Journal Vol – 14 No -2, April 2019

Abstract:

5G is the latest time of remote correspondence framework. It achieves something the 4G LTE-A, Wi-Max, 3G (UMTS, LTE) and 2G (GSM) structures. 5G execution targets high data rate, condensed inertness, essentialness saving, cost lessening, higher structure limit, and tremendous contraption arrange. The essential time of 5G judgments in Release-15 will be done by Apr-2019 to oblige the early business sending. The second stage in Release-16 is relied upon to be done by Apri-2020 for convenience to the International Telecommunication Union (ITU) as a contender of IMT-2020 advancement. The ITU IMT-2020 assurance demands quickens to 20 Gbps, reachable with wide channel information exchange limits and colossal MIMO. third Generation Partnership Project (3GPP) will submit 5G NR (New Radio) as its 5G correspondence standard recommendation. 5G NR can consolidate lower frequencies (FR1), underneath 6 GHz, and higher frequencies (FR2), more than 24 GHz and into the millimetre waves expand. In any case, the speed and idleness in early associations, using 5G NR programming on 4G gear (non-autonomous), are simply possibly better than anything new 4G systems, evaluated at 15% to half better. Here we completed fast, low dormancy, RAN based putting away advancement. This proposed work is named as LRC, and it is used for % 5G and higher development like 6G, 7G..... Etc.

Keywords:

Low latency,high speed, caching,5G-technology,75GHZ-frequency,

Refference:

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XVIII.M. F. Bari, R. Boutaba, R. Esteves, L. Z. Granville, M. Podlesny, M. G. Rabbani, Q. Zhang, and M. F. Zhani, ―Data Center Network Virtualization: A Survey,‖ IEEE Commun. Surv. Tutor., vol. 15, no. 2, pp. 909–928, Second 2013..

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Abstract:

To investigated the per capita demand and water consumption pattern using Ardino acquisition system and flow meter sensor. The water flow sensors were installed in the outlet pipe from water storage tank to hostel. The Ardino flow meter records the water flow for every moment. Also the student attendance on daily basis were also recorded each day during the survey period. The survey results were analyzed using Microsoft Excel 2016 and Minitab 18. The study results shows that the per capita consumption varies considerably each day the average per capita consumption was found 99.65 ± 21.79.There was a strong correlation found between the number of student available per day and the total water consumed in LPD having The R2 value was 0.8978.which shows that the students are the major consumer and the other categories of water consumption uses very low amount. There was no correlation found between the per capita water consumption in LPD and the maximum and minimum temperature humidity wind speed. There was no effect of humidex found on water consumption per capita LPD. The average water consumption pattern per capita per day shows some random peaks in the graph which mean that there is difference in routines of students. They have different class, sleeping, and wake up timing. The two major peaks observed one in morning time and one in evening time the water consumption. The morning peak between 08:00 to 09:00. While the evening peak starts from 13:00 to 14:00.the morning peak is higher than evening peak but the evening peak is broader than morning peak. Three types of peaking factors were calculated from the study data which are for 15 minutes, hourly and daily factors. In 15 minutes water consumption interval per capita per day average highest peaking factor found in the morning between 8:45 and 9:00 which was 3.0 and in average hourly peaking factor the highest peak factor found between 13:00 and 15:00 which was 2.4.while in average week days water consumption per capita per day the average consumption was high on the Saturday having peak factor of 1.15.

Keywords:

tudent hostel, water consumption pattern, per capita demand, Arduino flow meter,peaking factors,

Refference:

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Abstract:

The mobile networks in 5G must deliver high data rates with less latency. This paper presents the review of theoretical and comparative analysisbetween Orthogonal frequency division multiplexing (OFDM)and Generalised frequency division multiplexing (GFDM)waveformsfor 5G networks. GFDM is one of the promising candidate waveforms for 5G. This waveform supports multi-carrier system with malleable of pulse shaping filter. It supportsMultiple input and Multiple output(MIMO)and provides a high diversity gain. It meets the Industry 4.0 (I4.0) also called as smart factory requirements in with low Out-Of-Band (OOB) emissions. The GFDM transceiver is implemented on national instruments LabVIEW USRP devices and tested successfully for high data rates. The purpose of this paper is to discuss different research areas and evaluate different approaches for 5G Networks. This paper mainly focuses on some research areas such as peak to average power ratio (PAPR), Precoding techniques, index modulations, channel estimation and applications of the signal. The simulation results show that the GFDM outperforms OFDM for5G candidate waveform race. We conclude with several promising directions for future research of GFDM waveform in this paper.

Keywords:

I4.0,OOB, MIMO,GFDM,OFDM,5G,PAPR,Index Modulation,Precoding,

Refference:

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VIII.Bandari, Shravan Kumar, Venkata Mani Vakamulla, and A. Drosopoulos. “Training Based Channel Estimation for Multitaper GFDM System.” Mobile Information Systems, 2017.

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XII.Dahlman, Erik, Stefan Parkvall, and Johan Skold. “4G: LTE/LTE-advanced for mobile broadband,” Academic press, 2013.

XIII.Damnjanovic, A., Montojo, J., Wei, Y., Ji, T., Luo, T., Vajapeyam, M., &Malladi, D. “A survey on 3GPP heterogeneous networks”. IEEE Wireless Communications, vol.18, No.3, 2011

XIV.Datta, Tanumay, Harsha S. Eshwaraiah, and AnanthanarayananChockalingam. “Generalized space-and-frequencyindex modulation.” IEEE Transactions on Vehicular Technology vol. 65, no 7 pp 4911-4924, 2016.

XV.Datta, Jayanta, Hsin-Piao Lin, and Ding-Bing Lin. “A Method to implement Spatial Shift Keying (SSK) technique for Generalized Frequency Division Multiplexing (GFDM) systems.

“XVI.Dias, Joao T., and Rodrigo C. de Lamare. “Unique-Word GFDM Transmission Systems.” IEEE Wireless Communications Letters, 2017.

