Archive

Abstract:

The research study aim to improve the performance of fruit quality by two approaches, first by applying kernel technique combined with specific classification method support vector machine (SVM) with error-correcting output codes for fruit categorization and then by cross validation . It is measured by analyzing the different mention kernel selection on color and shape features. Two coding design method such as one-vs.-one and one- vs.- all are examined with three commonly used kernel function linear, polynomial (cubic) and Radial Basis Function (Gaussian). The Experiment was conducted on fruit dataset created from fruit 360 dataset with six categories such as Apples, Avacados, Bananas, Cherrys, Grapes and lemons. The accuracy obtained for the fruit category with 98% accuracy was enhanced by the proposed method by the use of kernel technique selection resulted to 99%. However kernel choice highly depends on the parameter used for fruit categorization is introduced and discussed. The Experiments was carried out to find the best SVM kernel among linear, cubic and Gaussian for fruit categorization. The Experiment also focuses on evaluation process using cross validation methods kfold and hold out which resulted in a better accuracy for the classification model.  The results show that the proposed method provides very stable and successful fruit classification performance over six categories of fruits. The coding design one- vs. - one performed better when compared to one- vs. - all with respect to accuracy and training speed.

Keywords:

Multiclass SVM,ECOC,kernel technique,KFold validation,

Refference:

I A.Bhargava, A.Bansal, Fruits and vegetables quality evaluation using computer vision: A review. Journal of King Saud University – Computer and Information Sciences (2018), https://doi.org/10.1016/j.jksuci.2018.06.002

II C.Maaoui, & A.Pruski, (2008, July). A comparative study of SVM kernel applied to emotion recognition from physiological signals. In 2008 5th International Multi-Conference on Systems, Signals and Devices (pp. 1-6). IEEE.

III C.Ouyang, D.Li, J.Wang, S.Wang, & Y.Han, (2012, October). The research of the strawberry disease identification based on image processing and pattern recognition. In International Conference on Computer and Computing Technologies in Agriculture (pp. 69-77). Springer, Berlin, Heidelberg.

IV C. Sammut and G. I. Webb, Eds., “Holdout Evaluation,” in Encyclopedia of Machine Learning and Data Mining, Boston, MA: Springer US, 2017, p. 624.

V Donahue, Jeff, et al. “Decaf: A deep convolutional activation feature for generic visual recognition.” arXiv preprint arXiv:1310.1531 (2013).

VI EzgiiErturk, Ebru AkapinarSezer “A comparision of some soft computing methods for software fault prediction” Expert system with applications,Elsevier, pp1872-1879,vol.42, 2015.

VII F.Al-Shargie, T.B.Tang, N.Badruddin, & M.Kiguchi, (2018). Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach. Medical & biological engineering & computing, 56(1), 125-136.

VIII Fruits 360 Dataset on Kaggle. https://www.kaggle.com/moltean/fruits. last visited on 06.07.2019

IX G.Muhammad, (2015). Date fruits classification using texture descriptors and shape-size features. Engineering Applications of Artificial Intelligence, 37, 361-367.

X H. M.Zawbaa, M.Abbass, M.Hazman, & A. E.Hassenian, (2014, November). Automatic fruit image recognition system based on shape and color features. In International Conference on Advanced Machine Learning Technologies and Applications (pp. 278-290). Springer, Cham.

XI L.Qiang, C.Jianrong, L.Bin, D.Lie, & Z.Yajing, (2014). Identification of fruit and branch in natural scenes for citrus harvesting robot using machine vision and support vector machine. International Journal of Agricultural and Biological Engineering, 7(2), 115-121.

XII M.Achirul Nanda, K. Boro Seminar, D. Nandika, & A. Maddu, (2018). A comparison study of kernel functions in the support vector machine and its application for termite detection. Information, 9(1), 5.

XIII P. Refaeilzadeh, L. Tang, and H. Liu, “Cross-Validation,” in Encyclopedia of Database Systems, L. LIU and M. T. ÖZSU, Eds. Boston, MA: Springer US, 2009, pp. 532–538.

XIV R. S. Chora’s, “Image Feature Extraction Techniques and their Applications for CBIR and Biometrics Systems”.International Journal of Biology and Biomedical Engineering, 2007, 1(1), 6–16.

XV S.Fatima, and M. Sesehashayee, (2020). Healthy Fruits Image Label Categorization through Color Shape and Texture Features Based on Machine Learning Algorithm, International Journal of Innovative Technology and Exploring Engineering, ISSN: 2278-3075, Volume-9 Issue-3.

XVI S. R.Dubey, & A. S. (2012). Robust approach for fruit and vegetable classification. Procedia Engineering, 38, 3449-3453.

XVII S. M. Iqbal, A.Gopal, P. E., Sankaranarayanan, & A. B. Nair, (2016). Classification of selected citrus fruits based on color using machine vision system. International journal of food properties, 19(2), 272-288.

XVIII Shepperd, D. Bowes, and T. Hall, “Researcher bias: The use of machine learning in software defect prediction,” Software Engineering, IEEE Transactions on, vol. 40,no. 6, pp. 603-616, 2014.

XIX S.Ibrahim, N. A.Zulkifli, N.Sabri, A. A.Shari, & M. R. M.Noordin, (2019). Rice grain classification using multi-class support vector machine (SVM). IAES International Journal of Artificial Intelligence, 8(3), 215.

