibulla Khan, “Design and characterization of
Optimized stacked electromagnetic band gap ground plane for low profile patch
antennas” International journal of pure and applied mathematics, Vol 118, No.
20, 2018, 4765-4776. (SCOPUS Indexed)
XVIII. K V L Bhavani, Habibulla Khan, “Multiband Slotted Aperture Antenna with
Defected Ground Structure for C And X-Band Communication Applications”,
Journal of Theoretical and Applied Information Technology, Vol 82, No 3, pp
454-461, 2015.
XIX. M Ajay babu, “Flared V-Shape Slotted Monopole Multiband Antenna with
Metamaterial Loading”, International Journal of communications Antenna
propagation, Vol 5, No 2, pp 93-97, 2015.
XX. P Syam Sundar, Sarat K Kotamraju, T V Ramakrishna, “Novel Miniatured Wide
Band Annular Slot Monopole Antenna”, Far East Journal of Electronics and
Communications, Vol 14, No 2, pp 149-159, 2015.
XXI. T V Rama Krishna, “Strip Loaded Closed Loop Resonator Based Multiband
Defected Ground Structured Antenna”, Journal of Engineering and Applied
Sciences, Vol 11, No 6, pp 1417-1422, 2016.
XXII. W.-C. Liu and C.-F. Hsu, “Dual-band CPW-fed Y-shaped monopole antenna for
PCS/WLAN application”, Electron. Lett., vol. 41, pp.390–391, 2005.
XXIII. Y.-L. Kuo and K.-L. Wong, “Printed double-T monopole antenna for 2.4/5.2
GHz dual-band WLAN operations,” IEEE Trans. Antennas Propag., vol. 51, no.
9, pp. 2187–2192, Sep. 2003.

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Abstract:

This paper deals with estimation ofthe stress-strength reliability for modified exponentiated Lomax distribution the suggested approach biased on using different estimation methods such as, Maximum likelihood method, Moment method, Least square method and Shrinkage methods, numerical study via MATLAB software, has been done and comparison between the obtained results has been carried out according to mean square error, the results showed that the effectiveness of these estimators which evaluated using Monte-Carlo simulation study.

Keywords:

Modified Exponentiated Lomax Distribution,Stress-Strength (S-S),Shrinkage Estimation,Least Square,Maximum likelihood estimation,

Refference:

I. Abdul-Moniem, I. B. Recurrence relations for moments of lower generalized
order statistics from exponentiated Lomax distribution and its
characterization. Journal of Mathematical and Computational Science, 2(4),
999-1011 .2012.
II. Bander Al-zahrani. An extended Poisson –Lomax distribution. Journal
advances in mathematics,4(2), 79-89.2015.
III. El-Bassiouny, A. H., Abdo, N. F., &Shahen, H. S. Exponential Lomax
distribution. International Journal of Computer Applications, 121(13).2015.
IV. Fatima, K., Jan, U., & Ahmad, S. P. Statistical Properties of Rayleigh Lomax
distribution with applications in Survival Analysis. Journal of Data
Science, 16(3), 531-548.2018.
V. Kareem,A.Nabeel ,A. Comparison between five estimation methods for
reliability function of weighted Rayleigh distribution by using simulation.
Journal of mathematical theory and modeling,4(6), 123-137 .2014.
VI. Kundu, D., &Raqab, M. Z. Generalized Rayleigh distribution: different
methods of estimations. Computational statistics & data analysis, 49(1), 187-
200. 2005.

VII. Kundu, D., &Raqab, M. Z. Estimation of R= P (Y< X) for three-parameter
Weibull distribution. Statistics & Probability Letters, 79(17), 1839-1846.2009.
VIII. Oguntunde, P. E., Khaleel, M. A., Ahmed, M. T., Adejumo, A. O., &
Odetunmibi, O. A. A New Generalization of the Lomax Distribution with
Increasing, Decreasing, and Constant Failure Rate. Modelling and Simulation
in Engineering, 2017.
XI. Pathak, A., &Chaturvedi, A. Estimation of the reliability function for fourparameter
exponentiated generalized Lomax distribution. International Journal
of Scientific and Engineering Research, 5(1), 1171-1180.2013.
X. Pathak, A., &Chaturvedi, A. Estimation of the reliability function for twoparameter
exponentiated Rayleigh or Burr type X distribution. Statistics,
Optimization & Information Computing, 2(4), 305-322. 2014.
XI. Shams, T. M. The Kumaraswamy-generalized Lomax distribution. Middle-East
Journal of Scientific Research, 17(5), 641-646.2013.
XII. Salman,A.Eman,A. On the reliability estimation in multicomponent stressstrength
models different failure distribution. Master theses, university of
Baghdad, college of education Ibn-Haithem.2018.
XIII. Salman.A.N and Hamed.A.M. Estimating the shape parameter for the power
function distribution through shrinkage Technique International Journal of
Science and Research, 78-96.2017.
XIV. Zaka.A, Akhter.A.SandFarooq.N.Methods of Estimating the parameters of
power function distribution .Journal of statistics21 PP 90-102.2014.

