Archive

Abstract:

Electrocardiogram (ECG) plays vital role in diagnosing large number of diseases and disorders related to heart. ECG devices are able to perform multiple parameters by analyzing the patterns of bio-signals. The state-of-art ECG machine uses electrodes attached to human body using gel. The whole process agitates the patient resulting in disturbed ECG report by producing noise due to movement, imbalanced electrodes, and heavy objects. The proposed ECG system is portable finger-based system that generates ECG report in minimum time duration with providing ease to users. The system replaces disturbing electrodes by a single bio signal identification sensor. It takes signals from one finger of patient through sensor in 7 seconds. The sensor is followed up by combination of various capacitors and buffers in order to enhance signals. The signals are then transferred to software using USB port for several medical required filtrations and overall noise removal. The result of the process is an ECG signal representing heart condition of patient. The results can be stored for future medical investigations like improvement or decline in health of patient. The proposed prototype is deployed in several hospitals for testing. The system evaluated through comparison method with current system and results are satisfactory.

Keywords:

ECG,Bio-Signals,Filters,IR Sensors,Quality of Service,

Refference:

I Al-Ghamdi, Bandar. “Subcutaneous implantable cardioverter defibrillators: an overview of implantation techniques and clinical outcomes.” Current cardiology reviews 15, no. 1 (2019): 38-48.

II Betancourt, Nancy, Carlos Almeida, and Marco Flores-Calero. “T Wave Alternans Analysis in ECG Signal: A Survey of the Principal Approaches.” In International Conference on Information Technology & Systems, pp. 417-426.Springer, Cham, 2019.

III Castro, I. D., Carolina Varon, Jonathan Moeyersons, Amalia Villa Gomez, John Morales, Margot Deviaene, Tom Torfs, Sabine Van Huffel, Robert Puers, and Chris Van Hoof. “Data Quality Assessment of Capacitively-coupled ECG signals.” In Proceedings of the 2019 Computing in Cardiology Conference (CinC), Singapore, pp. 8-11. 2019.

IV Chien, Jun-Chau. “A 1.8-GHz Near-Field Dielectric Plethysmography Heart-Rate Sensor With Time-Based Edge Sampling.” IEEE Journal of Solid-State Circuits (2019).

V Dos Reis, Jesús E., Paul Soullié, Julien Oster, Ernesto PalmeroSoler, Gregory Petitmangin, Jacques Felblinger, and Freddy Odille. “Reconstruction of the 12‐lead ECG using a novel MR‐compatible ECG sensor network.” Magnetic resonance in medicine (2019).

VI El_Rahman, Sahar A. “Biometric human recognition system based on ECG.” Multimedia Tools and Applications (2019): 1-18.

VII Gao, Yang, Varun V. Soman, Jack P. Lombardi, Pravakar P. Rajbhandari, Tara P. Dhakal, Dale Wilson, Mark Poliks, KanadGhose, James N. Turner, and ZhanpengJin. “Heart Monitor Using Flexible Capacitive ECG Electrodes.” IEEE Transactions on Instrumentation and Measurement (2019).

VIII Kamp, Nicholas J., and Sana M. Al-Khatib. “The subcutaneous implantable cardioverter-defibrillator in review.” American heart journal (2019).

IX Lee, Jae-Ho, and Dong-WookSeo. “Development of ECG Monitoring System and Implantable Device with Wireless Charging.” Micromachines 10, no. 1 (2019): 38.

X Lee, Jae-Neung, Sung Bum Pan, and Keun-Chang Kwak. “Individual identification Based on Cascaded PCANet from ECG Signal.” In 2019 International Conference on Electronics, Information, and Communication (ICEIC), pp. 1-4. IEEE, 2019.

XI Majumder, Sumit, Leon Chen, OgnianMarinov, Chih-Hung Chen, Tapas Mondal, and M. Jamal Deen. “Noncontact wearable wireless ECG systems for long-term monitoring.” IEEE reviews in biomedical engineering 11 (2018): 306-321.

XII Marathe, Sachi, DilkasZeeshan, Tanya Thomas, and S. Vidhya. “A Wireless Patient Monitoring System using Integrated ECG module, Pulse Oximeter, Blood Pressure and Temperature Sensor.” In 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN), pp. 1-4. IEEE, 2019.

XIII Rahman, Alvee, TahsinurRahman, NawabHaiderGhani, SazzadHossain, and JiaUddin. “IoT Based Patient Monitoring System Using ECG Sensor.” In 2019 International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), pp. 378-382. IEEE, 2019.

XIV Rachim, Vega Pradana, and Wan-Young Chung. “Wearable noncontact armband for mobile ECG monitoring system.” IEEE transactions on biomedical circuits and systems 10, no. 6 (2016): 1112-1118.

XV Roopa, C. K., and B. S. Harish. “A survey on various machine learning approaches for ECG analysis.” International Journal of Computer Applications 163, no. 9 (2017): 25-33.

XVI Steinberg, Christian, François Philippon, Marina Sanchez, Pascal Fortier-Poisson, Gilles O’Hara, Franck Molin, Jean-François Sarrazin et al. “A Novel Wearable Device for Continuous Ambulatory ECG Recording: Proof of Concept and Assessment of Signal Quality.” Biosensors 9, no. 1 (2019): 17.

XVII Saadatnejad, Saeed, MohammadhoseinOveisi, and MatinHashemi. “LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices.” IEEE journal of biomedical and health informatics (2019).

XVIII Wang, Ning, Jun Zhou, Guanghai Dai, Jiahui Huang, and YuxiangXie. “Energy-efficient intelligent ECG monitoring for wearable devices.” IEEE transactions on biomedical circuits and systems 13, no. 5 (2019): 1112-1121.

XIX Zhao, Peng, DekuiQuan, Wei Yu, Xinyu Yang, and Xinwen Fu. “Towards deep learning-based detection scheme with raw ECG signal for wearable telehealth systems.” In 2019 28th International Conference on Computer Communication and Networks (ICCCN), pp. 1-9.IEEE, 2019.

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Abstract:

In this paper the wind energy operations in addition to its all vital issues during transients are presented. A Type III or class C type wind turbine system with induction generator is implemented which is fed from both the side of rotor and grid. As the T-III-WT-DFIG wind turbine system is effective over normal speed variation among all sustainable power sources; with variable-pitch control for variable speed it is main criteria for the motive of the research. The major issue in wind energy system design is variable speed in the power generation sectors; so this research can play an important role to define the transient analysis and fault clearances. The system is integrated with 1.5MW grid system for the analysis. Using the MATLAB Simulink, the type-III WT DFIG with variable speed wind turbine integrated with the grid system is simulated and the control action is performed by conventional PI controller in the generator and turbine coupling. In this research paper three cases such as voltage dip or sag, 3 phase fault analysis and wind speed variation are executed and the stability of the power system are discussed.

Keywords:

Type- III WT,DFIG,WECS,SVOC,wind turbine,Auto Regressive Moving Average,decoupled control,

Refference:

I. AbdulhamedHwas, Reza Katebi, Wind Turbine Control Using PI Pitch Angle Controller, IFAC Proceedings Volumes, Volume 45, Issue 3, 2012, Pages 241-246, ISSN 1474-6670, ISBN 9783902823182, https://doi.org/10.3182/20120328-3-IT-3014.00041.
II. B. P. Ganthia, S. Mohanty, P. K. Rana and P. K. Sahu, “Compensation of voltage sag using DVR with PI controller,” 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, 2016, pp. 2138-2142, doi: 10.1109/ICEEOT.2016.7755068.
III. B. P. Ganthia, V. Agarwal, K. Rout and M. K. Pardhe, “Optimal control study in DFIG based wind energy conversion system using PI & GA,” International Conference on Power and Embedded Drive Control (ICPEDC), Chennai, 2017, pp. 343-347.
IV. Bekhada, HamaneDoumbia, Mamadou, BOUHAMIDA, Mohamed Draou, Azeddine CHAOUI, HichamBenghanem, Mustapha, “Comparative Study of PI, RST, Sliding Mode and Fuzzy Supervisory Controllers for DFIG based Wind Energy Conversion System”, International Journal of Renewable Energy Research (IJRER), Volume – 5, 2015/12/26, Page 1174 – 1185.
V. Djeriri, Youcef&Meroufel, Abdelkader&Massoum, Ahmed &Boudjema, Zinelaabidine. (2014). A comparative study between field oriented control strategy and direct power control strategy for DFIG. Journal of Electrical Engineering. 14. 169-178.
VI. IulianMunteanu, AntonetaIulianaBratcu, Nicolaos-Antonio Cutululis and Emil Ceanga, “Optimal Control of Wind Energy System”, Springer, London, 2008.
VII. Lei, Yazhou, et al. “Modeling of the wind turbine with a doubly fed induction generator for grid integration studies.” Energy Conversion, IEEE Transactions on 21.1 (2006): 257-264.
VIII. Power conversion and control of wind energy systems by Bin Wu, Yongqiang Lang, NavidZargari, Samir Kouro. IEEE publication.
IX. Qiao, Wei. “Dynamic modeling and control of doubly fed induction generators driven by wind turbines.” Power Systems Conference and Exposition, 2009. PSCE’09. IEEE/PES. IEEE, 2009.
X. S. M. Muyeen, Md. Hasan Ali, R. Takahashi, T. Murata, J. Tamura, Y. Tomaki, A. Sakahara and E. Sasano, “Comparative Study on Transient Stability Analysis of Wind Turbine Generator System Using Different Drive Train Models”, IET Renewable Power Generation, Vol. 1, No, 2, pp. 131-141, June 2007.
XI. Siraj, Kiran, HarisSiraj, and MashoodNasir. “Modeling and control of a doubly fed induction generator for grid integrated wind turbine.” Power Electronics and Motion Control Conference and Exposition (PEMC), 2014 16th International. IEEE, 2014.
XII. T.ghennam, E.M. Berkouk, B. Francois, “Modeling and Control of a Doubly Fed Induction Generator (DFIG) Based Wind Conversion System” IEEE 2009.
XIII. Tao Sun, “Power Quality of Grid-Connected Wind Turbines with DFIG and Their Interaction with the Grid”, Ph.D. dissertation, Aalborg University, Denmark, May 2004.
XIV. Yang, Jin. “Fault analysis and protection for wind power generation systems”. Diss. University of Glasgow, 2011.
XV. Zhang, B.; Hu, W.; Hou, P.; Tan, J.; Soltani, M.; Chen, Z. “Review of Reactive Power Dispatch Strategies for Loss Minimization in a DFIG-based Wind Farm” Energies2017, 10, 856.

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Abstract:

Reconnaissance or footprinting is the technique used for gathering information about computer systems and the entities they belong to. To exploit any system, a hacker might use various tools and technologies. This information is very useful to a hacker who is trying to crack a whole system. Subdomain enumeration plays a vital role in reconnaissance. Enumeration of subdomains provide an important insight towards the various underlying architecture and enable to find hidden user interfaces and admin panels. The less infrequent and unknown the domain name, the less visitors will visit the site. This enables a blindspot for the easy finding of low hanging vulnerabilities. Some of the most popular various tools used for recon on domains are Amass, Subfinder, KnockPy, altdns, sublis3r. We have done a comparative study and analysis of various functions of these tools on parameters like uniqueness, accuracy, complexity and conclude which works in certain scenarios along with static code analysis to find weak spots within the code infrastructure of each of the tools.

Keywords:

reconnaissance,web security,application security,

Refference:

I. A. Kothia, B. Swar and F. Jaafar, “Knowledge Extraction and Integration for Information Gathering in Penetration Testing,” 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C), Sofia, Bulgaria, 2019, pp. 330-335.
II. Adiwal, Sanjay &Rajendran, Balaji&Shetty, Pushparaj.(2018). Domain Name System (DNS) Security: Attacks Identification and Protection Methods.
III. AlkaAgrawal, MamdouhAlenezi, Rajeev Kumar and Raees Ahmad Khan, Securing Web Applications through a Framework of Source Code Analysis, Journal of Computer Science,Volume 15, Issue 12,Pages 1780-1794
IV. https://github.com/aboul3la/Sublist3r
V. https://github.com/guelfoweb/knock
VI. https://gitlab.com/paperrepo/subdomain-enumeratioon
VII. https://github.com/infosec-au/altdns
VIII. https://github.com/OWASP/Amass
IX. https://github.com/projectdiscovery/subfinder
X. K. Nirmal, B. Janet And R. Kumar, “Web Application Vulnerabilities- The Hacker’s Treasure,” 2018 International Conference On Inventive Research In Computing Applications (Icirca), Coimbatore, India, 2018, Pp. 58-62
XI. P. Harika Reddy SurapaneniGopi Siva SaiTeja,Cyber Security and Ethical Hacking,International Journal for Research in Applied Science & Engineering Technology (IJRASET),Volume 6 Issue VI, June 2018
XII. Richard Roberts and Dave Levin. 2019. When Certificate Transparency Is Too Transparent: Analyzing Information Leakage in HTTPS Domain Names. In Proceedings of the 18th ACM Workshop on Privacy in the Electronic Society (WPES’19).Association for Computing Machinery, New York, NY, USA, 87–92.
XIII. Russell, Rebecca & Kim, Louis & Hamilton, Lei &Lazovich, Tomo&Harer, Jacob &Ozdemir, Onur&Ellingwood, Paul &McConley, Marc. (2018). Automated Vulnerability Detection in Source Code Using Deep Representation Learning. 757-762. 10.1109/ICMLA.2018.00120.
XIV. S. M. Zia Ur Rashid ImtiazKamrulImtiazKamrulAsrafulAlamAsrafulAlam,Understanding the Security Threats of Esoteric Subdomain Takeover and Prevention Scheme, Conference: 2019 International Conference on Electrical,doi: 10.1109/ECACE.2019.8679122
XV. Siavvas M., Gelenbe E., Kehagias D., Tzovaras D. (2018) Static Analysis-Based Approaches for Secure Software Development. In: Gelenbe E. et al. (eds) Security in Computer and Information Sciences. Euro-CYBERSEC 2018.Communications in Computer and Information Science, vol 821. Springer, Cham
XVI. Thomassen, P., Benninger, J., &Margraf, M. (2018).Hijacking DNS Subdomains via Subzone Registration: A Case for Signed Zones. OJWT, 5, 6-13.

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Abstract:

Any sort of security compromise of the system will directly impact individual lifestyle. Therefore security and privacy of this particular technology is foremost vital concern to fix. Within this paper our experts present a thorough research of security issues in IoT and also classify achievable cyber- attacks on each coating of IoT construction. Our company like wise goes over problems to standard security options including cryptographic services, verification mechanisms and also essential management in IoT.

Keywords:

IoT,network security,challenges,

Refference:

I. Ashton, K. “That ‘Web of Qualities’ factor”. Quickly available online: http://www.rfidjournal.com/ (accessed on 22 June 2009).
II. A. J. Menezes, S. A. Vanstone, P. C. Van Oorschot, “Handbook of Applied Cryptography”, CRC Push, Inc., Boca Raton, FL, 1996.
III. A. Monelli and S. B. Sriramoju, “An Overview of the Challenges and Applications towards Web Mining,” 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on, Palladam, India, 2018, pp. 127-131.doi: 10.1109/I-SMAC.2018.8653669
IV. D. Slonim, P. Tamayo, J. Mesirov, T. Golub, and E. Lander. Class prediction and discovery using gene expression data. In Proc. 4th Int. Conf. on Computational Molecular Biology (RECOMB), 2000, pages263–272.
V. D. Deepika, a Krishna Kumar, MonelliAyyavaraiah, ShobanBabuSriramoju, “Phases of Developing Artificial Intelligence and Proposed Conversational Agent Architecture”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-8 Issue-12, October 2019, DOI: 10.35940/ijitee.L3384.1081219
VI. Kiran Kumar S V N Madupu, “Key Methodologies for Designing Big Data Mining Platform Based on CloudComputing”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 1 Issue 2, pp. 190-196, September-October 2016. Available at doi : https://doi.org/10.32628/CSEIT206271
VII. Kiran Kumar S V N Madupu, “Opportunities and Challenges Towards Data Mining with Big Data”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 1 Issue 3, pp. 207-214, July-August 2015. Available at doi : https://doi.org/10.32628/IJSRST207255
VIII. Kiran Kumar S V N Madupu, “A Survey on Cloud Computing Service Models and Big Data Driven Networking”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 4 Issue 10, pp. 451-458, September-October 2018. Available at doi : https://doi.org/10.32628/IJSRST207257
IX. Laurent, (2014) “Lighting in weight collective critical establishment system for the Net of Qualities” Pc Networks, vol. 64, pp. 273– 295.
X. P. Pramod Kumar, S. Naresh Kumar, V. Thirupathi, Ch. Sandeep, “QOS AND SECURITY PROBLEMS IN 4G NETWORKS AND QOS MECHANISMS OFFERED BY 4G”, International Journal of Advanced Science and Technology, Vol. 28, No. 20, (2019), pp. 600-606
XI. Pasha, S.N., Ramesh, D., Kodhandaraman, D. &Salauddin, M.D. 2019, “An research to enhance the old manuscript resolution using deep learning mechanism”, International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 6 Special Issue 4, pp. 1597-1599.