XX T. G. Dietterich, G.Bakiri, 1995. Solving Multiclass Learning Problems via Error-Correcting Output Codes. Journal of Artificial Intelligence Research, 2, pp. 263-286.

XXI Y.Zhang, & L. Wu, (2012). Classification of fruits using computer vision and a multiclass support vector machine. Sensors, 12(9), 12489-12505.

XXII Z.Wen, B.Li, R.Kotagiri, J.Chen, Y.Chen, & R.Zhang, (2017, February). Improving efficiency of SVM k-fold cross-validation by alpha seeding. In Thirty-First AAAI Conference on Artificial Intelligence.

XXIII Z.Yan, Y.Yang, (2014). Application of ecocsvms in remote sensing image classification. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(2), 191.

XXIV Z.Yan, & Y.Yang, (2014). Performance analysis and coding strategy of ECOC SVMs. International Journal of Grid and Distributed Computing, 7(1), 67-76

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Abstract:

A series of glasses by melt quenching method fabricated for spectroscopic investigations of Dy³⁺ ions doped Antimony (Sb)-Magnesium (Mg)-Strontium (Sr) Oxyfluoroborate (BSbMgFS) glasses. The structural and optical characterizations such as XRD, Raman, UV-visible-NIR absorption spectroscopy, photoluminescence (PL) (excitation and emission), were skilled to study the various properties of the glasses. Amorphous nature of present glass confirm from the broad peaks of XRD.  The transitions from lowest energy state to excited state in RE³⁺ ions were identified using optical UV-visible-NIR absorption spectra. By using Judd-Ofelt theory the J-O intensity parameters Ω_λ (λ = 2, 4, 6) have been evaluated from experimental (f_exp) and calculated (f_cal) oscillator strengths. The value of Ω₂ is higher than Ω₄ and Ω₆ and follows the trend Ω₂˃ Ω₆˃ Ω₄. This confirms the high covalency of Dy³⁺ ion with ligands and more asymmetric environment around the rare earth ion in host. The emission of light from glass system was concluded through PL spectra (Excitation and emission) for Dy³⁺ion. In the present work branching ratio of ⁴F_9/2→⁶H_13/2transition is obtained higher than 50% (0.55). The highest readings of A_R, β_R and σ_se are obtained for the transition n ⁴F_9/2→⁶H_13/2 (yellow).Hence, this can be consider as an appropriate mechanism for lasing action. Gain band width (Δλ_eff x σ_se)and optical-gain (σ_se x τ_R) were found to be high for BSbMgFSDy01 and this suggest that BSbMgFSD01 glasses were appropriate for optical amplifier. In the present study of Dy³⁺ -doped glasses, BSbMgFSD05 has shown highest emission with a Y/B ratio of 2.73 which is useful for white-LED applications. BSbMgFSDy05 glass is suitable for white light emitting devices and lasers applications in the visible region at 575 nm upon excitation of 425 nm.

Keywords:

Photoluminescence,Dy3+ -doped glasses,Judd-Ofelt theory,PL spectra,

Refference:

I. A. Lira, A. Speghini, E. Camarillo, M. Bettinelli, U. Caldino, Spectroscopic evaluation of Zn (Po3): Dy3+ glass as active medium of solid state laser, Opt. Mater. 38 (2014) 188.

II. A.S. Rao, Y.N. Ahammed, R.R. Reddy, T.V.R. Rao, Spectroscopic studies of Nd3+-doped alkali fluoroborophosphate glasses, Opt. Mater. 10 (1998) 245–252.

III. A. Thulasiramudu, S. Buddhudu, Optical characterization of Sm3+ and Dy3+ doped ZnO-PbO-B2O3 glasses, Spectrochim Acta Part A. 67 (2007) 802-807.

IV. B. R. Judd, Optical absorption intensities of rare earth ions, Phys. Rev. 127 (1962) 750.

V. C. Gorller-Walrand, K. Binnemans, Handbook on the Physics and Chemistry of Rare Earths, Spectral Intensities of f-f Transitions, vol. 5, Elsevier/North-Holand, Amsterdam, 1998, 101-264.

VI. C.K. Jorgenson, B.R. Judd, Hypersensitive pseudoquadrapole transition in Lanthanides, Mol. Phys. 8 (1964) 281–290.

VII. C. Nageswara Raju, S.Sailaja, S. Hemasundara Raju, S.J.Dhoble, U.Rambabu, Young-Dahl Jho, B.Sudhakar Reddy, Emission analysis of CdO–Bi2O3–B2O3 glasses doped with Eu3+ and Tb3+,Ceramic.International 40(2014) 7701–7709.

VIII. D.K. Sardar, W.M. Bradly, R.M. Yow, J.B. Gruber, B. Zandi, J. of Luminescence 106 (2004) 195-203.

IX. D. Rajesh, Y.C. Ratnakaram, M. Seshadri, A. Balakrishna, T. Satya Krishna, Structural and luminescence properties of Dy3+ ion in strontium lithium bismuth borate glasses J. Lumin. 132 (2012) 841-849.

X. G. Chinna Ram, T. Narendrudu, S. Suresh, A. Suneel Kumar, M.V. Sambasiva Rao, V. Ravi Kumar, D. Krishna Rao, Investigation of luminescence and laser transition of Dy3+ion in P2O5-PbO-Bi2O3 -Dy2O3 glasses, Optical Materials 66 (2017) 189-196.