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Abstract:

Digital currency a well-known term in past few years that grabbed the attention all over specially in financial sectors. Bit coin is a modern name use for digital currency in today’s world. Many names can be found in the market but Bit coin was first to show trust in digital currency and adopting it as a legal currency. Transactions made using block chain are well secure and transparent to user for monitoring. All signatures are stored in a distributed ledger that keeps the record for transactions happened. Decentralized network is use in block chain technology that connects all the nodes in a network. In this paper, the concept of block chain technology has been incorporated to improve the logistics process using secure digital currency and automate the payment process using bit coins and eliminating third parties interactions. Proposed workflow and conceptual model will help the supply chain management for secure and monitored delivery of goods to end customers. Model will provide advantages for logistics department that includes tracking, transactions updates, transparency, less documentation, on time delivery etc. SHA 256 is used in hashing for data encryption. Customer can also monitor their ordered material status. Proposed conceptual model can be used for all kind of shipment methods i.e. by air, by road, by sea. Paper includes comparison table that will highlight importance of block chain technology.

Keywords:

Bit coin,Distributed Ledger,Logistics,Signatures,Hash,

Refference:

I Aafaf Ouaddah, Anas Abou Elkalam, and Abdellah Ait Ouahman, “FairAccess:
a new Blockchain-based access control framework for the Internet of Things,”
Security and Communication Networks, pp. 5943-5964, February 2017.
II Bushra Mukri, “Blockchain Technology in Supply Chain Management: A
Review,” International Research Journal of Engineering and Technology
(IRJET), vol. 5, no. 6, pp. 2497-2500, June 2018.
III David Schwartz, Noah Youngs, and Arthur Britto, “The Ripple Protocol
Consensus Algorithm,” Ripple Labs Inc, p. 8, 2014.
IV G.D. Knott, “Hashing Functions,” The Computer Journal, vol. 18, no. 3, pp. 265
– 278, March 1975.
V Jae Kwon, “Tendermint: Consensus without Mining,” GitHub, vol. 10, 2014.

VI Krystsina Sadouskaya, “Adoption of Blockchain Technology in Supply Chain
and Logistics,” XAMK, Bachelor’s Thesis 2017.
VII Laura Jutila, “The blockchain technology and its applications in the financial
sector,” Aalto University, Bachelor Thesis 2017.
VIII Ramakrishna M.V, E. Fu, and E. Bahcekapili, “A Performance Study of Hashing
Functions for Hardware Applications,” In Proc. of Int. Conf. on Computing and
Information, pp. 1622-1636, 1994.
IX Rhonda R. Lummus and Robert J. Vokurka, “Defining supply chain
management: a historical perspective and practical guidelines,” Industrial
Management & Data Systems, vol. 99, no. 1, pp. 11-17, 1999.
X Saifedean Ammous, “Blockchain Technology: What is it good for?” Social
Science Research Network, August 2016.
XI Sarah Meiklejohn et al., “A Fistful of Bitcoins: Characterizing Payments Among
Men with No Names,” Proceedings of the 2013 conference on Internet
measurement conference, pp. 127-140, October 2013.
XII Zibin Zheng, Shaoan Xie, Hongning Dai, Xiangping Chen, and Huaimin Wang,
“An Overview of Blockchain Technology: Architecture, Consensus, and Future
Trends,” 2017 IEEE International Congress on Big Data, pp. 557-564, June
2017.

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Abstract:

This venture manages the immediate method for estimating driver weakness is estimating the condition of the driver for example sluggishness. So it is imperative to recognize the laziness of the driver to spare life and property. This undertaking is pointed towards building up a model of tiredness identification framework. This framework is a constant framework which catches picture consistently and measures the condition of the eye as indicated by the predetermined calculation and gives cautioning whenever required. . For executing this framework a few OpenCv libraries are utilized including Haar-course. The whole framework is actualized utilizing Raspberry-Pi.

Keywords:

Raspberry Pi,Open Cv,Camera,python IDLE,

Refference:

I. Akira Kuramori, Noritaka Koguchi, “Evaluation of Effects of Drivability on
Driver Workload by Using Electromyogram,”
2004.

III. Erez Dagan, Ofer Mano, Gideon P. Stein, et al, “Forward Collision Warning
with a Single Camera,” Proc. Intelligent Vehicles Symposium, pp. 37- 42, 2004.
IV. L.M. Bergasa, J. Nuevo, M.A. Sotalo, and M. Vazquez, “Real-time system for
monitoring driver vigilance,” Proc. IEEE Intelligent Vehicle Symposium, pp. 78-
83, 2004.
V. Lal, S. K. L., Craig, et al,“Development of an Algorithm for an EEG-based
Driver Fatigue Countermeasure,” Journal of Safety Research, vol. 34, pp. 321-
328, 2003.
VI. Luis M. Bergasa, Jesús Nuevo, “Real- Time System for Monitoring Driver
Vigilance,” IEEE Trans. Intelligent Transportation Systems, vol. 7, no. 1, pp.
63-77, 2006.
VII. Nikolaos P, “Vision-based Detection of Driver Fatigue,” Proc. IEEE
Internetional Conference on Intelligent Transportation, 2000.
VIII. Qiong Wang, Jingyu Yang, Mingwu Ren, and Yujie Zheng, “Driver Fatigue
Detection: A Survey,” Proc. Of the 6th World Congress on Intelligent Control
and Automation, pp. 8587- 8591, 2006.
IX. Royal D, “Volume I – Findings report; national survey on distracted and driving
attitudes and behaviours, 2002,” The Gallup Organization, Washington, D.C.,
Tech. Rep., DOT HS 809 566, 2003.
X. Wen-Bing Horng, Chih-Yuan Chen, Yi Chang, et al, “Driver Fatigue Detection
Based on Eye Tracking and Dynamic Template Matching,” Proc. of the 2004
IEEE International Conference on Networking, Sensing & Control, pp. 7-12,
2004
XI. Yoshihiro Takei, and Yoshimi Furukawa, “Estimate of driver’s fatigue through
steering motion,” Proc. IEEE International Conference on Systems, Man and
Cybernetics, vol. 2, pp. 1765-1770, 2005.