XII. P. Pramod Kumar, C. H. Sandeep, and S. Naresh Kumar.”An overview of the factors affecting handovers and effective highlights of handover techniques for next generation wireless networks.” Indian Journal of Public Health Research & Development, no. 11 (2018): 722-725.
XIII. Pushpa Mannava, “A Study on the Challenges and Types of Big Data”, “International Journal of Innovative Research in Science, Engineering and Technology”, ISSN(Online) : 2319-8753, Vol. 2, Issue 8, August 2013
XIV. Pushpa Mannava, “Data Mining Challenges with Bigdata for Global pulse development”, International Journal of Innovative Research in Computer and Communication Engineering, ISSN(Online): 2320-9801, vol 5, issue 6, june 2017
XV. Pushpa Mannava, “Big Data Analytics in Intra-Data Center Networks and Components Of Data Mining”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 1 Issue 3, pp. 82-89, November-December 2016. Available at doi : https://doi.org/10.32628/CSEIT206272
XVI. Sandeep, C. H., S. Naresh Kumar, and P. Pramod Kumar.”Security challenges and issues of the IoT system.” Indian Journal of Public Health Research & Development, no. 11 (2018): 748-753.
XVII. Sheshikala, M et al, “Natural Language Processing and Machine Learning Classifier used for Detecting the Author of the Sentence ”. International Journal of Recent Technology and Engineering (IJRTE) (2019).
XVIII. S. Naresh Kumar, P. Pramod Kumar, C. H. Sandeep, and S. Shwetha. “Opportunities for applying deep learning networks to tumour classification.” Indian Journal of Public Health Research & Development, no. 11 (2018): 742-747.
XIX. Sripada, Naresh Kumar et al. “Support Vector Machines to Identify Information towards Fixed-Dimensional Vector Space.”International Journal of Innovative Technology and Exploring Engineering (IJITEE),(2019).

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Abstract:

The early medical diagnosis and also outlook of a cancer kind have actually ended up being a requirement in cancer investigation, as it can assist in the succeeding scientific control of people. The usefulness of categorizing cancer clients right into higher or reduced risk groups has led lots of re- hunt staffs, coming from the biomedical as well as the bioinformatics field, to study the application of machine learning (ML) approaches. For that reason, these strategies have been actually taken advantage of as a goal to model the advancement and also treatment of malignant disorders. Additionally, the capability of ML devices to discover key attributes from complex datasets shows their value.

Keywords:

Machine Learning,Deep learning,

Refference:

I A. Monelli and S. B. Sriramoju, “An Overview of the Challenges and Applications towards Web Mining,” 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on, Palladam, India, 2018, pp. 127-131.doi: 10.1109/I-SMAC.2018.8653669
II D. Slonim, P. Tamayo, J. Mesirov, T. Golub, and E. Lander. Class prediction and discovery using gene expression data. In Proc. 4th Int. Conf. on Computational Molecular Biology (RECOMB), 2000, pages263–272.
III D. Deepika, a Krishna Kumar, MonelliAyyavaraiah, ShobanBabuSriramoju, “Phases of Developing Artificial Intelligence and Proposed Conversational Agent Architecture”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-8 Issue-12, October 2019, DOI: 10.35940/ijitee.L3384.1081219
IV Golub, Todd R., et al. “Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.” science 286.5439 (1999):531-537.
V Kiran Kumar S V N Madupu, “Tool to IntegrateOptimized Hardware and Extensive Software into Its Database to Endure Big Data Challenges”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 5, pp. 272-279, September-October 2019. Available at doi : https://doi.org/10.32628/CSEIT206275
VI Kiran Kumar S V N Madupu, “Key Methodologies for Designing Big Data Mining Platform Based on CloudComputing”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 1 Issue 2, pp. 190-196, September-October 2016. Available at doi : https://doi.org/10.32628/CSEIT206271
VII Kiran Kumar S V N Madupu, “Opportunities and Challenges Towards Data Mining with Big Data”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 1 Issue 3, pp. 207-214, July-August 2015. Available at doi : https://doi.org/10.32628/IJSRST207255
VIII Komuravelly Sudheer Kumar et al, “A Narrative Improvement Techniques Used with The Expert Systems.” (2019).
IX Lakhani, Sunil R., and Alan Ashworth. “Microarray and histopathological analysis of tumours: the future and the past?.” Nature Reviews Cancer 1.2 (2001):151-157.
X Nguyen, Danh V., and David M. Rocke. “Classification of acute leukemia based on DNA microarray gene expressions using partial least squares.” Methods of Microarray Data Analysis. Springer US, 2002.109-124.
XI Pasha, S.N., Ramesh, D., Kodhandaraman, D. &Salauddin, M.D. 2019, “An research to enhance the old manuscript resolution using deep learning mechanism”, International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 6 Special Issue 4, pp. 1597-1599.
XII P. Pramod Kumar, C. H. Sandeep, and S. Naresh Kumar.”An overview of the factors affecting handovers and effective highlights of handover techniques for next generation wireless networks.” Indian Journal of Public Health Research & Development, no. 11 (2018): 722-725.
XIII Pushpa Mannava, “Big Data Analytics in Intra-Data Center Networks and Components Of Data Mining”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 1 Issue 3, pp. 82-89, November-December 2016. Available at doi : https://doi.org/10.32628/CSEIT206272

XIV Pushpa Mannava, “An Overview of Cloud Computing and Deployment of Big Data Analytics in the Cloud”, International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Online ISSN : 2394-4099, Print ISSN : 2395-1990, Volume 1 Issue 1, pp. 209-215, 2014. Available at doi : https://doi.org/10.32628/IJSRSET207278
XV Pushpa Mannava, “Role of Big Data Analytics in Cellular Network Design”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 1 Issue 1, pp. 110-116, March-April 2015. Available at doi : https://doi.org/10.32628/IJSRST207254
XVI Sandeep, C. H., S. Naresh Kumar, and P. Pramod Kumar.”Security challenges and issues of the IoT system.” Indian Journal of Public Health Research & Development, no. 11 (2018): 748-753.
XVII Sheshikala, M et al, “Natural Language Processing and Machine Learning Classifier used for Detecting the Author of the Sentence”. International Journal of Recent Technology and Engineering (IJRTE) (2019).
XVIII S. Naresh Kumar, P. Pramod Kumar, C. H. Sandeep, and S. Shwetha. “Opportunities for applying deep learning networks to tumour classification.” Indian Journal of Public Health Research & Development, no. 11 (2018): 742-747.
XIX Siripuri Kiran, Shoban Babu Sriramoju, “A Study on the Applications of IOT”, Indian Journal of Public Health Research & Development, November 2018, Vol.9, No. 11, DOI Number: 10.5958/0976-5506.2018.01616.9
XX Sripada, Naresh Kumar et al. “Support Vector Machines to Identify Information towards Fixed-Dimensional Vector Space.”International Journal of Innovative Technology and Exploring Engineering (IJITEE),(2019).
XXI J Manasa, SN Kumar.”Distinguishing Stress Based on Social Interactions in Social Content Area”.International Journal of Pure and Applied Mathematics, 2018
XXII Sheshikala, M., Kothandaraman, D., VijayaPrakash, R. &Roopa, G. 2019, “Natural language processing and machine learning classifier used for detecting the author of the sentence”, International Journal of Recent Technology and Engineering, vol. 8, no. 3, pp. 936-939.

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Abstract:

Handover indicates transferring an ongoing telephone call or even information sessions from one cell to another. Handovers required due to the action of the mobile individual from one place to another place. Handovers are actually made use of to avoid an ongoing contact us to be actually separated. If we do not make use of handovers then whenever a user leaves the location of a certain tissue at that point its own ongoing call is instantly detached. The process of handovers needs a variety of guidelines e.g. what is actually the handover program we are making use of, how many stations are free of charge etc. In the handover procedure our service provider should additionally maintain the QoS approximately the specification. Vertical handover might be actually referred to a procedure of moving phone call attached to a network/data session from one network attached in a tissue to the core system of another.