XVII.Dannenberg, Martin, “Implementation of a 2 by 2 MIMO-GFDM Transceiver for Robust 5G Networks.” Wireless Communication Systems (ISWCS), 2015 International Symposium on. IEEE, 2015.

XVIII.Demel, Johannes, CarstenBockelmann, and Armin Dekorsy. “Evaluation of a software-defined GFDM implementation for industry 4.0 applications.” Industrial Technology (ICIT), 2017 IEEEInternational Conference on. IEEE, 2017.

XIX.Ehsanfar, Shahab, “Interference-Free Pilots Insertion for MIMO-GFDM Channel Estimation.” Wireless Communications and Networking Conference (WCNC), 2017 IEEE. IEEE, 2017.

XX.Ehsanfar, Shahab, “A Study of Pilot-Aided Channel Estimation in MIMO-GFDM Systems.” Smart Antennas (WSA 2016); Proceedings of the 20th International ITG Workshop on. VDE, 2016.

XXI.Ehsanfar, Shahab, “Theoretical Analysis and CRLB Evaluation for Pilot-Aided Channel Estimation in GFDM.” In Global Communication Conference, IEEE, (2016). December 4, pp. 1-7.

XXII.Farhang, Arman, Nicola Marchetti, and Linda E. Doyle. “Low-Complexity Modem Design for GFDM.” IEEE Trans. Signal Processing, vol 64, no 6, pp 1507-1518, 2016.

XXIII.Gaspar, Danilo, Luciano Mendes, and Tales Pimenta. “GFDM BER under Synchronization Errors.” IEEE Communications Letters, 2017.

XXIV.Gaspar, Ivan “Frequency-shift Offset-QAM for GFDM.” IEEE Communications Letters vol.19, No.8, pp 1454-1457, 2015.

XXV.Ghatak, Gourab, “On Preambles With Low Out of Band Radiation for Channel Estimation.” ArXiv preprint arXiv: pp 1608.06098, 2016.

XXVI.Gaspar, Ivan, “GFDM transceiver using precoded data and low-complexity multiplication in the time domain.” arXiv preprint arXivpp 1506.03350 2015.

XXVII.Gill, Harsimranjit Singh, Sandeep Singh Gill, and Kamaljit Singh Bhatia. “A novel approach for physical layer security in future-generation passive optical networks.” Photonic Network Communications, 2017, pp 1-10.

XXVIII.Gerzaguet, Robin, “The 5G candidate waveform race: a comparison of complexity and performance.” EURASIP Journal on Wireless Communications and Networking, vol. 1, no 13, 2017.

XXIX.Jahani-Nezhad, Tayyebeh, Mohammad Reza Taban, and Foroogh S. Tabataba. “CFO estimation in GFDM systems using extended Kalman filter.” Electrical Engineering (ICEE), 2017 Iranian Conference on. IEEE, 2017.

XXX.Lin, David W., and Po-Sen Wang. “On the configuration-dependent singularity of GFDM pulse-shaping filter banks.” IEEE Communications Letters vol.20, no.10, pp 1975-1978, 2016.

XXXI.Li, Fei, “An Interference-Free Transmission Scheme for GFDM System.” Globecom Workshops (GC Wkshps), IEEE, 2016.

XXXII.Lee, Kiwon, “Use of training subcarriers for synchronization in low latency uplink communication with GFDM.” Signal Processing Advances in Wireless Communications (SPAWC), 2016 IEEE 17th International Workshop on. IEEE, 2016.

XXXIII.Lizeaga, Aitor, “Evaluation of WCP-COQAM, GFDM-OQAM and FBMC-OQAM for industrial wireless communications with Cognitive Radio.” Electronics, Control, Measurement, Signals and their Application to Mechatronics (ECMSM), 2017 IEEE International Workshop of. IEEE, 2017.

XXXIV.Michailow, Nicola. “Generalized Frequency Division Multiplexing for 5th Generation Cellular Networks.” IEEE Transactions on Communications vol. 9. No.62, 2014, pp. 3045-3061.

XXXV.Matthé, Maximilian, Luciano Leonel Mendes, and Gerhard Fettweis. “Generalized frequency divisions multiplexing in a Gabor transform setting.” IEEE Communications Letters, vol.18, No.8, 2014, pp 1379-1382

XXXVI.Michailow, Nicola. “Robust WHT-GFDM for the next generation of wireless networks.” IEEE Communications Letters vol.19, No.1, pp.106-109, 2015.

XXXVII.Mesri, Mokhtaria. “Partial Transition Sequence Algorithms for Reducing Peak to Average Power Ratio in the Next Generation Wireless Communications Systems.” Journal of Electrical Systems, vol. 13, no 1, 2017.

XXXVIII.Matthé, Maximilian, Luciano Leonel Mendes, and Gerhard Fettweis. “Space-time coding for generalized frequency division multiplexing.” European Wireless 2014; 20th European Wireless Conference; Proceedings of. VDE, 2014.

XXXIX.Matthé,Maximilian, “Widely linear estimation for space-time-coded GFDM in low-latency applications.” IEEE Transactions on Communications vol. 63, no 11, pp 4501-4509, 2015.

XL.Matthé, Maximilian, “Precoded GFDM transceiver with low complexity time domain processing.” EURASIP Journal on Wireless Communications and Networking, vol 1, pp 138, 2016.

XLI.Matthé, Maximilian, Dan Zhang, and Gerhard Fettweis. “Sphere-decoding aided SIC for MIMO-GFDM: Coded performance analysis.” Wireless Communication Systems (ISWCS), 2016 International Symposium on. IEEE, 2016.

XLII.Matthé, Maximilian, “Short Paper: Near-ML Detection for MIMO-GFDM.” Vehicular Technology Conference, 2015. VTC Fall 2015, IEEE 82nd. 2015.