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Abstract:

Sentiment analysis is one of the recent important subjects in classification filed that recently growing using deep learning. With the spread use of internet, many rising social media, known forums, survey sites, as well as a lot of bloggers produce massive amount of information in shape of customer sentimental assessments, feelings, point of view, debate, opinion around various social news, products, trademark, and protocols, videos etc. Text analysis is an important subject for any system that deals with strings to extract the useful data. In this paper, the effective methods of deep learning will been applied for job with sentiment analysis to get rid of the text analyzing problems and applied some solutions to these problems, by using recurrent neural network (Gated Recurrent Unit (GRU)). In addition, noisy words will been removed to reduce the search space. In order to test the system performance, a set of tests was applied on three datasets. The first and second datasets are collected data from IMDB that consist of movie reviews expressed through long sentences of English, and the third dataset is collection of twitter using the Twitter Search API to collect these tweets by using keyword search, these tweets in English words with short sentences. The conducted tests on the developed system gave accuracy that range 88% - 68%, and the time will been reduced with percentage about 89% when compared with the results of other newly published works. Experimental results on Datasets demonstrate that our proposed models can learn effective features and obtain superior performance over the baseline models.

Keywords:

Gated recurrent unit,Deep Learning,Recurrent neural network,Sentiment analysis,

Refference:

I. Bengio, Y., Boulanger-Lewandowski, N., and Pascanu, R., “Advances in
Optimizing Recurrent Networks”, IEEE International Conference on
Acoustics, Speech and Signal Processing, pp. 8624-8628, 2013.
II. Bradbury, J., Merity, S., Xiong, C., & Socher, R., “Quasi-Recurrent
Neural Networks”, 5th International Conference on Learning
Representations, 2016.
III. Bengio, Y., “Learning Deep Architectures for AI”, Foundations and
Trends in Machine Learning Volume 2, no 1, pp 1-127, 2009.
IV. Collobert, R., “Deep Learning for Efficient Discriminative Parsing”, 14th
International Conference on Artificial Intelligence and Statistics
(AISTATS), Fort Lauderdale, FL, USA. Volume 15, pp. 224-232, 2011.
V. Chung, J., Gulcehre, C., Cho, K., and Bengio, Y., “Empirical Evaluation
of Gated Recurrent Neural Networks on Sequence Modeling”, arXiv
preprint arXiv: 1412.3555, 2014.
VI. Cho, K., Van Merriënboer, B., Bahdanau, D.,and Bengio, Y., “On the
Properties of Neural Machine Translation: Encoder–Decoder
Approaches”, arXiv preprint arXiv:1409.1259, 2014.

VII. Deng, L., and Wiebe, J., “Sentiment propagation via implicature
constraints”, Proceedings of the 14th Conference of the European
Chapter of the Association for Computational Linguistics, pages 377–385
2014.
VIII. Feng, S., Wang, Y., Liu, L., Wang, D., and Yu, G., “Attention based
hierarchical LSTM network for context-aware microblog sentiment
classification”, Springer Science Business Media, LLC, part of Springer
Nature, Volume 22, Issue 1, pp 59–81, 2019.
IX. Hridoy, S. A. A., Ekram, M. T., Islam, M. S., Ahmed, F., and Rahman, R.
M., “Localized twitter opinion mining using sentiment analysis”, springer
article, Vol. 2, 2015.
X. Hamouda, A., Marei, M., and Rohaim, M., “Building Machine Learning
Based Senti-word Lexicon for Sentiment Analysis”, Journal of advances
in information technology, vol. 2, no. 4, November 2011.
XI. Janane, S. K. , Keerthana, M. S. and Subbulakshmi, B., “Hybrid
Classification For Sentiment Analysis Of Movie Reviews”, International
journal of engineering sciences & research technology, ISSN: 2277-9655,
2018.
XII. Jivani, A. G., “A Comparative Study of Stemming Algorithms”, IJCTA
journal, ISSN: 2229-6093, volume 2, 2011.
XIII. Mohammad, S. M., “Sentiment Analysis: Detecting Valence, Emotions,
and Other Affectual States from Text”, Emotion Measurement. DOI:
Elsevier Ltd 2016.
XIV. Mouthami, K., Devi, K. N., and Bhaskaran, V. M., “Sentiment Analysis
and Classification Based on Textual Reviews”, international conference
on information communication and embedded systems, pp. 271-276,
2013.
XV. Meyer, D., Hornik, K., and Feinerer, I., “Text Mining Infrastructure in
R”, Journal of statistical software, Volume 25, Issue 5, 2008.
XVI. Medsker, L., Jain, L. C., “Recurrent Neural Network design and
application”, Departments of Physics and Computer Science and
Information Systems, American University, Washington, D.C., CRC
Press, 2001.
XVII. Nikita P.Katariya1, M. S. Chaudhari., “Text Prepeocessing For Text
Mining Using Side Information”, International Journal of Computer
Science and Mobile Applications, Vol.3, Issue. 1, 2015.
XVIII. Pal, S., Ghosh, S., and Nag, A., “Sentiment Analysis in the Light of
LSTM Recurrent Neural Networks”, International Journal of Synthetic
Emotions, Volume 9, Issue 1, January-June (IJSE), 9(1), 33-39 2018.
XIX. Prabowo, R., & Thelwall, M., “Sentiment analysis: A combined
approach”, Journal of Informetrics, vol. 3, no. 2, 143-157, 2009.
XX. Ramasubramanian C., R.Ramya, “Effective Pre-Processing Activities in
Text Mining using Improved Porter’s Stemming Algorithm”,
International Journal of Advanced Research in Computer and
Communication Engineering, Vol. 2, Issue 12, 2013.