XI. G. S. Ofelt, Intensities of crystal spectra of rare earth ions, J. Chem. Phys. 37 (1962) 511.

XII. G. Venkata Rao, C.K. Jayasankar., “Dy3+-doped tellurite based tungsten zirconium glasses: Spectroscopy study”, J. Mol. Struct. 1084 (2015) 182-189.

XIII. H.A. Othman, G.M. Arzumanyan, D. Moncke, The effect of alkaline earth oxides and cerium concentration on the spectroscopic properties of Sm/Ce doped lithium alumino-phosphate glasses Opt. Mater. 62 (2016) 689–696.

XIV. J. Juarez-Batalla, A.N. Meza-Rocha, G.Munoz, H.I.Camarillo, U.Caldino, Luminescence properties of Tb3+-doped zinc phosphate glasses for green laser application, Opt Mater. 58(2016) 406–411.

XV. Kenyon A.J, “Recent developments in rare-earth doped materials for optoelectronics, Prog. J. Quantum Electron, 26(2002) 225–284.

XVI. K. Jaroszewski, P. Głuchowski, M. Chrunik, R. Jastrz, A. Majchrowski, D. Kasprowicz, Near-infrared luminescence of Bi2ZnOB2O6:Nd3+/PMMA composite, Optical Materials 75 (2018) 13-18.

XVII. K.S.V. Sudhakar, M. Srinivasa Reddy, L. Srinivasa Rao, N. Veeraiah, Influence of modifier oxide on spectroscopic and thermoluminescence characteristics of Sm3+ ion in antimony borate glass system, J. of Luminescence 128 (2008) 1791– 1798.

XVIII. K. Swapna, Sk. Mahamuda, A. Srinivasa Rao, M. Jayasimhadri, T. Sasikala, L. Rama Moorthy, Optical absorption and luminescence characteristics of Dy3+ doped Zinc Alumino Bismuth Borate glasses for lasing materials and white LEDs, Journal of Luminescence 139 (2013) 119 -124.

XIX. K. Vijaya Babu, Sandhya Cole, Luminescence properties of Dy3+-doped alkali lead alumino borosilicate glasses, Ceramics International(2018) 9080-9090.

XX. K.V. Krishnaiah, K. Upendra Kumar, C.K. Jayasankar, Mater. Exp. 3 (2013) 61-70.

XXI. L. Eyring (Ed.), Progress in the Science and Technology of the Rare Earths, Pergamon, London (1966).

XXII. M.J. Plodinec, Borosilicate glass for nuclear waste immobilisation, Glass Technol. 41(2000), 186-192.

XXIII. M. Kemere, J. Sperga, U. Rogulis, G. Krieke, J. Grube, Structural and optical studies on Sm3+ ions doped bismuth fluoroborate glasses for visible laser applications, J. Lumin. 181 (2017) 25–30.

XXIV. M. Sundara Rao, V. Sudarsan, M.G. Brik, Y. Gandhi, K. Bhargavi, M. Piasecki, I.V. Kityk, N. Veeraiah, De-quenching influence of aluminum ions on Y/B ratio of Dy3+ ions in lead silicate glass matrix, Journal of Alloys and Compounds 575 (2013) 375-381.

XXV. M.V. Vijaya Kumar, B.C. Jamalaiah, K. Rama Gopal, R.R. Reddy., “Optical absorption and fluorescence studies of Dy3+-doped lead telluroborate glasses”, J. Lumin. 132 (2012) 86-90.

XXVI. Nisha Deopa, A.S. Rao, Photoluminescence and energy transfer studies of Dy3+ ions doped lithium lead alumino borate glasses for w-LED and laser applications, J. of Luminescence 192 (2017) 832–841.

XXVII. N. Kiran, A. Suresh Kumar., “White light emission from Dy3+ doped sodium lead borophosphate glasses under UV light excitation”, J. Mol. Struct. 1054 (2013) 6-11.

XXVIII. P. Suthanthirakumar, K. Marimuthu, Investigations on spectroscopic properties of Dy3+ doped zinc telluro-fluoroborate glasses for laser and white LED application,J. Mol. Struct. 1125 (2011) 443-452.

XXIX. R.C. Lucacel, I. Ardelean, FT-IR and Raman study of silver lead borate-based glasses, J. Non-Cryst. Solids. 353 (2007) 2020-2024.

XXX. S. Abed, H. Boughrraf, K. Bouchouit, Z. Sofiani, B. Derkowska, M.S. Aida, B. Sahraoui, Influence of Bi doping on the electrical and optical properties of ZnO thin films, Superlattice Microstruct. 85 (2015) 370-378.

XXXI. S.D. Jackson, Continuous wave 2.9µm dysprosium-doped fluoride fiber laser, Appl. Phys. Lett. 83 (2003) 1316-1318.

XXXII. S. Gai, C. Li, P. Yang, J. Lin, Recent progress in rare earth micro/nanocrystals: soft chemical synthesis, luminescent properties, and biomedical applications, Chem. Rev. 114 (2014) 2343-2389.

XXXIII. Sk. Mahamuda, K. Swapna, P. Packiyaraj, A. Srinivasa Rao, G. Vijaya Prakash, Lasing potentialities and white light generation capabilities of Dy3+ doped oxyfluoro borate glasses, J.Lumin. 153 (2014) 382−392.

XXXIV. Sudhakar Reddy: Judd–Ofelt theory: optical absorption and NIR emission spectral studies of Nd3+: CdO–Bi2O3– B2O glasses for laser applications, J Mater Sci. 47 (2012) 772–778.