Keywords:

Vertical handover,handoff,wireless,networks,

Refference:

I A. Hasswa, N. Nasser, H. Hassanein, Universal ethical handoff choice capacity for numerous wireless networks, 2005, Int. Conf. Wirel. Pick. Commun. Networks. (2005) 239– 243.
II A. Monelli and S. B. Sriramoju, “An Overview of the Challenges and Applications towards Web Mining,” 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on, Palladam, India, 2018, pp. 127-131.doi: 10.1109/I-SMAC.2018.8653669
III Bura Vijay Kumar, YerrollaChanti, NagenderYamsani, SrinivasAluvala, BandiBhaskar, Design a Cost Optimum for 5g Mobile Cellular Network Footing on NFV and SDN, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8, Issue-2S3, July 2019.
IV D. Todinca, C. Cernazanu-Glavan, Unfamiliar system variety formula based upon complex thinking, in: SACI 2013 – 8th IEEE Int. Symp. Appl. Comput. Intell. Informatics, Proc, 2013, pp. 467– 472.
V D. Deepika, a Krishna Kumar, MonelliAyyavaraiah, ShobanBabuSriramoju, “Phases of Developing Artificial Intelligence and Proposed Conversational Agent Architecture”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-8 Issue-12, October 2019, DOI: 10.35940/ijitee.L3384.1081219
VI J. Hou, D.C. O’Brien, Vertical handover decision-making formula making use of blurry reasoning for the packed radio-and-ow physical body, IEEE Trans. Wirel. Commun. 5 (2006) 176– 185.

VII Kiran Kumar S V N Madupu, “A Survey on Cloud Computing Service Models and Big Data Driven Networking”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 4 Issue 10, pp. 451-458, September-October 2018. Available at doi : https://doi.org/10.32628/IJSRST207257
VIII Kiran Kumar S V N Madupu, “Data Mining Model for Visualization as a Process of Knowledge Discovery”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, ISSN: 2278 – 8875, Vol. 1, Issue 4, October 2012.
IX Kiran Kumar S V N Madupu, “Advanced Database Systems and Technology Progress of Data Mining”, International Journal of Innovative Research in Science, Engineering and Technology, ISSN: 2319 – 8753, Vol. 2, Issue 3, March 2013
X KomuravellySudheer Kumar, J. Bhavana, “A Study on Data Mining towards Cloud Computing”, Indian Journal of Public Health Research & Development, Vol.9, No. 11,November 2018.
XI P. Pramod Kumar, S. Naresh Kumar, V. Thirupathi, Ch. Sandeep, “QOS AND SECURITY PROBLEMS IN 4G NETWORKS AND QOS MECHANISMS OFFERED BY 4G”, International Journal of Advanced Science and Technology, Vol. 28, No. 20, (2019), pp. 600-606
XII P. Pramod Kumar, K Sagar, “FLEXIBLE VERTICAL HANDOVER DECISION ALGORITHM FOR HETEROGENOUS WIRELESS NETWORKS IN 4G”, JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES, Vol.-14, No.-6, November – December (2019) pp 54-66
XIII P Pramod Kumar and K Sagar 2019, “A Relative Survey on Handover Techniques in Mobility Management”, IOP Conf. Ser.: Mater. Sci. Eng. 594 012027
XIV P. Pramod Kumar, K. Sagar, “Vertical Handover Decision Algorithm Based On Several Specifications in Heterogeneous Wireless Networks”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), Volume-8 Issue-9, July 2019
XV Pramod Kumar P,Thirupathi V, Monica D, “Enhancements in Mobility Management for Future Wireless Networks”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, Issue 2, February 2013
XVI PushpaMannava, “Big Data Analytics in Intra-Data Center Networks and Components Of Data Mining”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 1 Issue 3, pp. 82-89, November-December 2016. Available at doi : https://doi.org/10.32628/CSEIT206272
XVII PushpaMannava, “A Study on the Challenges and Types of Big Data”, “International Journal of Innovative Research in Science, Engineering and Technology”, ISSN(Online) : 2319-8753, Vol. 2, Issue 8, August 2013
XVIII PushpaMannava, “Data Mining Challenges with Bigdata for Global pulse development”, International Journal of Innovative Research in Computer and Communication Engineering, ISSN(Online): 2320-9801, vol 5, issue 6, june 2017
XIX Soumya, Pramod Kumar Poladi, VahiniSiruvoru. A Witness Oriented Secure Location Provenance Modelling for Location Proofs, International Journal TEST Engineering and Management, Volume 82, Jan-Feb 2020, Page Nos: 2793-2797, ISSN: 0193-4120.
XX SrinivasAluvala, K. Raja Sekhar, DeepikaVodnala, A novel technique for node authentication in mobile ad hoc networks, Perspectives in Science, Volume 8, 2016, Pages 680-682
XXI SiripuriKiran, ShobanBabuSriramoju, “A Study on the Applications of IOT”, Indian Journal of Public Health Research & Development, November 2018, Vol.9, No. 11, DOI Number: 10.5958/0976-5506.2018.01616.9
XXII X. Yan, N. Peanut, Y.A. S¸ ekerciolu, A taking a trip stretch revelation set up technique to lower needless handovers arising from tissue systems to wlans, IEEE Commun. Lett. 12 (2008) 14– 16.

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Abstract:

The interdisciplinary field of Data Mining (DM) develops from the assemblage of statistics as well as machine learning (artificial intelligence). It provides a technology that helps to assess and also recognize the relevant information contained in a database, as well as it has been used in a large number of areas or requests. Exclusively, the idea DM originates from the correlation between the seek beneficial info in data banks as well as exploration useful minerals in a hill.

Keywords:

Data Mining,KDD process,security,

Refference:

I Bhavana Jamalpur, Komuravelly Sudheer Kumar, “Implementation of Bovw Model Towards Obtaining Discriminative Features of the Images”, International Journal of Advanced Science and Technology, Vol. 28, No. 17, (2019).
II Bhavana Jamalpur,“Analysis of Noise Reduction of Large Data sets Using Mathematical Tools in Data Mining”, International Journal of Pure and Applied Mathematics, Volume 120 No. 6 2018, 7061-7070 ISSN: 1314-3395
III D. Deepika, a Krishna Kumar, Monelli Ayyavaraiah, Shoban Babu Sriramoju, “Phases of Developing Artificial Intelligence and Proposed Conversational Agent Architecture”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-8 Issue-12, October 2019, DOI: 10.35940/ijitee.L3384.1081219
IV D. Ramesh, Sallauddin Md, Syed Nawaz Pasha “Enhancements of Artificial Intelligence and Machine Learning “ , International Journal of Advanced Science and Technology, vol 28,No.17(2019),pp-16-23
V Kiran Kumar S V N Madupu, “Key Methodologies for Designing Big Data Mining Platform Based on CloudComputing”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 1 Issue 2, pp. 190-196, September-October 2016. Available at doi : https://doi.org/10.32628/CSEIT206271
VI Kiran Kumar S V N Madupu, “Tool to Integrate Optimized Hardware and Extensive Software into Its Database to Endure Big Data Challenges”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 5, pp. 272-279, September-October 2019. Available at doi : https://doi.org/10.32628/CSEIT206275

VII Kiran Kumar S V N Madupu, “Functionalities, Applications, Issues and Types of Data Mining System”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 5, Issue 8, August 2017
VIII KomuravellySudheer Kumar, J. Bhavana, “A Study on Data Mining towards Cloud Computing”, Indian Journal of Public Health Research & Development, Vol.9, No. 11,November 2018.
IX Mohammed Ali Shaik, “A Survey on Text Classification methods through Machine Learning Methods”, International Journal of Control and Automation, Vol. 12, No.6, (2019), pp. 390 – 396.
X Mohammed Ali Shaik, “Time Series Forecasting using Vector quantization”, International Journal of Advanced Science and Technology, Vol. 29, No. 4, (2020), pp. 169-175.
XI Mohammed Ali Shaik, P.Praveen, Dr.R.VijayaPrakash, “Novel Classification Scheme for Multi Agents”, Asian Journal of Computer Science and Technology, ISSN: 2249-0701 Vol.8 No.S3, 2019, pp. 54-58.
XII Monelli and S. B. Sriramoju, “An Overview of the Challenges and Applications towards Web Mining,” 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on, Palladam, India, 2018, pp. 127-131.doi: 10.1109/I-SMAC.2018.8653669
XIII P. Pramod Kumar, S. Naresh Kumar, V. Thirupathi, Ch. Sandeep, “QOS AND SECURITY PROBLEMS IN 4G NETWORKS AND QOS MECHANISMS OFFERED BY 4G”, International Journal of Advanced Science and Technology, Vol. 28, No. 20, (2019), pp. 600-606
XIV Praveen P., Rama B. (2018) A Novel Approach to Improve the Performance of Divisive Clustering- BST. In: Satapathy S., Bhateja V., Raju K., Janakiramaiah B. (eds) Data Engineering and Intelligent Computing. Advances in Intelligent Systems and Computing, vol 542. Springer, Singapore
XV PushpaMannava, “Big Data Analytics in Intra-Data Center Networks and Components Of Data Mining”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 1 Issue 3, pp. 82-89, November-December 2016. Available at doi : https://doi.org/10.32628/CSEIT206272
XVI PushpaMannava, “A Study on the Challenges and Types of Big Data”, “International Journal of Innovative Research in Science, Engineering and Technology”, ISSN(Online) : 2319-8753, Vol. 2, Issue 8, August 2013