XLIII.Matthe, Maximilian, Dan Zhang, and Gerhard Fettweis. “Iterative Detection using MMSE-PIC Demapping for MIMO-GFDM Systems.” European Wireless 2016; 22nd European Wireless Conference; Proceedings of. VDE, 2016. XLIV.Matthé, Maximilian, “Widely linear estimation for space-time-coded GFDM in low-latency applications.” IEEE Transactions on Communications, vol. 63, no 11, 2015, pp 4501-4509

XLV.Mokdad, Ali, PaeizAzmi, and Nader Mokari. “Radio resource allocation for heterogeneous traffic in GFDM-NOMA heterogeneous cellular networks.” IET Communications, vol. 12, 2016, pp 1444-1455

XLVI.Nimr, Ahmad. “Optimal Radix-2 FFT Compatible Filters for GFDM.” IEEE Communications Letters, 2017.

XLVII.NING, Xiaoyan, Huimin LUO, and Zhiguo SUN. “Generalized Frequency Division Multiplexing and the reutilizing of Fragmental Spectrum.”

XLVIII.NagarjunaTelagam, S.Lakshmi, K.Nehru, “ Digital audio broadcasting based gfdm transceiver using software defined radio”, International journal of innovative technology and exploring engineering, vol 8, no 5, 2019, pp 273-281.

XLIX.NagarjunaTelagam, S.Lakshmi, K.Nehru, “BER analysis of concatenated levels of encoding in GFDM system using LabVIEW”, Indonesian journal

L.Oh, Hyunmyung, and Hyun Jong Yang. “PAPR Reduction Scheme Using Selective Mapping in GFDM.” The Journal of Korean Institute of Communications and Information Sciences, vol. 41, no 6, pp 698-706, 2016.

LI.Ortega, Andres, Lorenzo Fabbri, and VelioTralli. “Performance evaluation of GFDM over a nonlinear channel.” Information and Communication Technology Convergence (ICTC), 2016 International Conference on. IEEE, 2016

LII.Öztürk, Ersin, ErtugrulBasar, and Hakan Ali Çırpan. “Spatial modulation GFDM: A low complexity MIMO-GFDM system for 5G wireless networks.” Black Sea Conference on Communications and Networking (BlackSeaCom), 2016 IEEE International. IEEE, 2016.

LIII.Schedler, Stephan, and Volker Kühn. “Optimal lattice spacing for GFDM with Gaussian waveform.” Wireless Communications and Networking Conference (WCNC), IEEE, 2016.

LIV.Sharifian, Zahra, “Polynomial-based compressing and iterative expanding for PAPR reduction in GFDM.” Electrical Engineering (ICEE), 2015 23rd Iranian Conference on. IEEE, 2015.

LV.Sameen, Muhammad, InamUllah Khan, and NaziaAzim. “Comparison of GFDM and OFDM with respect of SER,PSD, and PAPR.” IJMCA vol. 4.no 6, 2017, pp 432-438.

LVI.Sharifian, Zahra, “Linear Precoding for PAPR Reduction of GFDMA.” IEEE Wireless Communications Letters, vol. 5, no 5, pp 520-523, 2016.

LVII.Tiwari, Shashank, SuvraSekhar Das, and Kalyan Kumar Bandyopadhyay.”Precoded GFDM System to Combat Inter-Carrier Interference: Performance Analysis.” arXiv preprint arXiv: 2015.

LVIII.Tahara, Tatsuki, “Algorithm for extracting multiple object waves without Fourier transform from a single image recorded by spatial frequency-division multiplexing and its application to digital holography.” Optics Communications, vol 40, no 2, 2017, pp 462-467.

LIX.Tang, Nan, “IQ Imbalance Compensation for Generalized Frequency Division Multiplexing Systems.” IEEE Wireless Communications Letters 2017.

LX.Vilaipornsawai, USA, and Ming Jia. “Scattered-pilot channel estimation for GFDM.” Wireless Communications and Networking Conference (WCNC), IEEE, 2014.

LXI.Wang, Zhenduo, “Bit error rate analysis of generalized frequency division multiplexing with weighted-type fractional Fourier transforms precoding.” IET Communications vol. 11, no 6 pp 916-924, 2017.

LXII.Wang, Po-Sen, and David W. Lin. “Maximum-likelihood blind synchronization for GFDM systems.” IEEE Signal Processing Letters, vol. 23, No.6, pp 790-794, 2016.

LXIII.Wei, Peng, “Low-complexity DGT-based GFDM receivers in broadband channels.” Communication Systems (ICCS), International Conference on. IEEE, 2016.

LXIV.Wei, Peng, “Fast DGT-Based Receivers for GFDM in Broadband Channels.” IEEE Transactions on Communications, vol 64, no 10, pp 4331-4345, 2016.

LXV.Wu, Jinqiu, “Influence of Pulse Shaping Filters on PAPR Performance of Underwater 5G Communication System Technique: GFDM.” Wireless Communications and Mobile Computing 2017.

LXVI.Xiao, Yue, “GFDM with interleaved subcarrier-index modulation.” IEEE Communications Letters vol. 18, no 8, pp 1447-1450, 2014.

LXVII.Yenilmez, Ayhan, TansalGucluoglu, and PiotrRemlein. “Performance of GFDM-maximal ratio transmission over Nakagami-m fading channels.” Wireless Communication Systems (ISWCS), International Symposium on. IEEE, 2016.

LXVIII.Yoshizawa, Atsushi, Ryota Kimura, and Ryo Sawai. “A Singularity-Free GFDM Modulation Scheme with Parametric Shaping Filter Sampling.” Vehicular Technology Conference (VTC-Fall), 2016 IEEE 84th. IEEE, 2016.

LXIX.Zeng, Yonghong, “Fast Algorithms for FBMC and GFDM in Dynamic Spectrum Access.” Wireless Communications and Networking Conference (WCNC), 2017.