XXXV. Swapna K, Mahamuda S, Rao AS, Jayasimhadri M, Moorthy LR. Visible fluorescence Characteristics of Dy3+ doped zinc alumino bismuth borate glasses for optoelectronic devices, Ceramic Int. 39 (2013) 8459–65.

XXXVI. T. Srihari, C.K. Jayasankar, Fluorescence properties and white light generation from Dy3+-doped niobium phosphate glasses, Optical Materials 69 (2017) 87-95.

XXXVII. Valluri Ravi Kumar, G. Giridhar, N. Veeraiah, Influence of modifier oxide on emission features of Dy3+ ion in Pb3O4 ‒ZnO‒P2O5 glasses, Optical Materials, 60 (2016) 594-600.

XXXVIII. W. Bi, N. Louvain, N. Mercier, J. Luc, I. Rau, B. Sahraoui, A switchable NLO organic- inorganic compound based on conformationally chiral disulfide molecules and Bi(III)I5 iodobismuthate networks, Adv. Mater. 20 (2008) 1013-1017.

XXXIX. W.T. Carnall, P.R. Fields, K.Rajnak, Electronic Energy Levels in the Trivalent Lanthanide AquoIons. I. Pr3+, Nd3+, Pm3+, Sm3+, Dy3+, Ho3+, Er3+, and Tm3+, J. Chem. Phys. 49 (1968) 4424–4442.

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Abstract:

We introduce a new class of soft generalized star -closed sets(brieflysoft-closed set), soft - open set in soft topological spaces(from now on STS). We have studied the relationship between this type of closed sets and other existing closed sets in STS and some of their basic properties.

Keywords:

Soft closed,Soft generalized closed,Soft g^* β-closed set,Soft g^* β-open set,Soft topological spaces,

Refference:

Arockiarani.I and ArockiaLancy.A,Generalized soft gβ closed sets and soft gsβ closed sets in soft topological spaces, International Journal of
Mathematical Archive-4(2),2013,17-23.

Hussain.S and Ahmad.B, Some Properties of Soft topological spaces,
Comput. Math. Appl., Vol. 62(2011), 4058-4067.

Kannan.K,Soft Generalized closed sets in soft topological spaces, Journal
of Theoretical and Applied information Technology,Vol.37, No.1(2012), 17-21.

Levine.N, Generalized closed sets in Topology, Rend. Circ. Mat. Palermo,
Vol. 19, No.2(1970), 89-96.

Molodstov.D,”soft set theory-first results”, Computers and Mathematics
with applications (1999), 19-31.

Muhammad Shabir and Munazza Naz,”on soft topological spaces”,
Computers and Mathematics with applications, (2011),Vol.61,issue 7,
1786-1799.

Punitha Tharani. A and Sujitha. H, The concept of g^* β-closed sets in topologicalspaces, International Journal of Mathematical Archive,(2020) Vol.11(4), 14-23.

Shabir.M and Naz.M, on soft topological spaces, Computers and Mathematics with Applications, 61(2011) 1786-1799.

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Abstract:

Now a day, fire accidents in houses, apartments and communities, threatening to the victims and property. As it is a very dangerous job to involve any person like fire fighters during fire accidents, that potentially cause loss of property and human lives due to lack of technology innovation.Hence the firefighting robots are used to rescue the operation instead of humans. In our project, Firefighting robot is used to alert whenever fire accidents are detected and moves in the direction of flame or smoke to extinguish it. Hence the firefighting robot operation is to rescue victims and stop fire in a house within a little span of time.Thus, it reduces the risk of injury to the victims and also property damage.This device includes various sensors like Proximity Infrared Sensor (PIR), flame sensor, ultrasonic sensor, MQ2 (LPG) sensor, and actuators like Motorsand buzzer.

Keywords:

Firefighting robot, Proximity Infrared Sensor,flame sensor,ultrasonic sensor,MQ2 (LPG) sensor,Internet of Things,

Refference:

I. Anusha, M. & Jha, S. 2018, “Embedded secured authentication and speed limiting in various zones with alert system”, International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 2 Special Issue 2, pp. 463-467.

II. Arabelli, R.R.&Rajababu, D. 2019, “Transformer optimal protection using internet of things”, International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 11, pp. 2169-2172.

III. Deepak, N., Rajendra Prasad, C. & Sanjay Kumar, S. 2018, “Patient health monitoring using IOT”, International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 2, pp. 454-457.

IV. https://thingspeak.com/pages/commercial_learn_more

V. Mohd Hasimi Mohd Sidek, WHW Zuha, S Suhaidi, MM Hamiruce,” Fire Fighting Robot,” Asia Pacific Symposium on Applied Electromagnetics and Mechanics (APSAEM2010), 2010.

VI. P, Shanmuga Sundaram, Raj Pradeesh T, et al. “A Case Study on Investigation of Fire Accident Analysis in Cotton Mills.” 14th International Conference on Humanizing Work and Work Environment HWWE-2016 on December 8-11, 2016, NIT, Jalandhar.

VII. Revathi, R. & Renuka, G. 2019, “Child safety seat cooling system”, International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 6 Special Issue 4, pp. 810-814.

VIII. RutujaJadkar, et al. “A Survey on Fire Fighting Robot Controlled Using Android Application.” International Journal of Innovative Research in Science, Engineering and Technology, vol. 4, no. 11, Nov. 2015, pp. 10701–04.