XVII PushpaMannava, “Data Mining Challenges with Bigdata for Global pulse development”, International Journal of Innovative Research in Computer and Communication Engineering, ISSN(Online): 2320-9801, vol 5, issue 6, june 2017
XVIII S. Naresh Kumar, P. Pramod Kumar, C. H. Sandeep, and S. Shwetha. “Opportunities for applying deep learning networks to tumour classification.” Indian Journal of Public Health Research & Development, no. 11 (2018): 742-747.
XIX Sandeep, C. H., S. Naresh Kumar, and P. Pramod Kumar. “Security challenges and issues of the IoT system.” Indian Journal of Public Health Research & Development, no. 11 (2018): 748-753.
XX SiripuriKiran, ShobanBabuSriramoju, “A Study on the Applications of IOT”, Indian Journal of Public Health Research & Development, November 2018, Vol.9, No. 11, DOI Number: 10.5958/0976-5506.2018.01616.9
XXI Thirupathi, Ch. Sandeep, S. Naresh Kumar, P. Pramod Kumar ,A COMPREHENSIVE REVIEW ON SDN ARCHITECTURE, APPLICATIONS AND MAJOR BENIFITS OF SDN, International Journal of Advanced Science and Technology, Volume 28, Issue 20, December 2019, Page Nos: 607-614, ISSN: 2005-4238.
XXII ThotapallyMounika Reddy, BhavanaJamalpur, “Dynamic and Secure Ranked Keyword Search over Encrypted Cloud Data”,International Journal of Research,Volume 05 Issue 07,March 2018.
XXIII Y.Chanti, J. Bhavana, “Fast Nearest Neighbor Search Partial Query With Keyword”,International Journal For Technological Research In Engineering ,Volume 3, Issue 4, December-2015, ISSN (Online): 2347 – 4718.

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Abstract:

Abstract There are limited studies that address the quality of measurement items in motor observation questionnaire for teachers (MOQ-T)even though their use has gained attention locally. One aspect of the item quality that can be reviewed is content validity through expert consensus. Therefore, this study aims to examine the content validity of MOQ-T instrument items using an expert panel. A total of 15 experts in the areas of measurement and evaluation, occupational therapy, motor development and special education were selected for this study through purposive sampling. A total of 18 MOQ-T items were analysed using Content Validity Ratio (CVR) analysis and the items were reviewed through email correspondence and face to face during meeting sessions with experts. The findings showed that all items are significant as they exceed the critical CVR value of 0.49. However, one new item was added as one of the items was broken down to two sentences in response to expert suggestions to avoid items that are 'double barrelled' where conjunctions like 'and’ are used to describe two different issues for one intended response. Subsequently, new items were derived to measure the skills needed. This study contributed to new MOQ-T with 19 items that can be used by teachers to study special needs students in Malaysia. For further research, it is proposed that new psychometric measurement theories, such as the Rasch measurement model can be added to improve the reliability of motor measurement items for teachers including MOQ-T. In addition, this study created an opportunity to review the localised version of MOQ-T that can be used for the initial screening of developmental coordination disorder (DCD) problems in the context of special needs students in Malaysia.

Keywords:

content validity,expert panel,content validity ratio, Motor observation questionnaire for teachers (MOQ-T),Developmental coordination disorder (DCD),

Refference:

I. Ab Aziz, A., Yusof, Z.M. &Mokhtar, U.A.“Electronic document and records management system (edrms) adoption in public sector-instrument’s content validation using content validation ratio (CVR)”. Journal of Physics: Conference Series 1196(1), 2019.

II. Ahari, M.N., Azad, A., Alizadeh-Zarei, M., Ebadi, A., Parand, A. &Mohammadi, P. “Development and Validity of the School Interim Competency of Performance Skill Battery Scale (SICPSBS)”. International Journal of Paediatrics-Mashhad 6(11): 8451–8473, 2018.

III. Ahmad, N.A., Drus, S.M., Kasim, H. & Othman, M.M. “Assessing Content Validity of Enterprise Architecture Adoption Questionnaire (EAAQ) Among Content Experts”. Proceedings of 2019 IEEE 9th Symposium on Computer Applications & Industrial Electronics (ISCAIE) 160–165, 2019.

IV. Ali, N., Tretiakov, A. &Whiddett, D. “A Content Validity Study for a Knowledge Management System Success Model in Healthcare”. Journal of Information Technology Theory and Application (JITTA) 15(2): 3, 2014.

V. Almanasreh, E., Moles, R. & Chen, T.F. “Evaluation of methods used for estimating content validity”. Research in Social and Administrative Pharmacy 15(2): 214–221. http://dx.doi.org/10.1016/j.sapharm.2018.03.066, 2019.

VI. Asunta, P., Viholainen, H., Ahonen, T. &Rintala, P. “Psychometric properties of observational tools for identifying motor difficulties – a systematic review”. BMC Pediatrics 19(1): 1–13, 2019.

VII. Asunta, Piritta & Viholainen, Helena & Westerholm, Jari & Rintala, Pauli.“ Cultural Adaptation of Motor Observation Questionnaire for Teachers – development of Finnish version (MOQ-T-FI)”. Liikunta & Tiede. 52. 78-86, 2015.

VIII. Asunta, P., Viholainen, H., Ahonen, T. &Rintala, P. “Reliability and validity of theFinnish version of Motor Observation Questionnaire for Teachers (MOQ-T-FI)”:Abstracts of 11th International Conference on Developmental Coordination Disorder (DCD11) . Journal of Comorbidity 5(2): 32, 2015.
IX. Asunta, P., Viholainen, H,.Ahonen, T.,Cantell, M., Westerholm, J., Schoemaker, MM.,Rintala, P. 2017. “Reliability and validity of the Finnish version of the motor observation questionnaire for teachers”. Hum Movement Sci. 53:63–71, 2017.

X. Ayre, C. & Scally, A.J.“Critical values for Lawshe’s content validity ratio: Revisiting the original methods of calculation”. Measurement and Evaluation in Counselling and Development 47(1): 79–86, 2014.

XI. Baghestani, A.R., Ahmadi, F., Tanha, A. &Meshkat, M. “Bayesian Critical Values for Lawshe’s Content Validity Ratio”. Measurement and Evaluation in Counselling and Development 52(1): 69–73. https://doi.org/10.1080/07481756.2017.1308227, 2017.

XII. Bardid, F., Vannozzi, G., Logan, S.W., Hardy, L.L. & Barnett, L.M. “ A hitchhiker’s guide to assessing young people’s motor competence: Deciding what method to use”. Journal of Science and Medicine in Sport 22(3): 311–318. https://doi.org/10.1016/j.jsams.2018.08.007, 2019.

XIII. Bekwa, N.N.”The Development And evaluation of africanised items for multicultural cognitive assessment”. University of South Africa. PhD Dissertation. University of South Africa, 2016.

XIV. Braccialli, A.C., Araújo, R. de C.T. & Scherer, M. “Translation and cross-cultural adaptation of the Educational Technology Device Predisposition Assessment into Brazilian–Portuguese language”. Disability and Rehabilitation (10) 1–7. https://doi.org/10.1080/09638288.2019.1624839, 2019.

XV. Cairney J, Veldhuizen S. “Is developmental coordination disorder a fundamental cause of inactivity and poor health-related fitness in children?”.Dev Med Child Neurol. 55(s4):55–58, 2013.

XVI. Camden, C., Wilson, B., Kirby, A., Sugden, D. &Missiuna, C. 2015. “Best practice principles for management of children with developmental coordination disorder (DCD): Results of a scoping review”. Child: Care, Health and Development 41(1): 147–159, 2015.

XVII. Capistrano R, Ferrari EP, Souza LPD, Beltrame TS, Cardoso FL. “Concurrent validation of the MABC-2 motor tests and MABC-2 checklist according to the developmental coordination disorder questionnaire”. Br. Motriz: J Phys Ed. 21(1):100–6, 2015.