LXX.Zhang, Dan. “A Study on the Link Level Performance of Advanced Multicarrier Waveforms under MIMO Wireless Communication Channels.” IEEE Transactions on Wireless Communications, vol. 16, no.4, 2017, pp 2350-2365.

LXXI.Zhang, Wei, “STC-GFDM systems with Walsh-Hadamard transform.” Electronic Information and Communication Technology (ICEICT), IEEE International Conference on. IEEE, 2016.

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LXXIII.Zhang, Dan, “Expectation propagation for near-optimum detection of MIMO-GFDM signals.” IEEE Transactions on Wireless Communications vol. 15, no 2, pp 1045-1062, 2016.

LXXIV.Zhong, Zhipeng, and JunqiGuo. “Bit error rate analysis of a MIMO-generalized frequency division multiplexing scheme for 5th generation cellular systems.” Electronic Information and Communication Technology (ICEICT), IEEE International Conference on. IEEE, 2016.

LXXV.Zhang, Dan, Andreas Festag, and Gerhard Fettweis. “Performance of Generalized Frequency Division Multiplexing Based Physical Layer in Vehicular Communication.” IEEE Transactions on VehicularTechnology, 2017.

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Abstract:

In this paper, we will present the existence of necessary and sufficient conditions for linear systems of the first order delay deferential inequalities and equations to have oscillatory, eventually negative solutions and has ultimately positive solutions. Also, some illustrative examples of each case are given.

Keywords:

Delay, Differential,System, Eventually,Positive,Negative,Oscillatory,Equation,Inequality, Bounded,Solution,

Refference:

I.A.Martin andS.Ruan, “Predator-Prey Models with Delay and Prey Harvesting”, J. Math. Biol., 43:247-267 (2001). II.A.Raghothama, and S.Narayanan, “Periodic Response and Chaos in Nonlinear Systems with Parametric Excitation and time Delay”, Nonlin. Dyn., 27:341-365 (2002).

III.B.T. Bingtuanli, “Oscillations of Delay Differential Equations with Variable Coefficients”, Journal of Mathematical Analysis and Applications, 192, 312-321 (1995).

IV.B.Cahlon and D.Schmidt, “On Stability of Systems of Delay Differential Equations”,Journal of Computational and Applied Mathematics 117, 137-158 (2000).

V.C.T.H.Baker, C.A.H.PaulandD.R. Willé, “A bibliography on the Numerical Solution of Delay Differential Equations”, Numerical Analysis Report 269, Mathematics Department, University of Manchester, U.K(1995).

VI.K. Gopalsamy,“Oscillatory Properties of Systems of First-order Linear Delay Differential Inequalities”, Pacific Journal of Mathematics, Vol. 128, No. 2 (1987).

VII.L.F.Shampine, I.Gladwell, and S.Thompson, “Solving ODEs withMATLAB”, Cambridge Univ. Press, Cambridge(2003).

VIII.Y.KITAMURA and T.KUSANO “Asymptotic Properties of Solutions of Two-dimensional Differential Systems with Deviating Argument”, HIROSHIMA MATH. J.8 , 305-326(1978).

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Abstract:

As the size of the air quality data increases, it is difficult toforecastthe air quality metrics due to the non-stationary and randomization form of data distribution. Air quality prediction refers to the problem of finding the air quality by using statistical inference measures. However, traditional air prediction models are based on static fixed parameters for quality prediction. Also, it is difficult to classify and predict the air quality index for both rural and urban areas due to change in data drift and distribution. PM2.5 is one of the major factor to predict the air quality index (AQI) and its severity level. Due to high noisy and outliers in the PM2.5 data, it is difficult to classify and predict the air quality by using the traditional quality prediction models. In order to overcome these issues, an optimized Bayesian networks based probabilistic inference model is designed and implemented on the air quality data. An IOT enabled Air pollution monitoring system includes a DSM501A Dust sensor which detects PM2.5, PM1.0, MQ series sensor interfaced to a Node MCU equipped with ESP32 WLAN adaptor to send the sensor reading to Thing Speak cloud. In the proposed model, the data is initially gathered from the ICAO records of Safdarjung weather station and pre-processed.An improved discrete and continuous parameter estimation and bayes score optimization are implemented on the air quality prediction process. Experimental results show that the present optimized Bayesian network classify and predicts the air quality data with high less computational error rate and high accuracy. Further the proposed optimized model is applied on the real data which is gathered using IOT enabled gas sensors and the model is giving best results in predicting the air quality Index.

Keywords:

Bayesian Classification Algorithm,IOT,Air Quality Index,Data Pre-processing,

Refference:

I.Ayaskanta Mishra, Air Pollution Monitoring System based on IoT: Forecasting and Predictive Modeling using Machine Learning”, International Conference on Applied Electromagnetics, Signal Processing and Communication (AESPC), 22nd -24th October-2018, Bhubaneswar, Odisha, India, IEEE, Paper ID# 9.

II.C. Li and Z. Zhu, “Research and application of a novel hybrid air quality early-warning system: A case study in China”, Science of The Total Environment, vol. 626, pp. 1421-1438, 2018. Available: 10.1016/j.scitotenv.2018.01.195 [Accessed 20February 2019].

III.Hybrid improved differential evolution and wavelet neural network with load forecasting problem of air conditioning Int. J. Electr. Power Energy Syst. 61, 673–682IV.H. Li, J. Wang, R. Li and H. Lu, “Novel analysis–forecast system based on multi-objective optimization for air quality index”, Journal of Cleaner Production, vol. 208, pp. 1365-1383, 2019. Available: 10.1016/j.jclepro.2018.10.129 [Accessed 20 February 2019.V.https://raw.githubusercontent.com/alyakhtar/AQI-Delhi/master/Data/Original-Data/Original_Combine.csv

VI.K. Gan, S. Sun, S. Wang and Y. Wei, “A secondary-decomposition-ensemble learning paradigm for forecasting PM2.5 concentration”, Atmospheric Pollution Research, vol. 9, no. 6, pp. 989-999, 2018. Available: 10.1016/j.apr.2018.03.008 [Accessed 20 February 2019.