IX. Sonal, Makhare, et al. “Fire Fighting Robot.” International Research Journal of Engineering and Technology, vol. 4, no. 6, June 2017, pp. 136–38.

X. William Dubel, Hector Gongora, Kevin Bechtold and Daisy Diaz, “An Autonomous Firefighting Robot”.

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Abstract:

The people leaves in rural areas will need security in many aspects. All security applications will be operated with the most advanced technology services in embedded and GSM.  This system will be useful to home and rural security for an area of village. In this paper we had smart security surveillance that can send information to authorized person about metal detected if any at entrance of the village. This smart security was done with ARM7 LPC2148 processor, PIR Sensor, metal detector for allowing them into the area by authorized and unauthorized with buzzer.In this paper the PIR sensor will detect the Person and it will check for any metal with the person who would like to enter into the secured zone. The system will send the information about the status of metal and allow them if there is no metal by unauthorized. If metal detected with the person then the system indicates with the buzzer primarily and then it will send the information to authorized person that the person will have some unsecured objects please check once and will not allow into the secured zone.

Keywords:

ARM7LPC2148,Security in rural areas,surveillance,metal detector,

Refference:

I. AravinthanVisvakumar, NanyakkaraThrihantha. Intellligent signal classification in VLF metal detectors to distinguish landmines from harmless metal debris. Proceedings of the annual sessions of the IEE Sri Lunka section. 2004.

II. A.Mounika and K.Rajkumar, “Underwater Positioning Navigation Based On Metal Detector” International Journal of Advanced Research Trends in Engineering and Technology(IJARTET) Vol.4, Special Issue 2, January 2017.

III. B. Liu and W. Zhou, “The research of metal detectors using in food industry,” in Proceedings of the International Conference on Electronics and Optoelectronics (ICEOE ’11), vol. 4, pp. V4-43–V4-45, Dalian, China, July 2011.

IV. B.Swetha and G.Renuka, “Design of IoT Based Intelligent Controlling Of Appliances And Parameter Monitoring System for Environment” International Journal of Advanced Research Trends in Engineering and Technology (IJARTET),Vol. 4, Special Issue 2, January 2017, ISSN 2394-3777 (Print) ISSN 2394-3785 (Online),pg no-234-239.

V. D.Nikitha And P.Anuradha, “Driver Assistance and Safety System for Car”, International Journal of Scientific Engineering and Technology Research held on September, 2016 pg.no 5518-5524, ISSN 2319-8885, Vol 5 Issue 27.

VI. Face Detection and Face Recognition Using Raspberry Pi Shrutika V. Deshmukh1 , Prof Dr. U. A. Kshirsagar International Journal of Advanced Research in Computer and Communication Engineering

VII. Kumar, J. T., and Kumar, V. S. “Novel Distance-Based Subcarrier Number Estimation Method for OFDM System,” In International conference on Modelling, Simulation and Intelligent Computing, vol. 659, pp. 328-335, 2020.

VIII. Kumar, J. Tarun, and V. S. Kumar. “A Novel Optimization Algorithm for Spectrum Sensing Parameters in Cognitive Radio System,” International conference on Modelling, Simulation and Intelligent Computing. vol. 659, pp. 336-344, 2020.

IX. Kumar, V. Sandeep. “Joint Iterative Filtering and Companding Parameter Optimization for PAPR Reduction of OFDM/OQAM Signal,” AEU-International Journal of Electronics and Communications (2020): 153365.

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Abstract:

Localization algorithm plays the major rule for different applications such as tracking, positioning, and monitoring. The general framework presented by localization approaches may not work well in practical environments, due to many reasons related with dealing with 2 Dimensional space only or having high computational costs. As a result, Hybrid Localization Algorithm (HLA) was proposed and presented in this paper based on the use of both Received Signal Strength (RSS) and Angle-of-Arrival (AoA). The algorithm has been tested in a 3 Dimensional indoor scenario, with considering the effects of different building materials. Obtained result indicate an effectiveness in localizing the received points by using 2 transmitters for more accuracy in positioning coordination with average ranging error of less than 0.23m for both Line of Sight (LoS) and Non Line of Sight (NLoS) cases.

Keywords:

RSS,Localization algorithm, indoor,,hybrid,

Refference:

I. C. Feng, et al. “Received-signal-strength-based indoor positioning using compressive sensing.” IEEE Transactions on mobile computing, Vol. 11, no.12, pp: 1983-1993, 2011.‏

II. C. Wong, R. Klukas, and G. M. Geoffrey “Using WLAN infrastructure for angle-of-arrival indoor user location.” 2008 IEEE 68th Vehicular Technology Conference. IEEE, pp: 1-5, 2018. ‏

III. D. Dardari, P. Closas, & P. M. Djurić, “Indoor tracking: Theory, methods, and technologies”. IEEE Transactions on Vehicular Technology, Vol.64, no.4, pp: 1263-1278, 2015.

IV. F. Zafari, G. Athanasios, and K. L. Kin. “A survey of indoor localization systems and technologies.” IEEE Communications Surveys & Tutorials, Vol. 21, no.3, pp: 2568-2599, 2019‏.

V. G. Wang, H. Chen, Y. Li, & M. Jin. “On received-signal-strength based localization with unknown transmit power and path loss exponent”. IEEE Wireless Communications Letters, Vol.1, no.5, pp: 536-539, 2012.