XVIII. Cohen, R. J., Swerdlik, M., &Sturman, E. “Psychological testing and assessment: and introduction of test and measurement (8thed.)”. New York: McGraw Hill, 2012

XIX. Colton, D. & Covert, R.W. “Designing and constructing instruments for social research and evaluation”. First Ed. San Francisco: John Wiley & Sons, Inc, 2007.

XX. Connell, L.E., Carey, R.N., de Bruin, M., Rothman, A.J., Johnston, M., Kelly, M.P. &Michie, S. “Links Between Behaviour Change Techniques and Mechanisms of Action: An Expert Consensus Study”. Annals of Behavioural Medicine 53(8): 708–720, 2019.

XXI. Creswell, J.W. “Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research” (Enhanced Pearson eText with Loose-Leaf Version–Access Card Package). New York: Pearson Education, Inc, 2015.

XXII. Creswell, J. W., & Creswell, J. D. “Research design: Qualitative, quantitative, and mixed methods approaches”. Thousand Oaks: Sage publications, 2017.

XXIII. Deming, W. E. “Sample design in business research (Vol. 23)”. New York: John Wiley & Sons, 1990.

XXIV. De Milander M, Coetzee FF, Venter A. “Teachers’ ability to identify children with developmental coordination disorder”. African Journal for Physical Activity and Health Sciences 2(Issue-41):990–1005, 2016.

XXV. DeVellis, R.F. “Scale Development Theory and Applications (Fourth Edition)”. Thousand Oaks: SAGE Publication, 2016.

XXVI. Dimitropoulou, D., Evaggelinou, C., Kourtesis, T., Ellinoudis, T. “Teacher rating checklists as assessment tools for the detection of Developmental Coordination Disorder: Their suitability for use by educators”. PANR Journal June 21, 2018. Retrieved from https://www.panr.com.cy/?p=1739., 2018.

XXVII. Dixon, D. & Johnston, M. “Content validity of measures of theoretical constructs in health psychology: Discriminant content validity is needed”. British Journal of Health Psychology 2019 ept. 24 (3): 477–484, 2019.

XXVIII. Doderer L, Miyahara M. “Critical triangulation of a movement test, questionnaires, and observational assessment for children with DCD”. Int J TherRehabil. 20(9):435–42, 2013.
XXIX. Dussart G. “Identifying the clumsy child in school: an exploratory study”. Br J Special Education 21(2):81–6, 1994.

XXX. Dutt, A., Tan, M., Alagumalai, S. & Nair, R. “Development and Validation of the Ability in Behaviour Assessment and Interventions for Teachers Using Delphi Technique and Rasch Analysis”. Journal of Autism and Developmental Disorders 49(5): 1976–1987. http://dx.doi.org/10.1007/s10803-019-03887-4, 2019.
XXXI. Engel-Yeger B, Hanna-Kassis A, Rosenblum S. “Can gymnastic teacher predict leisure activity preference among children with developmental coordination disorders (DCD)?” Res DevDisabil. 33(4):1006–13, 2012.
XXXII. GhazaliDarusalam,&SufeanHussin. “MetodologipenyelidikandalamPendidikan”. Kuala Lumpur: PenerbitUniversiti Malaya, 2016.
XXXIII. Giofrè, D., Barbato, L., Cornoldi, C. &Schoemaker, M.M. “Il MOQ-T: Un questionario per gliinsegnanti di facile utilizzo per la rilevazionedeisintomi del disturbodellacoordinazionemotoria”. PsicologiaClinicadelloSviluppo 19(3): 499–509, 2015.

XXXIV. Giofrè, D., Cornoldi, C. &Schoemaker, M.M. “Identifying developmental coordination disorder: MOQ-T validity as a fast screening instrument based on teachers’ ratings and its relationship with praxic and visuospatial working memory deficits”. Research in Developmental Disabilities 35(12): 3518–3525. http://dx.doi.org/10.1016/j.ridd.2014.08.032, 2014.

XXXV. Green D, Bishop T, Wilson BN, Crawford S, Hooper R, Kaplan B, Baird G.“ Is questionnaire-based screening part of the solution to waiting lists for children with developmental coordination disorder?” .Br J OccupTher. 68(1):2–10,2005.

XXXVI. Hamid Salehi, MahshidZarezadeh, S.B. “Validity and Reability of Persian Motor Obsevation Questionnaire for Teachers (PMOQ-T)”. Iranian Journal of Psychiatry & Clinical Psychology 18(3): 211–219. http://ijpcp.iums.ac.ir/article-1-1887-en.html, 2012.

XXXVII. Hasmayuzee, N., Hashim, B., Haron, Z. &Surat, S. “Ciri-CiriPsikometrikInstrumen Music Model of Academic Motivation Inventory (MMAMI)”.Proceeding of Seminar AntarabangsaIsu-IsuPendidikan (ISPEN2018), 2018.

XXXVIII. Hughes, C., Foley, S., White, N. & Devine, R.T. “School readiness in children with special educational needs and disabilities: Psychometric findings from a new screening tool, the Brief Early Skills, and Support Index”. British Journal of Educational Psychology 88(4): 06–627, 2018.

XXXIX. Hyrkäs, K., Appelqvist-Schmidlechner, K. &Oksa, L. “Validating an instrument for clinical supervision using an expert panel”. International Journal of Nursing Studies 40(6): 619–625, 2003.

XL. IlkerE,,Sulaiman A., &Musa, R.S.A. “Comparison of Convenience Sampling and Purposive Sampling Comparison of Convenience Sampling and Purposive Sampling”. American Journal of Theoretical and Applied Statistics 5 (1). January 2016:. 1-4, 2016.

XLI. James, C., MacKenzie, L. & Capra, M. “Content validity of the WorkHab functional capacity evaluation”. Australian Occupational Therapy Journal 66(3): 380–392, 2019.

XLII. Jongmans, M.J., Smits-Engelsman, B.C.M. &Schoemaker, M.M. “Consequences of Comorbidity of Developmental Coordination Disorders and Learning Disabilities for Severity and Pattern of Perceptual-Motor Dysfunction”. Journal of Learning Disabilities 36(6): 528–537, 2003.
XLIII. Junaid K, Harris SR, Fulmer KA, Carswell A. “Teachers’ use of the MABC checklist to identify children with motor coordination difficulties”. PediatrPhysTher. 12(4):158–63, 2000.

XLIV. Kadar, M., Ibrahim, S., Razaob, N.A., Chai, S.C. &Harun, D. “Validity and reliability of a Malay version of the Lawton instrumental activities of daily living scale among the Malay speaking elderly in Malaysia”. Australian Occupational Therapy Journal 65(1): 63–68, 2018.

XLV. Kuisma, S. &Vaahtio, S. “Motoristentaitojenhavainnointijaarviointi päiväkodissa”.https://www.theseus.fi/bitstream/10024/116974/1/Kuisma_Satu_ja+Vaahtio_Satu.pdf, 2016.

XLVI. Lawshe, C.H. “Quantitative Approach to Content Validity”. Personnel Psychology 28(4): 563–575, 1975.

XLVII. Lynn, M. “Determination and quantification of content validity”. Nursing Research, 35(6), 382-385. http://dx.doi.org/10.1097/00006199-198611000-00017, 1986.

XLVIII. Mehdizadeh, A., Shafiee, M., Khadem-Rezaiyan, M., Sardar, M.A., Vatanparast, H., Rose, E., Rajabzadeh, M. &Nematy, M. “Evidence for the Validity of the Children’s Attraction to Physical Activity (CAPA) Scale in Iranian Preschool Children”. Journal of Pediatric Nursing 44: 52–57, 2019.

XLIX. MichalisAnastasiadis, Thomas Kourtessis, E.Z.“Educator’s ability to identify students with coordination disorders: A review of literature”. Arab Journal of Nutrition and Exercise (AJNE) 2(3): 139-151, 2017.

L. Mohd Effendi @ Ewan MohdMatore, HisyamsaniIdris, Normawati Abdul Rahman& Ahmad ZamriKhairani. “KesahanKandunganPakarInstrumen IKBAR BagiPengukuran AQ MenggunakanNisbahKesahanKandungan”. Proceeding of International Conference On Global Education V (ICGE V) (May): 979–997, 2017.

LI. Mohd Effendi MohdMatore& Ahmad ZamriKhairani. “Assessing the Content Validity of IKBAR using Content Validity Ratio”. Australian Journal of Basic and Applied Sciences 9(7): 255–257, 2015a.

LII. Mohd Effendi MohdMatore& Ahmad ZamriKhairani. “Face Validity of IKBAR using CVR method”. International Journal of Advances in Science, Engineering and Technology 3(2): 63–66, 2015b.