XXI. Rachel Tsz-Wai Lo, He, B., and Ounis, I., “Automatically building a
stopword list for an information retrieval system”, Journal on Digital
Information Management: Special Issue on the 5th Dutch-Belgian
Information Retrieval Workshop (DIR),Volume 5, Pp. 17-24, 2005.
XXII. Shuqin Gu, Lipeng Zhang and Yuexian Hou, “A Position-aware
Bidirectional Attention Network for Aspect-level Sentiment Analysis”,
27th International Conference on Computational Linguistics, pages 774–
784, Santa Fe, New Mexico, USA, August 20-26, 2018.
XXIII. Singh, V., Saini, B., “An Effective Tokenization Algorithm for
Information Retrieval Systems”, in the 1st international Conference on
data Mining, DMIN-2014, Banglore, and DOI: 10.5121, pp. 109–119,
2014.
XXIV. Vijayarani, S., Ilamathi, M. J., and Nithya, M., “Preprocessing
Techniques for Text Mining – An Overview”; International Journal of
Computer Science & Communication Networks, Volume 5 ,Number 1,
Pp. 7-16, ISSN 2249-5789, 2015.
XXV. Wang, X., Wu, P., Liu, G., Huang, Q., Hu, X., and Xu, H., “Learning
performance prediction via convolutional GRU and explainable neural
networks in e-learning environments”, Springer-Verlag GmbH Austria,
part of Springer Nature 2019.

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Abstract:

Microprocessors design consist of many micro level chips that reaches to a state where thermal upsurge occurs due to rapid processing of data and effect (reduce) their efficiency in many different aspects. That production of heat can cause disintegration which makes the chips disable of doing many function they are assign to perform. Embedded devices are designed to combine hardware and software, software integration can insert to hardware to perform some specific function. Multicore embedded devices are in different shapes and dimension. It has various applications on larger scale in networking and nuclear powerhouses to small multimedia players printers, automobiles, cameras mobile handset due to higher demand of power the energy becomes the major concern of the multicore devices for this a thermal aware scheduling algorithm has been proposed that consider the migration of load from higher state to that of lower state and considers all type of tasks and forecast them according to the priority by maintaining the previous history. The proposed technique also considers various thermal values by consulting the previous priorities of task on multicore systems. Migration policy is used to share load from one core to another the algorithm efficiently decreases almost 3℃ temperatures at 40% utilization and the energy utilization is 221.3 J which is 3.12 % improved as compare to the global EDF.

Keywords:

Storm,Dynamic Power Management,Multicore Devices,Real-time systems,

Refference:

I. Alexandru Andrei, PetruEles, Zebo Peng, Marcus T. Schmitz, Bashir M. Al
Hashimi, “Energy optimization of multiprocessor systems on chip by voltage
selection,” IEEE Transaction on VLSI, vol 50, no.3, 2007.
II. C. J. Lasance, “Thermally Driven Reliability Issues in Microelectronic
Systems: Status-quo and Challenges”. Microelectronics Reliability, pp. 1969–
1974, December 2003.
III. FarzanFallah and MassoudPedram. Standby and active leakage current
control and minimization in cmosvlsi circuits. IEICE Transactions, 88-
C(4):509–519, 2005.