VI. H. Nurminen, M. Dashti, & R. Piché, “A survey on wireless transmitter localization using signal strength measurements”, Wireless Communications and Mobile Computing, 2017. ‏

VII. I. Guvenc, & C. C Chong, “A survey on TOA based wireless localization and NLOS mitigation techniques”, IEEE Communications Surveys & Tutorials, Vol.11, no.3, pp: 107-124, 2009.

VIII. I. Guvenc, and C. Chia-Chin “A survey on TOA based wireless localization and NLOS mitigation techniques.” IEEE Communications Surveys & Tutorials, Vol.11, no.3 pp: 107-124, 2009.‏

IX. International Telecommunication Union, “Effects of building materials and structures on radio wave propagation above about 100 MHz”, Recommendation ITU-R P.2040-1, pp. 22–23, July 2015.

X. J. H. Huh, and Seo. K. “An indoor location-based control system using bluetooth beacons for IoT systems.” Sensors, Vol. 17, no.12, pp: 2917, 2017.‏

XI. J.Yim, G. Subramaniam, and H. K. Byeong. “Location-based mobile marketing innovations.”, Mobile Information Systems, 2017.‏

XII. M. M. Abdulwahid, O. A. S. Al-Ani, M. F. Mosleh and R. A. Abd-Alhmeed. “Optimal access point location algorithm based real measurement for indoor communication”. In Proceedings of the International Conference on Information and Communication Technology, pp: 49-55, 2019.‏

XIII. M. S. AL-Hakeem, I. M. Burhan, M. M. Abdulwahid, “Hybrid Localization Algorithm for Accurate Indoor Estimation Based IoT Services”, IJAST, vol. 29, no. 05, pp. 9921 – 9929, 2020.

XIV. M. M. Abdulwahid, et al. “Investigation and optimization method for wireless AP deployment based indoor network.” MS&E, Vol.745, no.1, pp: 012031, 2020.‏

XV. M. M. Abdulwahid, O. A. S. Al-Ani, M. F. Mosleh and R. A. Abd-Alhmeed..”Investigation of millimeter-wave indoor propagation at different frequencies”. In 2019 4th Scientific International Conference Najaf (SICN),pp. 25-30, 2019.

XVI. O. A Shareef, M. M. Abdulwahid, M. F. Mosleh, & R. A. Abd-Alhameed. “The optimum location for access point deployment based on RSS for indoor communication”, International Conference on Modelling and Simulation (UKsim2019), Vol.20, p 2.1, 2019.
XVII. N. B. Mohamadwasel, & Bayat, O. Improve DC Motor System using Fuzzy Logic Control by Particle Swarm Optimization in Use Scale Factors, 2019.

XVIII. REMCOM Inc, “The Wireless InSite user’s manual.” version 2.6.3, romcom inc., 315 s. allen st., suite 416 state college, pa16801,2009.

XIX. R. Peng, and L. S. Mihail. “Angle of arrival localization for wireless sensor networks.” 2006 3rd annual IEEE communications society on sensor and ad hoc communications and networks. Vol. 1, pp. 374-382, 2006.‏

XX. S. Aditya, F. M. Andreas, and M. B. Hatim. “A survey on the impact of multipath on wideband time-of-arrival based localization.” Proceedings of the IEEE, Vol. 106, no.7 pp: 1183-1203, 2018.‏

XXI. S. Sen, R. C. Romit, and N. Srihari. “SpinLoc: Spin once to know your location.” Proceedings of the Twelfth Workshop on Mobile Computing Systems & Applications, pp: 1-6, 2012.‏

XXII. V. Carrizales, Y. Samantha, N. Marco Aurelio, and R. L. Javier. “A platform for e-health control and location services for wandering patients.” Mobile Information Systems, 2018.‏

XXIII. W. Dai, S. Yuan, and Z. W. Moe. “Distributed power allocation for cooperative wireless network localization.” IEEE Journal on Selected Areas in Communications Vol.33, no.1, pp: 28-40, 2014.

XXIV. Y. R. Mohammed, N. Basil, O. Bayat, and A. Hamid, “A New Novel Optimization Techniques Implemented on the AVR Control System using MATLAB-SIMULINK A New Novel Optimization Techniques Implemented on the AVR Control System using MATLAB-SIMULINK,” no. May, 2020.

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Abstract:

The area of fractal modeling is a present-day applicative growth. Fractals contain unlimited amount of information in contradiction to conventional geometric shapes. A well-established method of creating fractals is by means of Iterated Function Systems, with extra - ordinary work done on 2D IFS, where the rendering of the same acquired in an easy and effective manner. Though the 3D IFS transpires/takes shape as a natural world derived add-on, more research has to be carried on it in real-world fractal science and engineering. Here 3D IFS is used to get enchanting fractals by applying algorithms.  The methods used here have a wide- spread use in fractal science very, an example being, recursive fractals elucidated through algebraic transformations. Also presented is a suitable algorithm for processing of arrays. Finally, the outputs obtained are passed through shading and exposure to get a viewing picture. The processes used above result in producing modified versions of objects in a variety of shape andtexture.