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Abstract:

“Whereas most technologies tend to automate workers on the periphery doing menial tasks, blockchains automate away the center. Instead of putting the taxi driver out of a job, blockchain puts Uber out of a job and lets the taxi drivers work with the customer directly.” — Vitalik Buterin, co-founder Ethereum and Bitcoin Magazine Blockchain has evolved to be the most discussed and potentially disruptive technology and is expected to become a driving force for technology-based business innovations. Although blockchain is still in infancy in terms of technological maturity, experimental adoption and customization are already in progress. One of its' early adopters, Supply chain Management is expecting to find fascinating solutions for its most pressing issues like confidentiality and trust, along with those of the inability to share information between supply chain partners, limitations of IT systems and lack of data standards. This paper is an attempt to seek the applicability of blockchain technology in the business process of Supply Chain Management. The Paper provides a comprehensive map for technical feasibility of a blockchain based supply chain through the distributed concepts including proof of work, consensus, and smart contracts.

Keywords:

Blockchain,Supply Chain Management,Decentralized Applications,

Refference:

I.Agarwal, A., Shankar, R. and Tiwari, M.K., 2007. Modeling agility of supply chain. Industrial marketing management, 36(4), pp.443-457.

IIAkins, B.W., Chapman, J.L., Gordon, J.M.: A whole new world: Income tax considerations of the bitcoin economy (2013), https://ssrn.com/abstract=2394738

III.Alexander Grech and Anthony F. Camilleri. 2017. Blockchain in Education. No. JRC108255. Joint Research Centre (Seville site).

IV.All Cryptocurrencies | Coinlore. coinlore.com. Retrieved August 19, 2018. URL-https://www.coinlore.com/all_coins

V.Bozarth, C.C., Warsing, D.P., Flynn, B.B. and Flynn, E.J., 2009. The impact of supply chain complexity on manufacturing plant performance. Journal of Operations Management, 27(1), pp.78-93.

VI.Bentov, I., Lee, C., Mizrahi, A. and Rosenfeld, M. (2014) „Proof of activity: extending Bitcoin‟s proof of work viaproof of stake [extended abstract]‟, ACM SIGMETRICS Performance Evaluation Review, Vol. 42, No. 3, pp.34–37.

VII.Bhatnagar, R. and Teo, C.C., 2009. Role of logistics in enhancing competitive advantage: A value chain framework for global supply chains. International Journal of Physical Distribution & Logistics Management, 39(3), pp.202-226.

VIII.Blockchain Innovation, A Patent Analysis report, prepared by IP Australia, November 2018

IX.Chopra, S. and Meindl, P., 2015. Supply Chain Management: Strategy, Planning, and Operation, Pearson, 528 pp.

X.Christensen, C., (1997). The Innovator‟s Dilemma. The Revolutionary Book That Will Change the Way You Do Business, 1st ed. Collins Business Essentials, New York

XI.Ferrara, Michael. “Blockchain in Manufacturing: Enhancing Trust, Cutting Costs and Lubricating Processes across the Value Chain.” Cognizant, vol. 1, no. 1, ser. 1, 1 Nov. 2017, pp. 1–32. 1.

XII..Francisco K, Swanson D (2018) The supply chain has no clothes: technology adoption of blockchain for supply chain transparency. Logistics 2:2

XIII.Harland, C., Brenchley, R., and Walker, H., 2003. Risk in supply networks. Journal of Purchasing and Supply Management, 9(2), pp.51-62.

XIV.Iansiti, M. and Lakhani, K.R., 2017. The Truth About Blockchain. Harvard Business Review, 95(1), pp.118-127.

XV.J. Mattila, The blockchain phenomenon: The disruptive potential of distributed consensus architectures, ETLA working papers: Elinkeinoelämän Tutkimuslaitos, Research Institute of the Finnish Economy, 2016 URL https: //books.google.com.pk/books?id=StNQnQAACAAJ.

XVI.LeBlanc, Gannon, “The effects of cryptocurrencies on the banking industry and monetary policy” (2016). Senior Honors Theses. 499.

XVII.Loi Luu, Jason Teutsch, Raghav Kulkarni, and Prateek Saxena. Demystifying incentives in the consensus computer. In Proceedings of the 22Nd ACM SIGSAC Conference on Computer and Communications Security, CCS ’15, pages 706{719. ACM, 2015.

XVIII.Loi L, Duc-Hiep C, Hrishi O, Prateek S, Aquinas H, Making Smart Contracts Smarter CCS‟16, October 24 -28, 2016, Vienna, Austria.

XIX.Miraz, M.H.; Ali, M. Applications of Blockchain Technology beyond Cryptocurrency. Ann. Emerg. Technol. Comput. 2018, 2, 1–6.

XX.M. Mettler, “Blockchain technology in healthcare: The revolution starts here,” in e-Health Networking, Applications, and Services (Healthcom), 2016 IEEE 18th International Conference on. IEEE, 2016, pp. 1–3.

XXI.Melo, M.T., Nickel, S. and Saldanha-Da-Gama, F., 2009. Facility location and supply chain management–A review. European journal of operational research, 196(2), pp.401-412.

XXII.Nash, Kim S., 2016. IBM Pushes Blockchain into the Supply Chain. The Wall Street Journal. Available online: https://www.wsj.com/articles/ibm-pushes-blockchain-into-the-supplychain-1468528824

XXIII.Pradhan, Alex, et al. “Blockchain Fundamentals for Supply Chain: A Guide to the New Boardroom Buzzword.” Gartner, vol. 1, no. 1, ser. 1, 23 Feb. 2018, pp. 1–12. HBLL.