IV. G. Petrone, G. Seagnuolo, R. Teodorescu, ‘”Reliability Issues In Photovoltaic
Power Processing Systems”, IEEE Traansactions, July, 2008.
V. H. Khan, Q. Bashir, and M. U. Hashmi, “Scheduling based Energy
Optimization Technique in multiprocessor Embedded Systems,” in 2018
International Conference on Engineering and Emerging Technologies
(ICEET). doi:10.1109/iceet1.2018.8338643, 2018.
VI. H. Khan, M. U. Hashmi, Z. Khan, and R. Ahmad, “Offline Earliest Deadline
first Scheduling based Technique for Optimization of Energy using STORM
in Homogeneous Multi- core Systems,” IJCSNS Int. J. Comput. Sci. Netw.
Secur. VOL.18 No.12, December 2018, vol. 18, no. 12, pp. 125–130, 2018.
VII. H. Khan, S. Ahmad, N. Saleem, M. U. Hashmi, and Q. Bashir, “Scheduling
Based Dynamic Power Management Technique for offline Optimization of
Energy in Multi Core Processors,” Int. J. Sci. Eng. Res. Vol. 9, Issue 12,
December-2018, vol. 9, no. 12, pp. 6–10, 2018.
VIII. H. Khan, M. U. Hashmi, Z. Khan, R. Ahmad, and A. Saleem, “Performance
Evaluation for Secure DES-Algorithm Based Authentication & Counter
Measures for Internet Mobile Host Protocol,” IJCSNS Int. J. Comput. Sci.
Netw. Secur. VOL.18 No.12, December 2018, vol. 18, no. 12, pp. 181–185,
2018.
IX. Jian-Jia Chen, Shengquan Wang, and Lothar Thiele. Proactive speed
scheduling for real-time tasks under thermal constraints. In Proceedings of
the 2009 15th IEEE Symposium on Real-Time and Embedded Technology
and Applications, RTAS ’09, pages 141–150, Washington,DC, USA, 2009.
IEEE Computer Society.
X. Mehdi Kamal, A. Iranfar, A. Afzali-Kusha, M.Pedram,”A Thermal Stressaware
Algorithm for Power and Temperature Management of MPSoCs” In
EDAA, 2015.
XI. Matt Bach. How ambient temperatures affect your pc. Temperatures-Affect-
Your-PC-158/, Written on August 15, 2012.
XII. PratyushKumar,LotharThiele,”Thermally Optimal Stop-GO Scheduling of
Task Graphs with Real Time Constraints,”In ASP-DAC, IEEE Press, pp.
123-128, 2011.
XIII. Q. Bashir, H. Khan, M. U. Hashmi, and S. Ali zamin, “A Survey on
Scheduling Based Optimization Techniques in Multi-Processor Systems,” in
Proceedings of the 3rd International Conference on Engineering & Emerging
Technologies (ICEET), Superior University, Lahore, PK, 7-8 April, 2016.,
2016.

XIV. ShekharBorkar. Design challenges of technology scaling. IEEE Micro,
19(4):23–29, July 1999.
XV. Stephen H. Gunther, Frank Binns, Douglas M.Carmean, and Jonathan C.
Hall. Managing the impacincreasing microprocessor power consumption.
(Q1):9, February 2001.
XVI. T. S. Rosing, K. Mihic, G. De Micheli, “Power and Reliability Management
of SoCs,”In IEEE Transaction Very Large Scale Integrated System (VLSI),
vol. 15. no.4, pp.391-403, 2007.
XVII. ThidapatChantem, Xiaobo Sharon Hu, and Robert P. Dick.Temperatureaware
scheduling and assignment for hard real-time applications on mpsocs.
IEEE Trans. VLSI Syst., 19(10):1884–1897, 2011.

 

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Abstract:

In this paper, we will in general propose an incorrect multiplier factor that is quick at any rate essentialness proficient. The projected approximate multiplier factor is to around the operands to the closest exponent of 2. By doing this it improves the speed. The arranged technique is appropriate to each marked and unsigned increases. It's higher precision in contrast with existing multipliers. The brief rough multiplier factor is considered in 2 picture procedure applications i.e., picture honing and smoothing, that outcomes in decreases in power utilization, postponement and semiconductor check contrasted and an unequivocal multiplier factor.

Keywords:

Approximate computing,accuracy,multipliers,high speed,DSP,

Refference:

I. A. Momeni, J. Han, P. Montuschi, and F. Lombardi, “Structure and
assessment of unpleasant blowers for increment,” IEEE Trans. Comput., vol.
64, no. 4, pp. 984–994, Apr. 2015.
II. C.- H. Lin and C. Lin, “High precision inaccurate multiplier with screw up
alteration,” in Proc. IEEE 31st Int. Conf. Comput. Plan, Sep. 2013, pp. 33–
38.
III. C. Liu, J. Han, and F. Lombardi, “A low-control, predominant inaccurate
multiplier with configurable fragmentary slip-up recovery,” in Proc. Conf.
Show. (DATE), 2014, pp. 1–4.
IV. E. J. Ruler and E. E. Swartzlander, Jr., “Data subordinate truncation contrive
for parallel multipliers,” in Proc. 31st Asilomar Conf. Sign, Circuits Syst.,
Nov. 1998, pp. 1178–1182.
V. G. Zervakis, K. Tsoumanis, S. Xydis, D. Soudris, and K. Pekmestzi,
“Structure viable evaluated enlargement circuits through partial thing gap,”
IEEE Trans. Incredibly Large Scale Integr. (VLSI) Syst., vol. 24, no. 10, pp.
3105–3117, Oct. 2016.
VI. H. R. Mahdiani, A. Ahmadi, S. M. Fakhraie, and C. Lucas, “Bio-breathed life
into dubious computational squares for viable VLSI execution of fragile
enlisting applications,” IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 57, no.
4, pp. 850–862, Apr. 2010.
VII. J. Liang, J. Han, and F. Lombardi, “New estimations for the unflinching
nature of evaluated and probabilistic adders,” IEEE Trans. Comput., vol. 63,
no. 9, pp. 1760–1771, Sep. 2013.
VIII. K.- J. Cho, K.- C. Lee, J.- G. Chung, and K. K. Parhi, “Structure of low-botch
fixed-width changed slow down multiplier,” IEEE Trans. Enormous Scale
Integr. (VLSI) Syst., vol. 12, no. 5, pp. 522–531, May 2004.
IX. K.Praveen Kumar, “Active Switchable Band-Notched UWB Patch Antenna”,
International Journal of Innovative Technology and Exploring Engineering
(IJITEE), Volume-8 Issue-8 June, 2019
X. K.Praveen Kumar, “Circularly Polarization of Edge-Fed Square Patch
Antenna using Truncated Technique for WLAN Applications”, International
Journal of Innovative Technology and Exploring Engineering (IJITEE),
Volume-8 Issue-8 June, 2019
XI. K.Praveen Kumar, “Triple Band Edge Feed Patch Antenna; Design and
Analysis”, International Journal of Innovative Technology and Exploring
Engineering (IJITEE), Volume-8 Issue-8 June, 2019
XII. K.Praveen Kumar, Dr. Habibulla Khan “Active PSEBG structure design for
low profile steerable antenna applications” Journal of advanced research in
dynamical and control systems, Vol-10, Special issue-03, 2018