Keywords:

Fractals,IFS,Self-similarity,Time Complexity,Time Image,

Refference:

I D.M. Monro and F. Budbridge, “Rendering Algorithms for deterministic fractals,” IEEE Computer Graphics and its Applications, Pages 32-41, 1995.
II G. Eason, B. Noble, and I. N. Sneddon, “On certain integrals of Lipschitz-Hankel type involving products of Bessel functions,” Phil. Trans. Roy. Soc. London, vol. A247, pp. 529–551, April1955.
III S. Jacobs and C. P. Bean, “Fine particles, thin films and exchange anisotropy,” in Magnetism, vol. III, G. T. Rado and H. Suhl, Eds. New York: Academic, 1963, pp. 271–350.
IV J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.
V M. Young, the Technical Writer’s Handbook.Mill Valley, CA: University Science, 1989.
VI M. Young, the Technical Writer’s Handbook. Mill Valley, CA: University Science, 1989.
VII Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, “Electron spectroscopy studies on magneto-optical media and plastic substrate interface,” IEEE Transl. J. Magn. Japan, vol. 2, pp. 740–741, August 1987

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Abstract:

In this paper, we accomplished the concept of convolution of Laguerre transform for the study of continuous Laguerre wavelet transform and discuss some of its basic properties. Finally our main goal is to find out the Plancherel and inversion formula for the Continuous Laguerre WaveletTransform.

Keywords:

Laguerre transforms,Laguerre convolution,Wavelet transform,2010 Mathematics Subject Classification,42C40,65R10,44A35,

Refference:

I A. Erdèlyi (ed.), Tables of Integral Transforms, Vol. II, Mc Graw-Hill Book Co., New York, 1954.

II C.K. Chui, An introduction to Wavelets, Academic Press, NewYork, 1992.

III Market, Mean Cesaro summability of Laguerre expansions and norm estimates with sift parameter, Anal. Math. 8 (1982), pp. 19–37.

IV E. Gorlich and C. Market, A convolution structure for Laguerre series, Indag. Math. 44 (1982), pp. 61–171.

V F.M. Cholewinski and D.T. Haimo, The dual Poisson–Laguerre transform, Trans. Amer. Math. Soc. 144 (1969), pp. 271–300.

VI G. Kaiser, A Friendly Guide to Wavelets, Birkhauser Verlag, Boston, 1994.

VII R.S. Pathak and M.M. Dixit, Continuous and discrete Bessel wavelet transform, J. Comput. Appl. Math. 160 (2003), pp. 241–250.

VIII S.K. Upadhyay, A. Tripathi, Continuous Watson Wavelet Transform, Integral Transforms and Special Functions, 23:9, 639-647.

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Abstract:

In the paper, identification and classification of high impedance faults (HIF) are analyzed with the Extended Kalman filter and Deep Belief Neural Network (DBN). Here, the proposed method is utilized for classifying the HIF in power system. To extract the features of the signals, EKF is introduced and the DBN is used for classify the signals. Initially, the distribution system, the No Fault (NF) signals are analyzed. After that, in the distribution system linear load and non-linear loads are applied to the system. In this proposed method, radial distribution system and meshed distribution systems are analyzed under the HIF conditions. Here, harmonic coefficients of 3rd, 5th, 7th, 9th and 13th are analyzed with the help of proposed method. The feature signals of current and voltage under the harmonic components are taken as the input of DBN. The feature signals are classified with the help of DBN classifier. The proposed method is implemented in MATLAB/Simulink working platform and the detection performance evaluated. The evaluated results are compared with Artificial Neural Network (ANN) and Neuro Fuzzy Controller (NFC) methods. In addition, the proposed method is tested with the statistical measures like, Accuracy, Sensitivity, and Specificity etc

Keywords:

DBN,EKF,linear load,non-linear load,ANN,NFS,harmonic coefficients,HIF,

Refference:

I Bokka Krishna Chaitanya, Anamika Yadav and Mohammad Pazoki, “An Intelligent Detection of High-Impedance Faults for Distribution Lines Integrated with Distributed Generators”, IEEE Systems Journal, Vol. 14, No. 1, pp. 870 – 879, March 2020

II Chengye Lu, Sheng Wu, Chunxiao Jiang and Jinfen, “Weak Harmonic Signal Detection Methodin Chaotic Interference based on Extended Kalman Filter”, Digital Communications and Networks, Vol.5, No.1, pp.51-55, February 2019

III Érica Mangueira Lima, Núbia Silva Dantas Brito and Benemar Alencar de Souza, “High impedance fault detection based on Stockwell transform and third harmonic current phase angle”, Electric Power Systems Research, Vol.175, pp.1-14, October 2019,

IV Junbo Zhao, Marcos Netto and Lamine Mili, “A Robust Iterated Extended Kalman Filter for Power System Dynamic State Estimation”, IEEE Transactions on Power Systems, Vol. 32, No. 4, pp. 3205 – 3216, July 2017

V J.U.N. Nunes, A.S. Bretas, N.G. Bretas, A.R. Herrera-Orozco and L.U. Iurinice, “Distribution systems high impedance fault location: A spectral domain model considering parametric error processing”, Elsevier, International Journal of Electrical Power & Energy Systems, Vol. 109, pp. 227-241, July 2019

VI Kumari Sarwagya, Sourav De and Paresh Kumar Nayak, “High-impedance fault detection in electrical power distribution systems using moving sum approach”, IET Science, Measurement & Technology, Vol. 12, No. 1, pp. 1-8, 2018

VII Meera R.Karamta and J.G.Jamnani, “Implementation of Extended Kalman Filter Based Dynamic State Estimation on SMIB System Incorporating UPFC Dynamics”, Energy Procedia, Vol.100, pp. 315-324, November 2016