XXIV.Research and Challenges on Bitcoin Anonymity by Jordi Herrera-Joancomarti proceedings of the 9th International Workshop on Data Privacy Management. Springer. LNCS 8872, pp. 1-14. (2014)

XXV.Seetharaman, A., Saravanan, A. S., Patwa, N., & Mehta, J. (2017). Impact of Bitcoin as a World Currency. Accounting and Finance Research, 6(2), 230

XXVI.Satoshi Nakamoto. Bitcoin: A Peer-to-Peer Electronic Cash System, 2008, http://www.bitcoin.org

XXVII.Schwartz, D., Young, N., and Britto, A. (2014) The Ripple Protocol Consensus Algorithm, Ripple Labs Inc White Paper.

XXVIII.Sunny King, Scott Nadal, PPCoin: Peer-to-Peer Crypto-Currency with Proof-of-Stake, 2012

XXIX.The Business Blockchain: Promise, Practice, and Application of the Next Internet Technology by William Mougayar, Vitalik Buterin,ISBN: 978-1-119-30031-1 May 2016.

XXX.Xu, X., Weber, I., Staples M, Liming Z, Jan B, Len B, Cesare P, Paul R, A Taxonomy of Blockchain-Based Systems for Architecture Design. Published by. IEEE, July 2017. DOI, 10.1109/icsa.2017.33. Authors. Xu, X., Weber, I., Staples.

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Abstract:

Internet of Things (IoT) technology is ubiquitous. In the past decade there was an exponential growth in IoT deployments, so as the potential danger of attacks and threats using IoT devices. The privacy of an individual can be breached and the sensitive information can be disclosed if proper security measures are not in place in the IoT device. A patient monitoring system using an IoT device is vulnerable to many such threats. Even centrifuges and atomic reactors were fallen victim of an industrial security breach caused by popular malware like slammer and Stuxnet. Vehicular and personal gadgets are vulnerable to IoT vulnerabilities that may lead to a leak of information to potential insurance companies and thereby increase of premiums. Our own homes including energy meters, IP cameras, and security monitoring systems may be taken control by hackers if there exist vulnerabilities in the IoT devices. This paper, discusses on IoT vulnerabilities by surveying several sectors of IoT and proposes several security measures that can be implemented to minimize those vulnerabilities.

Keywords:

Internet of Things,IoT,Vulnerabilities,,ecurity Issues,Protocols,IoT Security,

Refference:

I.Ahmad-Reza Sadeghi, C. Wachsmann and M. Waidner, “Security and privacy challenges in industrial Internet of Things,” San Francisco, CA, USA, 2015.

II.AndreaZanella, NicolaBui and AngeloCastellani, “Internet of Things for Smart Cities,” vol. 1, no. 1, 2014.

III.D. MOORE, V. PAXSON and STEFAN SAVAGE, “Inside the Slammer Worm,” 2003.

IV.D. Singh, G. Tripathi and A. J. Jara, “A survey of Internet-of-Things: Future vision, architecture, challenges and services,” Seoul, South Korea, 2014.

V.Jason Bau, Elie Bursztein, Divij Gupta and John Mitchell, “State of the Art: Automated Black-Box Web Application Vulnerability Testing,” Berkeley, California, USA, 2010.

VI.Jinesh Ahamed and Amala V. Rajan, “Internet of Things (IoT): Application systems and security vulnerabilities,” Ras Al Khaimah, United Arab Emirates, 2016

VII.Kevin Poulsen, “Slammer worm crashed Ohio nuke plant network,” 2003.

VIII.M. Muneer Bani Yassein, Mohammed Q. Shatnawi and Dua’ Al-zoubi, “Application layer protocols for the Internet of Things: A survey,” Agadir, Morocco, 2016.

IX.NausheenFarha and Sayyada Hajera Begum, “Healthcare IoT: Benefits, vulnerabilities and solutions,” Coimbatore, India, 2018.

X.P Ravi Kiran Varma, Kotari Prudvi Raj and KV Subba Raju, “Android mobile security by detecting and classification of malware based on permissions using machine learning algorithms,” in IEEE International Conference on IoT in Social, Mobile, Analytics and Cloud(I-SMAC), Tiruchengode, 2017.

XI.P. Sethi and S. R. Sarangi, “Internet of Things: Architectures, Protocols, and Applications,” 2017.XII.Rahat Masood, Um-e-Ghazia and Dr. Zahid Anwar, “SWAM: Stuxnet Worm Analysis in Metasploit,” Islamabad, Pakistan, 2011.

XIII.Ravi Kiran Varma Penmatsa and Padmaprabha Kakarlapudi, “Web phishing detection: feature selection using rough sets and ant colony optimisation,” International Journal of Intelligent Systems Design and Computing, vol. 2, no. 2, pp. 102-113, 2018.

XIV.S. M. Riazul Islam, Daehan Kwak, Kabir MD. Humaun and .., “The Internet of Things for Health Care: A Comprehensive Survey,” vol. 3, 2015.

XV.Simone Cirani, Luca Davoli, Gianluigi Ferrari and …, “A Scalable and Self-Configuring Architecture for Service Discovery in the Internet ofThings,” vol. 1, no. 5, 2014.

XVI.Smruti R. Sarangi and Pallavi Sethi, “Internet of Things: Architectures, Protocols, and Applications,” 2017.XVII.Tobias Heer, Oscar Garcia-Morchon and R. Hummen, “Security Challenges in the IP-based Internet of Things,” 2011. XVIII.Tobias Heer, Oscar Garcia-Morchon and Sye Loong Keoh, “Security Challenges in the IP-based Internet of Things,” vol. 61, no. 3, 2011.

XIX.Wei Zhou, Y. Yan Jia, Anni Peng and Yuqing Zhang, “The Effect of IoT New Features on Security and Privacy: New Threats, Existing Solutions, and Challenges Yet to Be Solved,” 2018.

XX.Woo-Sik Bae, “Verifying a secure authentication protocol for IoT medical devices,” Boryeong,Korea, 2017.