XIII. K.Praveen Kumar, Dr. Habibulla Khan “Optimization of EBG structure for
mutual coupling reduction in antenna arrays; a comparitive study”
International Journal of engineering and technology, Vol-7, No-3.6, Special
issue-06, 2018. page 13- 20
XIV. K.Praveen Kumar, Dr. Habibulla Khan, “Design and characterization of
Optimized stacked electromagnetic band gap ground plane for low profile
patch antennas” International journal of pure and applied mathematics, Vol
118, No. 20, 2018, 4765-4776
XV. K.Praveen Kumar, Kumaraswamy Gajula “Fractal Array antenna Design for
C-Band Applications”, International Journal of Innovative Technology and
Exploring Engineering (IJITEE), Volume-8 Issue-8 June, 2019
XVI. P. Kulkarni, P. Gupta, and M. D. Ercegovac, “Trading exactness for power a
multiplier plan,” J. Low Power Electron., vol. 7, no. 4, pp. 490–501, 2011.
XVII. R. Venkatesan, A. Agarwal, K. Roy, and A. Raghunathan, “MACACO:
Modeling and examination of circuits for inaccurate enlisting,” in Proc.
IEEE/ACM Int. Conf. Comput.- Aided Design (ICCAD), Oct. 2011, pp. 667–
673.
XVIII. S. Narayanamoorthy, H. A. Moghaddam, Z. Liu, T. Park, and N. S. Kim,
“Essentialness capable construed enlargement for mechanized sign taking
care of and gathering applications,” IEEE Trans. Incredibly Large Scale
Integr. (VLSI) Syst., vol. 23, no. 6, pp. 1180–1184, Jun. 2015.
XIX. S. Suman et al., “Picture redesign using geometric mean channel and gamma
change for WCE iamges,” in Proc. 21st Int. Conf., Neural Inf. Technique.
(ICONIP), 2014, pp. 276–283.
XX. V. Gupta, D. Mohapatra, A. Raghunathan, and K. Roy, “Low-control
propelled sign planning using evaluated adders,” IEEE Trans. Comput.-
Aided Design Integr. Circuits Syst., vol. 32, no. 1, pp. 124–137, Jan. 2013.

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Abstract:

The infilled frame in the construction industry is divided into two types of structural and non-structural ones. Masonry infilled frames are used because of the architecture performance or the structural performance. Building frames in the peripheral and intermediates sections of the building are filled with masonry walls as a separator or sound and thermal insulation, which causes the difference in the behavior of these frames with the empty frames. This type of walls is called the infilled frame and the mechanism consists of a frame and infilled frame is called an infilled reinforced frame. Infilled frames, especially in the event of moderate and severe earthquakes, collide with their environment frame, and the interaction created between them changes the behavior of the concrete frame. In this study, using the ABAQUS software, an analytical study was carried out on the effect of masonry infilled frame and its impact on the seismic behavior of reinforced concrete frames with moderate height. After modeling the 4-story building frame and defining the plastic range for its materials, the structure under the dynamic load of the earthquake is mapped with accelerometer and horizontal and vertical load of earthquakes. According to the results, the structure energy has increased significantly after applying the infilled frame effect, which is due to the increasing the stiffness of the frame and the absorption of more force from the earthquake. Also, the final strain in the middle of the wall is due to an increase in the displacement of the structure with increasing the height, and the other reason is due to the lower wall stiffness in a vertical direction along it.