VIII MuhammadSarwar, FaisalMehmood, Muhammad Abid, Abdul QayyumKhan, Sufi TabassumGul and Adil SarwarKhan, “High impedance fault detection and isolation in power distribution networks using support vector machines”, Journal of King Saud University – Engineering Sciences, July 2019

IX Sinha, Pampa, and Manoj Kumar Maharana, “Artificial Intelligence in Classifying High Impedance Faults in Electrical Power Distribution System”, In proceedings of International Conference on Recent Trends in Computing, Communication and Networking Technologies (ICRTCCNT’19), Kings Engineering College, pp.1-5, 2019

X VicenteTorres-Garcia, DanielGuillen, JimenaOlveres, BorisEscalante-Ramirez and Juan R.Rodriguez-Rodriguez, “Modelling of high impedance faults in distribution systems and validation based on multiresolution techniques”, Computers & Electrical Engineering, Vol. 83, pp.1-15, May 2020

XI Yuming Hua, Junhai Guo and Hua Zhao, “Deep Belief Networks and deep learning”, In Proceedings of International Conference on Intelligent Computing and Internet of Things, pp.1-4, 2015

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Abstract:

Softwarecost and schedule estimation is usually based on the estimated size of the software. Advanced estimation techniques also make use of the diverse factors viz, nature of the project, staff skills available, time constraints, performance constraints, technology required and so on. Usually, estimation is based on an estimation model prepared with the help of experienced project managers. Estimation of software cost is predominantly a crucial activity as it incurs huge economic and strategic investment. However accurate estimation still remains a challenge as the algorithmic models used for Software Project planning and Estimation doesn’t address the true dynamic nature of Software Development. This paper presents an efficient approach using the contemporary Constructive Cost Model (COCOMO) augmented with the desirable feature of fuzzy logic to address the uncertainty and flexibility associated with the cost drivers (Effort Multiplier Factor). The approach has been validated and interpreted by project experts and shows convincing results as compared to simple algorithmic models. The proposed model cost is close to the actual cost to a tune of 98%.

Keywords:

COCOMO,fuzzy logic,software development,cost estimation,

Refference:

I Ali Bou Nassif, Mohammad Azzeh, Ali Idri and Alain Abran, Software Development Effort Estimation Using Regression Fuzzy Models, Computational Intelligence and Neuroscience, Hindawi publications, Feb 2019, Volume 2019 |Article ID 8367214 | 17 pages | https://doi.org/10.1155/2019/8367214

II Attarzadeh, I., Siew Hock Ow, “A novel soft computing model to increase the accuracy of software development cost estimation”, Published in 2nd International Conference on Computer and Automation Engineering (ICCAE), 2010, Volume: 3, Pages: 603 – 607, DOI: 10.1109/ICCAE.2010.5451810

III Ashish Sharma Manu Vardhan, A Versatile Approach for the Estimation of Software Development Effort Based on SRS Document , International Journal of Software Engineering and Knowledge Engineering (IJSEKE), 2014pp 1-42

IV Attarzadeh, I., Siew Hock Ow, “Improving estimation accuracy of the COCOMO II using an adaptive fuzzy logic model”, IEEE International Conference on Fuzzy Systems (FUZZ), 2011, Pages: 2458 – 2464, DOI: 10.1109/FUZZY.2011.6007471

V Attarzadeh, I.; Siew Hock Ow, Proposing a New High Performance Model for Software Cost Estimation””, ICCEE ’09. Second International Conference on Computer and Electrical Engineering, 2009, Volume: 2 Pages: 112 – 116, DOI: 10.1109/ICCEE.2009.97

VI [BOE81] Boehm, B., Software Engineering Economics, Prentice-Hall, 1981.

VII D. Manikavelan, R. Ponnusamy, Software quality analysis based on cost and error using fuzzy combined COCOMO model, Journal of Ambient Intelligence and Humanized Computing (2020), Springerlink, March 2020

VIII Huang, X.; Ho, D.; Ren, J.; Capretz, L.F., “A neuro-fuzzy tool for software estimation”, 20th IEEE International Conference on Software Maintenance, 2004. Proceedings. Page: 520, DOI: 10.1109/ICSM.2004.1357862

IX Iman Attarzadeh ; Siew Hock Ow, Proposing a new software cost estimation model based on artificial neural networks, 2nd International Conference on Computer Engineering and Technology, IEEE April 2020, DOI: 10.1109/ICCET.2010.5485840

X Kushwaha, N., Suryakant, “Software cost estimation using the improved fuzzy logic framework”, Conference on IT in Business, Industry and Government (CSIBIG), 2014, Pages: 1 – 5, DOI: 10.1109/CSIBIG.2014.7056959

XI Mirseidova, S.; Atymtayeva, L., “Definition of software metrics for software project development by using fuzzy sets and logic “,13th International Symposium on Advanced Intelligent Systems (ISIS), Joint 6th International Conference on Soft Computing and Intelligent Systems (SCIS) and 2012, Pages: 272 – 276, DOI: 10.1109/SCIS-ISIS.2012.6505336

XII [PUT92] Putnam, L. and W. Myers, Measures for Excellence, Yourdon Press, 1992.

XIII Rama, S.P., “Analytical structure of a fuzzy logic controller for software development effort estimation”, International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO), Year: 2015, Pages: 1 – 4, DOI: 10.1109/EESCO.2015.725

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