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Abstract:

This paper addresses the fundamental issues of developing a speaker independent, dialect modelling system for recognizing the widely spoken, colloquial South Kamrupi dialect of Assamese language. The proposed dialect model is basically designed on Hidden Markov Model (HMM). Hidden Markov Model Toolkit (HTK) is used here as the building block for feature extraction, training, recognition and verification for the model building process. A primary corpus is built as a prerequisite for the empirical study. Altogether, 16 people (9 male, 7 female) are volunteering in the primary corpora building process. The corpora are comprised of one training and two testing sets of recorded speech files. The whole corpora are made up of around 2.5 hours of recordings. The proposed dialect model is trained on South Kamrupi dialect training corpora. A comparative test recognition is carefully designed and carried out which exhibit a recognition correctness of 87.13% for South Kamrupi dialect and 68.52% correctness for the Central Kamrupi dialect. Thus, the findings of this paper evidence that the dialect modelling with proper training has recognized a dialect with better precision.

Keywords:

Dialect Modelling,,Automatic Speech Recognition, Corpora Building,Feature Extraction, HTK,

Refference:

I.B. Kakati, ―Assamese its formation and development‖. Guwahati, India, LBS publication, 2007.

II.B. Ramani, S. L Christina, G. A Rachel, V. S Solomi, M. K Nandwana, A. Prakash, S. A Shanmugam, R. Krishnan, S. K Prahalad and K.Samudravijaya, ―A common attribute based unified hts framework for speech synthesis in Indian languages‖, In Eighth ISCA Workshop on Speech Synthesis, 2013.

III.D. Jurafsky and J. H Martin, ―Speech and language processing‖, volume 3. Pearson London, 2014.

IV.D. S Kulkarni, R. R Deshmukh, P. P Shrishrimal, and S. D Waghmare, ―Htk based speech recognition systems for indian regional languages: A review‖ 2016.

V.G. Aneeja and B. Yegnanarayana, ―Extraction of fundamental frequency from degraded speech using temporal envelopes at high snr frequencies‖, IEEE/ACM Transactions on Audio, Speech, and Language Processing, 25(4):829–838, 2017.

VI.G. Anumanchipalli, R. Chitturi, S. Joshi, R. Kumar,S. P Singh, RNV Sitaram, and SP Kishore, ―Development of indian language speech databases for large vocabulary speech recognition systems‖, In Proc. SPECOM, 2005.

VII.G. Salvi, ―Htk tutorial‖, KTH Royal Institute of Technology, Department of Speech, Music and Hearing, Drottning Kristinas, 31, 2003.

VIII.H. Sarfraz, S. Hussain, R. Bokhari, A. A Raza, I. Ullah, Z. Sarfraz, S. Pervez, A. Mustafa, I. Javed and R. Parveen, ―Speech corpus development for a speaker independent spontaneous urdu speechrecognition system‖, Proceedings of the O-COCOSDA,Kathmandu, Nepal, 2010.

IX.H. Sarma, N. Saharia, and U. Sharma, ―Development and analysis of speech recognition systems for assamese language using htk‖, ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), 17(1):7, 2017.

X.K. Kumar, RK Aggarwal, and A. Jain, ―A hindi speech recognition system for connected words using htk‖, International Journal of Computational Systems Engineering, 1(1):25–32, 2012.

XI.K. Medhi, ―Assamese grammar and origin of the Assamese language‖. Publication Board, Assam, 1988.

XII.K. Tokuda and H. Zen, ―Fundamentals and recent advances in hmm-based speech synthesis‖, Tutorial of INTERSPEECH, 2009.

XIII.L. Besacier, E. Barnard, A. Karpov,and T. Schultz, ―Automatic speech recognition for under-resourced languages: A survey‖, Speech Communication, 56:85–100, 2014.

XIV.L. R Rabiner, ―A tutorial on hidden markov models and selected applications in speech recognition‖, Proceedings of theIEEE, 77(2):257–286, 1989.

XV.M. Dua, RK Aggarwal, V. Kadyan and S. Dua, ―Punjabi automatic speech recognition using htk‖, International Journal of Computer Science Issues (IJCSI), 9(4):359, 2012.

XVI.M. S Liang, R. Y Lyu, and Y. C Chiang, ―Phonetic transcription using speech recognition technique considering variations in pronunciation‖, In Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on, volume 4, pages IV–109. IEEE, 2007.

XVII.R. Das and U. Sharma, ―Extracting acoustic feature vectors of south kamrupi dialect through mfcc‖, In Computing for Sustainable Global Development (INDIACom), 2016 3rd International Conference on, pages 2808–2811. IEEE, 2016.

XVIII.S. L Maguer, I. Steiner, and A. Hewer, ―An hmm/dnn comparison for synchronized text-to-speech and tongue motion synthesis‖, Proc. Interspeech 2017, pages 239–243, 2017.

XIX.S. Mahanta. ―Assamese‖, Journal of the International Phonetic Association, 42(2):217–224, 2012.

XX.S. Young, G. Evermann, M. Gales, T. Hain, D. Kershaw, X. Liu, G. Moore, J. Odell, D. Ollason, and D. Povey, ―The htk book‖, Cambridge university engineering department, 3.5:433, 2015.

XXI.T. F Quatieri, ―Discrete-time speech signal processing: principles and practice‖, Pearson Education India, 2006.

XXII.V. Sneha, G Hardhika, K J. Priya, and D. Gupta, ―Isolated kannada speech recognition using htk —a detailed approach‖, In Progress in Advanced Computing and Intelligent Engineering, pages 185–194. Springer, 2018.