Keywords:

The Effect of Infilled Frames,Concrete Building,Moment-Resisting Frame,Building,

Refference:

I. Al-Chaar, G. Evaluating Strength and Stiffness of Unreinforced Masonry
Infill Structures. U.S. Army Corps of Engineers, under project
622784AT41, 2002.
II. Asteris P.G, “Lateral stiffness of brick masonry infilled plane frames.” J.
of Struct. Eng., 129(8), 1071-1079, 2003.
III. Bazan, E., & Meli, R,. Seismic Analysis of Structures With Masonry
Walls. In Proc., 7th World Conf. on Earthquake Engineering (Vol. 5, pp.
633-640). Tokyo: International Association of Earthquake Engineering
(IAEE), 1980.
IV. Buonopane.S. G.and White R. N, “Pseudo-dynamic testing of masonry
infilled reinforced concrete frame.” J. of Struct. Eng., 125(6), 578-589,
1999.
V. El-Dakhakhni W. W. Three-Strut Model For Concrete Masonry-Infilled
Steel Frames. J. of Struct. Eng., 129(2), 177-185, 2003.
VI. Giordano, A., Mele, E. and Luca, A.,”Modeling of historical masonry
structures comparison of different approachs through a case study”,
Engineering Structures., Vol. 24, pp. 1057-1069, 2002.
VII. Holmes, M. Steel Frames with Brickwork and Concrete Infilling. In ICE
Proceedings (Vol. 19, No. 4, pp. 473-478). Thomas Telford, 1961.
VIII. Ioannis Koutromanos , Andreas Stavridis, P. Benson Shing, Kaspar
Willam ,”Numerical modeling of masonry infilled RC frames subjected to
seismic loads”, Computers and Structures 89, 1026–1037, 2011.
IX. Kappos, A. J., Ellul F. Seismic Design and Performance Assessment of
Masonry Infilled R/C Frames. Proceedings of the 12th World Conference
on Earthquake Engineering, 989 on CD-ROM, New Zealand, 2000.
X. L.D, Decanini, G.E. Fantin,. Simplified models of Masonry Included in
porches. Features lateral stiffness and strength limit state, Argentine
Conference on Structural Engineering, Buenos Aires, Argentina, Vol.2,
pp.817-836, 1986.
XI. Liauw T. C., Kwan K. H. Nonlinear Behavior of Non-Integrel Infilled
Frames. J Computer and Structure., 18(3), 551-560, 1984.
XII. Moghaddam H. A,. Dowling P. J. The State of The Art in Infilled Frames.
ESEE Res.Rep. No 87-2, Imperical Coll. of Sci. and Technol., Civ.Engrg.
Dept., London, England, 1987.
XIII. Moghaddam H. A,. Dowling P. J. The State of The Art in Infilled Frames.
ESEE Res.Rep. No 87-2, Imperical Coll. of Sci. and Technol., Civ.Engrg.
Dept., London, England, 1987.

XIV. Moghaddam H. A., “Lateral load behavior of masonry infilled steel frames
with repair and retrofit” J. of Struct. Eng., 130(1), 56-63, 2004.
XV. Murty C. V. R.., Jain S. K. Beneficial Influence of Masonry Infill Wallson
Seismic Performance of RC Frame Buildings. Proceedings of 12th WCEE,
P. 1790, 2000.
XVI. Paulay, T,. Priestley, M. J. N. Seismic design of reinforced concrete and
masonry buildings. John Wiley & Sons, Inc., New York, NY, USA, 1992.
XVII. Polyakov S. V.”Masonry in framed buildings.” Translated by G. L. Cairns
in 1963. National Lending Library for Science and Technology, Boston
Spa,Yorkshire, U.K., 1956.
XVIII. Riddington, J. R., and Stafford Smith, B. , “Analysis of infilled frames
subjected to racking with design recommendations.” The Structural
Engineer, Vol.55, No. 6, pp 263-268, 1977.
XIX. Stafford-Smith B. Lateral Stiffness of Infilled Frames. J. Struct.
Div.ASCE, 88(ST6), 183-199, 1962.
XX. Stafford-Smith B., Carter C. A Method of Analysis for Infilled Frames.
Proceedings of the Institution of Civil Engineers, Vol. 44, pp. 31-48, 1969.
XXI. Tasnimi A. A., Mohebkhah A. Effect of Infill Vertical Irregularity on
Seismic Demands of RC Buildings. 2nd International Conference on
Concrete and Development, BHRC, Tehran, Iran, 2005.

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Abstract:

System on chip design enables more and more IP core integration to meet demands of era of multimillion gate chips. The new levels of integration present significant challenges to provide compatible STD interfaces and flexible bus architectures. IP cores which are constituents of SOCs are designed with many different interfaces and common protocols. In paper proposing well define standard interface, the open core protocol for a hybrid method of AHB bus based architecture. The hybrid approach of AHB bus architecture defines a set of bus interface to make easy basic and rupture read/write transactions.AHS as well define inner shared bus architecture with multiplexers which can accommodate a small number of IP cores facilitating multi master, multi slave operation s simultaneously. OPC-has been selected since it is release to the public and OCP-IP features cross bar/partial cross bar based inter connect and realizes various techniques. The tradeoffs for using OCP interface with AHB bus architecture are concluded in terms of orthogonality, performance, power and bandwidth. Each memory sub system achieved its maximum bandwidth because of OCP-interface.

Keywords:

Pipe line transaction,lock transaction,single transaction,and burst transaction,SoC,

Refference:

I. Advanced Microcontroller Bus Architecture (AMBA) Specification Rev 2.0 &
3.0, http://www.arm.com.
II. Choi, J.-T. Kong, S.-K. Eo, “Fast and Accurate TransactionLevel Modeling of an
Extended AMBA2.0 Bus Architecture,” Design, Automation, and Test in
Europe, pages 138-139, 2005.
III. Kim Y.-T., T. Kim, Y. Kim, C. Shin, E.-Y. Chung, K.-M. Lo C.-K. and R.-S.
Tsay, “Automatic Generation of Cycle Accurate and Cycle Count Accurate
Transaction Level Bus Models from a Formal Model,” Asia and South Pacific
Design Automation Conference, pages 558-563, 2009.
IV. Open Core Protocol (OCP) Specification, http://www.ocpip.org/home.