LIII. Nakai A, “The Development and Cross-Cultural Adaptation of the Developmental Coordination Disorder Questionnaire (DCDQ) and the Motor Observation Questionnaire for teachers (MOQ-T) for Japanese Children”. Presented at the 57thAnnual Meeting; 2010 Oct 26–31, AACAP, 2010.
LIV. NetelenbosJB.“Teachers’ ratings of gross motor skills suffer from low concurrent validity”. Hum Movement Sci. 24(1):116–37. https://doi.org/1 0.1016/j.humov.2005.02.001, 2005.

LV. Nowak, A. &Schoemaker, M. “Psychometric properties of the Polish version of the Motor Observation Questionnaire for teachers (MOQ-T)”. Human Movement 19(2): 31–38, 2018.

LVI. Nunnally, J.C. & Bernstein, I.H. “Psychometric Theory 3rd Edition”. New York: McGraw Hill, 1994.

LVII. Rubio D.M, Marla Berg-Weger, S.S.T. & E. Suzanne Lee, and S.R. “Objectifying content validity: Conducting a content validity study in social work research”. Social Work Research V.27(2): 94-104, 2003.

LVIII. Piek, J.P., Barrett, N.C., Dyck, M.J. &Reiersen, A.M. “Can the Child Behavior Checklist be used to screen for motor impairment?”. Developmental Medicine and Child Neurology 52(2): 200–204, 2010.

LIX. Piek JP, Edwards K. “The identification of children with developmental coordination disorder by class and physical education teachers”. Br J Educ Psychol. 67(1):55–6, 1997.

LX. Piek, J.P., Hands, B. &Licari, M.K.“Assessment of motor functioning in the preschool period”. Neuropsychology Review 22(4): 402–413, 2012.

LXI. Prado, M., Magalhães, L. & Wilson, B. “Cross-cultural adaptation of the Developmental Coordination Disorder Questionnaire for Brazilian children”. Brazilian Journal of Physical Therapy 13(3): 236–243, 2009.

LXII. Ramli, N.F., Talib, OManaf, U.K. A. & Hassan, S.A. “Content Validity of STEMTIP Using CVR Method”. International Journal of Academic Research in Business and Social Sciences 8(7): 1118–1125, 2018.

LXIII. Rodrigues, I.B., Adachi, J.D., Beattie, K.A. &MacDermid, J.C. “Development and validation of a new tool to measure the facilitators, barriers and preferences to exercise in people with osteoporosis”. BMC Musculoskeletal Disorders 18(1): 1–9, 2017.

LXIV. Rosenblum S. “The development and standardization of the children activity scales (ChAS-P/T) for the early identification of children with developmental coordinationdisorders”.ChildCareHealthDev. 32(6):619–32, 2006.

LXV. Schoemaker, M. M. “Manual of the motor observation questionnaire for teachers”. Groningen: Internal Publication. Center for Human Movement Sciences, 2003.
LXVI. Schoemaker, M.M., Flapper, B., Verheij, N.P., Wilson, B.N., Reinders-Messelink, H.A. & de Kloet, A. “Evaluation of the developmental coordination disorder questionnaire as a screening instrument”. Developmental Medicine and Child Neurology 48(8): 668–673, 2006.

LXVII. Schoemaker, M.M., Flapper, B.C.T., Reinders-Messelink, H.A. &Kloet, A. de. “Validity of the motor observation questionnaire for teachers as a screening instrument for children at risk for developmental coordination disorder”. Human Movement Science 27(2): 190–199, 2008.
LXVIII. Schoemaker MM, Niemeijer AS, Flapper BC, Smits-Engelsman BC. “Validity and reliability of the movement assessment battery for children-2 checklist for children with and without motor impairments”. Dev Med Child Neurol. 54(4):368–75, 2012.

LXIX. Sekaran, U. &Bougie, R. “Research Methods for Business A Skill-Building Approach”. Illinois: Wiley, 2016.

LXX. Shalbafan, M., Najarzadegan, M., Soraya, S., Rashedi, V., Najafian, R., Ahmadkhaniha, H., Seddigh, R., Hassanzadeh, M., Saeedi, V. &Kamalzadeh, L. “Validity and Reliability of the Persian Version of Internet Sex Screening Test in Iranian Medical Students”. Sexual Addiction & Compulsivity 26 2019 3-4: 1–10, 2019.

LXXI. Shorten, A. &Moorley, C. “Selecting the sample”. Evidence-Based Nursing 17(2): 32–33, 2014.

LXXII. Shrotryia, V.K. &Dhanda, U. “Content Validity of Assessment Instrument for Employee Engagement”. SAGE Open January-March 2019: 1-7, 2019.

LXXIII. Sourani, A. &Sohail, M. “The Delphi Method: Review and Use in Construction Management Research”. International Journal of Construction Education and Research 11(1): 54–76, 2015.

LXXIV. Stevenson, W.A. “Examining School Readiness”. PhD Dissertation: University of Kentucky, 2019.

LXXV. Swami, V. & Barron, D. “Translation and validation of body image instruments: Challenges, good practice guidelines, and reporting recommendations for test adaptation”. Body Image 1–17. https://doi.org/10.1016/j.bodyim.2018.08.014, 2018.

LXXVI. Swan, M.A. & Hobbs, B.B. “Querying Rural Content Experts Using an Online Questionnaire”. Online Journal of Rural Nursing and Health Care 18(2): 189–203, 2018.

LXXVII. Syed SofianSyed,Mohammad Aziz Shah MohdArip, Muhammad BazlanMustafa,MohdHanafiMohdYasin“Pembinaan Dan KesahanInventoriKebijaksanaanEmosi”Confrence.KUIS.edu.my. 014-ISRA-2017:1–8, 2017.

LXXVIII. Taherdoost, H. “Validity and Reliability of the Research Instrument; How to Test the Validation of a Questionnaire/Survey in a Research”. SSRN Electronic Journal 5(3): 28–36, 2018.
LXXIX. Tamuri, A.H. “The Validity and Reliability of Da’ Wah Education” :108–118, 2019.

LXXX. ThalissaManiaes, Ana Paula Carraro&IndiaraSoares Oliveira. “Evaluation of the Translation, Cross-cultural Adaptation and Properties of Measurement of the FACT-BMT Questionnaire”. Journal of Pharmacy and Pharmacology 7(1): 1–14, 2019.

LXXXI. Tin, A.C. &Wah, L.L. “Instrumen penilaiankualitimodulpengajaran: Pengujianciripsikometrik”. Jurnal Kurikulum Pengajaran Asia Pasifik 4(4): 1–19, 2016.

LXXXII. Tsai, T.I., Luck, L., Jefferies, D. & Wilkes, L.”Challenges in adapting a survey: Ensuring cross-cultural equivalence”. Nurse Researcher 26(1): 21–25,2018.

LXXXIII. Tsang, S., Royse, C.F. &Terkawi, A.S. “ Guidelines for developing, translating, and validating a questionnaire in perioperative and pain medicine”. Saudi Journal of Anaesthesia 13(3): 281,2017.

LXXXIV. Van Dellen, T., & Kalverboer, A. F. “Groninger Motorische Observatielijst [Groningen Motor Observation Scale]”. Laboratory for experimental psychology, Groningen State University,1990.

LXXXV. Vermeulen, J., Peersman, W., Quadvlieg, L., Fobelets, M., De Clercq, G., Swinnen, E. &Beeckman, K.Development and validation of the Midwife Profiling Questionnaire assessing women preferred perinatal care professional and knowledge of midwives’ legal competences. Sexual and Reproductive Healthcare 16(September 2017): 23–32. https://doi.org/10.1016/j.srhc.2018.01.003, 2018.

LXXXVI. Wilson, B.N., Crawford, S.G., Green, D., Roberts, G., Aylott, A., & Kaplan, B. “Psychometric Properties of the Revised Developmental Coordination Disorder Questionnaire”. Physical & Occupational Therapy in Pediatrics, 29(2):182-202,2009.

LXXXVII. Wilson, P.H., Smits-Engelsman, B., Caeyenberghs, K., Steenbergen, B., Sugden, D., Clark, J., Mumford, N. & Blank, R. “Cognitive and neuroimaging findings in developmental coordination disorder: new insights from a systematic review of recent research””. Developmental Medicine and Child Neurology 59(11): 1117–1129,2017.

LXXXVIII. Wilson, F. R., Pan, W., &Schumsky, D. A. Recalculation of the Critical Values for Lawshe’s Content Validity Ratio”. Measurement and Evaluation in Counseling and Development, 45(3), 197–210, 2012.