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Abstract:

In strategic management it is necessary to step forward with a strategic approach. One of the important steps in using water resources strategies is to determine and formulate them; there are different methods and models for this purpose, each of which has a specific concept and insight, and the technique and instructions specially follows. Among them, the SWOT matrix that evaluates the strengths, weaknesses, opportunities and system threats is more common and popular. Therefore, in the present study, for the purpose of strategic management of water resources, using the SWOT strategy development method, we will develop appropriate strategies. In this regard, the use of Hurricane method, which is one of the group decision making methods, has been used to extract SWOT matrix factors and then, by examining the importance factor and rank of these factors, using the quantitative strategy planning matrix of the well-known superior strategies group and its strategies will be extracted. In this research, in order to extract strategies for water resources management, a SWOT strategy has been used. Using a quantitative strategy planning matrix, the best group of strategies is selected by examining the internal and external factors affecting the four groups of watersheds. Slowly To this end, at first weaknesses, strengths, opportunities and threats have been extracted by experts and experts in the water area, as well as a review of the studies in that area, the method of storm and group decision making, and then the coefficient of importance and rank of each One of the factors was determined in the assessment matrix. According to the results, the weaknesses overcome the strengths and also water resources are more threatened than opportunities. Hence, strategies of the WT group (defensive strategies) were identified as selected strategies in this way, which allows them to achieve the goals and prospects of water resources.

Keywords:

Waterresources management,strategic analysis,SWOT matrix,and brainstorming,

Refference:

I.Abdul Razagh Damani and Seyed Ahmad Hashmi, Strategic Analysis of Water Resource Management in the Iranshahr City Using SWOT Model. Pal. Jour. 2017, V 16, I, 3, No 2, 436-446.

II.Arabzad, S.M.; Ghorbani, M.; Razmi, J.; Shirouyehzad, H. Employing fuzzy TOPSIS and SWOT for supplier selection and order allocation problem. Int. J. Adv. Manuf. Technol. 2015, 76, 803–818, doi:10.1007/s00170-014-6288-3.

III.Arsić, S.; Nikolić, D.; Živan, Ž. Hybrid SWOT-ANP-FANP model for prioritization strategies of sustainable development of ecotourism in national park Djerdap, Serbia. For. Policy Econ. 11–26, doi:10.1016/j.forpol.2017.02.003

IV.Awad, W.R. The problem of utilization the water resources of the Republic of Iraq under progressive desertification conditions. Geogr. Nat. Resour. 2014, 35, 373–397, doi:10.1134/S1875372814040106.

V.Gong, L.; Jin, C. Fuzzy comprehensive evaluation for carrying capacity of regional water resources. Water Resour. Manag. 2009, 23, 2505–2513, doi:10.1007/s11269-008-9393-y.

VI.Hakimeh Khalifipour, Alireza Soffianaian, Sima Fakheran. Application of SWOT Analysis in Strategic Environmental Planning: A Case Study of Isfahan/ Iran. International Conference on Applied Life Sciences (ICALS2012) Turkey, 2012.

VII.Hamilton, M., Goldsmith, W., Harmon, R., Lewis, D., Srdjevic, B., Goodsite, M., . . . Macdonell, M. Sustainable Water Resources Management: Challenges and Methods Sustainable Cities and Military Installations(pp. 133-144): Springer, 2014.

VIII.Huayi Luo, Jingcheng Wang, Xiaocheng Li and Jiayu Zhu. Layout optimization of large-scale urban water supply network pressure measuring point distribution using genetic algorithm. IEEE. Control Conference (CCC), 2017, 36th Chinese. DOI: 10.23919/ChiCC.2017.8027594

IX.Koch, H., Vögele, S., Kaltofen, M., Grossmann, M., & Grünewald, U. Security of Water Supply and Electricity Production: Aspects of Integrated Management. Water resources management, 2014, 28(6), 1767-1780.

X.Mala-Jetmarova, Helena & Sultanova, Nargiz & Savic, Dragan. Lost in Optimisation of Water Distribution Systems? A Literature Review of System Operation. Environmental Modelling & Software. 93. 209-254. 10.1016/j.envsoft. 2017.02.009.

XI.Menga Ebonzo, A.D.;Liu, X. The use of axiomatic fuzzy set theory in AHP and TOPSIS methodology to determine strategies priorities by SWOT analysis. Qual. Quant. 2013, 47, 2671–2685, doi:10.1007/s11135-012-9679-2.

XII.Mirshahi, Amin and Ghaemi, A, prioritization of water resources development plan based on the vision system, water and sanitation, 2009, Issue 3.

XIII.Nagara, G.; Lam, W.H.; Lee, N.C.H.; Othman, F.; Shaaban, M.G, Comparative SWOT analysis for water solutions in Asia and Africa. Water Resour. Manag. 2015, 125–138, doi:10.1007/s1126901408318.

XIV.Nazer, D.W.; Siebel, M.A.; Van der Zaag, P.; Mimi, Z.; Gijzen, H.J. A. Financial, environmental and social evaluation of domestic water management options in the West Bank, Palestine. Water Resour. Manag. 2010, 4445–4467, doi:10.1007/s11269.010.9667.z.

XV.Nejad Irani, F., Azizi, K and Beikzadeh Y, the effect of value engineering, the performance of the organization, Water and Wastewater Case Study of West Azerbaijan province, productivity management, 2014. (25) 7, 106-81.

XVI.Rehana, S., & Mujumdar, P. Basin Scale Water Resources Systems Modeling Under Cascading Uncertainties. Water resources management, 2014, 28(10), 3127-3142

XVII.Seyyed Reza Mousavizadeh, Sediqeh Khorrami, and Marziyeh Bahreman. Presenting a Strategic Plan of IntegratedWater Resources Management by using SWOT in Bushehr Province. International Journal of Operations and Logistics Management, 2015, Volume 4, Issue 1Pages: 27-42.

XVIII.Zhao, J.; Jin, J.; Zhu, J.; Xu, J.; Hang, Q.; Chen, Y.; Han, D. Water resources risk assessment model based on the subjective and objective combination weighting methods. Water Resour. Manag. 2016, 30, 3027–3042, doi:10.1007/s11269-016-1328-4.

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