V. Schirner.G and R. Domer, “Quantitative Analysis of Transaction Level Models
for the AMBA Bus,” Design, Automation, and Test in Europe, 6 pages, 2006.
VI. owstuffworks.com
VII. 21st Century Airships, Inc., High Altitude Platforms, at
http://www.21stcenturyairships.com/
VIII. Geostationary Orbits, in Wikpedia, at
http://en.wikipedia.org/wiki/Geostationary_orbit (last accessed Sept. 28, 2004).
IX. TWUF, Broadband Takes to the Skiesrt Techdirt, Get Your Wireless
Broadband By Stratellite,

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Abstract:

Copulas distinguish the dependence among random vectors components as opposed to marginal and joint distributions, which can be directly observed, thus,so the copulas are considered as a hidden dependence among random vectors. Hence , the copulas could be defined as a structure that connects the joint distribution with the marginal distribution based on the non-parametric estimation with the use of the kernel function by the existence of the copula as it is considered as a tool hugely used in the modern statistics and more used in the non-parametric estimations; besides indicating the general characteristics of the estimator and selecting the appropriate bandwidth through the simulation process. A comparison was carried out between transformation estimator and Beta estimator and local likelihood transformation(LLTE) estimator in the estimation of the probability density function , using bimodel normal distribution. The results of simulation showed , according to the measurement of comparison used , that the best method is the method of (LLTE), where V. good estimations and easily to be implemented have been obtained while reducing boundary effect problems.

Keywords:

Copula functions,Transformation kernel,Beta kernel,LocalLikelihood transformation Estimator,

Refference:

I. A .Charpentier, Fermanian, J.D. and Scaillet,O.,(2007).”The estimation
of copulas: Theory and practice”.
II. A .Sklar., (1959), “Fonctions de répartition à n dimensions et leurs
marges”, Publications de l’Institut de Statistique de l’Université de Paris,
8, 229-231.
III. B.Nelsen, R, (2007).” An introduction to copulas”. Springer Science &
Business Media
IV. C. Genest, and R.J. MacKay, (1986a), “The joy of copulas: Bivariate
distributions with uniform marginals”, The American Statistician, 40,
280-283.
V. C. Genest, and R.J. MacKay,(1986b), Copules Archimédiennes et
familles de lois bidimensionnelles dont les marges sont données, The
Canadian Journal of Statistics, 14, 145-159
VI. C .Loader, 2006. Local regression and likelihood. Springer Science &
Business Media..
VII. G.Geenens, A .Charpentier, and Paindaveine, D. (2014). “Probit
transformation for nonparametric kernel estimation of the copula
density”. arXiv:1404.4414.
VIII. H. Joe., 1997. “Multivariate models and multivariate dependence
concepts”. Chapman and Hall/CRC.
IX. I. Gijbels, and Mielniczuk, J. (1990). “Estimating the density of a copula
function.Communications in Statistics – Theory and Methods”,
19XVIII:445–464.
X. K. Wen, and Wu, X., (2018). “Transformation-Kernel Estimation of
Copula Densities”. Journal of Business & Economic Statistics, (justaccepted),
pp.1-36.
XI. Munaf Yousif, H. (2005). “Comparing nonparametric estimators for
probability density functions”, Ph. D. dissertation, Department of
Statistics, Baghdad University.
XII. R .Lokoman.Yusof,.F,(2018). “Parametric Estimation Methods For
Bivariate Copula In Rainfall Application”, Jurnal Teknologi (Sciences &
Engineering), 81I ,pp1–10
XIII. S.Zhang, and Karunamuni, R.J., (2010). “Boundary performance of the
beta kernel estimators”. Journal of Nonparametric Statistics, 22I, pp.81-
104.

XIV. T .Nagler., (2018). “kdecopula: An R Package for the Kernel Estimation
of Bivariate Copula Densities”. Journal of Statistical Software. Volume
84, Issue 7.XVII
XV. T .Nagler, (2016). “kdecopula: An r package for the kernel estimation of
copula densities”. arXiv preprint arXiv:1603.04229
XVI. W. Scott, D. and Terrell, G. R. (1987). “Biased and unbiased crossvalidation
in density estimation”. Journal of the American Statistical
Association, 82(400):1131–1146.
XVII. W. Silverman, B. (1986).” Density Estimation for Statistics and Data
Analysis”. Chapman and Hall.
XVIII. X. S. Chen, (1999). “Beta kernel estimators for density functions”.
Computational Statistics & Data Analysis, 31XVIII:131–145.
XIX. X. S. Chen, and Huang, T.M., (2007). “Nonparametric estimation of
copula functions for dependence modelling”. Canadian Journal of
Statistics, 35XVIII, pp.265-282.
XX. Yan J (2007). “Enjoy the Joy of Copulas: With a Package copula.”
Journal of Statistical Software, 21VII, 1–21.

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