LXXXIX. World Health Organization. “The World Health Organization quality of life assessment (WHOQOL): Position paper 1995”.Social Science and Medicine, 41(10): 1403-1409,1995.

XC. Wright HC, Sugden DA, Ng R, Tan J. “Identification of children with movement problems in Singapore: usefulness of the movement ABC checklist”. APAQ. 11(2):150–7, 1994.

XCI. Wright HC, Sugden DA. “A two-step procedure for the identification of children with developmental co-ordination disorder in Singapore”. Dev Med Child Neurology 38(12):1099–105, 1996.

XCII. Zamanzadeh, V., Ghahramanian, A., Rassouli, M., Abbaszadeh, A., Alavi-Majd, H. & Nikanfar, A.-R. “Design and Implementation Content Validity Study: Development of an instrument for measuring Patient-Centered Communication”. Journal of Caring Sciences 4(2): 165–178. http://dx.doi.org/10.15171/jcs.2015.017, 2015.

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Abstract:

This paper gives a complete assessment of the various strategies used to decorate the skills of Low Voltage Ride Through (LVRT) of Double Fed Induction Generators (DFIG) primarily based wind turbine systems (WT). As the world is using about 20% to 25% of renewable energy from wind using DFIG primarily based WT machine is at once connected to the grid without the digital interface of power, as a result the terminal voltage or reactive electricity output can’t manage. Therefore, unique LVRT approaches based at the implementing additional active interface technologies had been proposed within this paper. Many techniques are developed nowadays to overcome the issue of this low voltage due to faults. This paper tries to define such active methods to short the gap by way of presenting a complete analysis of these LVRT strategies for DFIG based WECS in terms of overall adaptive performance, complexity of controllers, and cost effectiveness. Here characteristic of this paper is to highlight the methods for increasing the ability of LVRT relying on the configuration of the relationship into 3 major areas according to its grid integrations. In this paper hybrid (series-shunt) connections of FACT devices are used in WECS to study its effectiveness and benefits. The mathematical models of the whole system are simulated through MATLAB simulink and results are discussed.  

Keywords:

LVRT, DFIG,WT,FACT,WECS,

Refference:

I. Abdulhamed Hwas, Reza Katebi, Wind Turbine Control Using PI Pitch Angle Controller, IFAC Proceedings Volumes, Volume 45, Issue 3, 2012, Pages 241-246, ISSN 1474-6670, ISBN 9783902823182, https://doi.org/10.3182/20120328-3-IT-3014.00041.
II. B. P. Ganthia, V. Agarwal, K. Rout and M. K. Pardhe, “Optimal control study in DFIG based wind energy conversion system using PI & GA,” International Conference on Power and Embedded Drive Control (ICPEDC), Chennai, 2017, pp. 343-347.
III. Bekhada, Hamane Doumbia, Mamadou, BOUHAMIDA, Mohamed Draou, Azeddine CHAOUI, Hichamn Benghanem, Mustapha, “Comparative Study of PI, RST, Sliding Mode and Fuzzy Supervisory Controllers for DFIG based Wind Energy Conversion System”, International Journal of Renewable Energy Research (IJRER), Volume – 5, 2015/12/26, Page 1174 – 1185.
IV. Djeriri, Youcef & Meroufel, Abdelkader & Massoum, Ahmed & Boudjema, Zinelaabidine. (2014). A comparative study between field oriented control strategy and direct power control strategy for DFIG. Journal of Electrical Engineering. 14. 169-178.
V. B. P. Ganthia, S. Mohanty, P. K. Rana and P. K. Sahu, “Compensation of voltage sag using DVR with PI controller,” 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, 2016, pp. 2138-2142, doi: 10.1109/ICEEOT.2016.7755068.
VI. B.P. Ganthia, P.K. Rana, T. Patra, R. Pradhan and R. Sahu, “Design and Analysis of Gravitational Search Algorithm Based TCSC Controller in Power System”, Materials Today: Proceedings, vol. 5, no. 1, pp. 841-847, 2018.
VII. Moghadasi,aA.,aSarwat,aA.,a&aGuerrero,aJ.aM.a(2016).aAaComprehensiveaReviewaofaLow-Voltage-Ride-ThroughaMethodsaforaFixed SpeedaWindaPoweraGenerators.aRenewablea&aSustainableaEnergyaReviews,a55,a823–839.ahttps://doi.org/10.1016/j.rser.2015.11.020.
VIII. Ganthia, B.P., Rout, K.: Deregulated power system based study of agc using pid and fuzzy logic controller. Int. J. Adv. Res. 4(06) (2016)
IX. Siraj, Kiran, HarisSiraj, and MashoodNasir. “Modeling and control of a doubly fed induction generator for grid integrated wind turbine.” Power Electronics and Motion Control Conference and Exposition (PEMC), 2014 16th International. IEEE, 2014.
X. S. M. Muyeen, Md. Hasan Ali, R. Takahashi, T. Murata, J. Tamura, Y. Tomaki, A. Sakahara and E. Sasano, “Comparative Study on Transient Stability Analysis of Wind Turbine Generator System Using Different Drive Train Models”, IET Renewable Power Generation, Vol. 1, No, 2, pp. 131-141, June 2007.
XI. Zhang, B.; Hu, W.; Hou, P.; Tan, J.; Soltani, M.; Chen, Z. “Review of Reactive Power Dispatch Strategies for Loss Minimization in a DFIG-based Wind Farm” Energies 2017, 10, 856.

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Abstract:

The DC-DC converter steps up or steps down depending upon the application requirement. In soft robots, they are used to amplify the signal from milli-volts to several kilovolts. This allows soft robots to obtain diverse features. The current work describes the details of DC-DC converter based on the Cockcroft Walton Multiplier circuit which is developed to control the output voltage required to electrically potentially induce the actuation of dielectric elastomer films used in the bio-mimetic eye. Soft robots require careful manipulation of voltage and current signals. The input to converter is 12V Alternating Voltage whereas the output is 3.7kV. Dielectric elastomer films require voltages in several kilovolts for actuation. This converter is suitable for soft robot applications because of being low cost, lightweight and portability. In the study, a multiplier circuit is constructed based on the Cockcroft Walton generator.

Keywords:

Electro-active (EA),dielectric elastomer (DE),Human Machine Interference (HMI),Switched Mode Power Supply (SMPS),Cockcroft Walton Multiplier (CWM),

Refference:

I. Andrea Mari˜no-L´opez, Ana Sousa-Castillo, Enrique Carb´o-Argibay, Francisco Otero-Espinar, Ramon A Alvarez-Puebla, MoisesP´erezLorenzo, and Miguel A Correa-Duarte. Laser-protective soft contact lenses: Keeping an eye on the eye through plasmonics. Applied Materials Today, 15:1–5, 2019.
II. Chandra Shekhar and Shirshu Varma. An optimized 2.4 ghzrf energy harvester for energizing low-power wireless sensor platforms. Journal of Circuits, Systems and Computers, 28(06):1950104, 2019.
III. Chitra Sharma, AK Jhala, and Manish Prajapati. Selection of passive component for Cockcroft walton voltage multiplier: A low cost technique. International Research Journal of Engineering and Technology, 3(2):667–671, 2016.
IV. CK Dwivedi and MB Daigvane. Multi-purpose low cost dc high voltage generator (60kv output), using cockcroft-walton voltage multiplier circuit. In 2010 3rd International Conference on Emerging Trends in Engineering and Technology, pages 241–246. IEEE, 2010.
V. David F Spencer, Rahmat Aryaeinejad, and Edward L Reber. Using the cockroft-walton voltage multiplier with small photomultipliers. IEEE Transactions on nuclear Science, 49(3):1152–1155, 2002.
VI. David Frazer Spencer, Rahmat Aryaeinejad, and EL Reber. Using the cockroft-walton voltage multiplier design in handheld devices. In 2001 IEEE Nuclear Science Symposium Conference Record (Cat. No. 01CH37310), volume 2, pages 746–749. IEEE, 2001.
VII. DeveshMalviya, AK Bhardwaj, Mangesh Borage, and Sunil Tiwari. Simulation studies on current fed cockroft-waltonmultiplier. In Proceedings of the seventh DAE-BRNS Indian particle accelerator conference: book of abstracts, 2015.
VIII. Dmitri Vinnikov, Andrii Chub, Oleksandr Korkh, Elizaveta Liivik, FredeBlaabjerg, and Samir Kouro. Mppt performance enhancement of low cost pv micro converters. Solar Energy, 187:156–166, 2019.
IX. Edgar Everhart and Paul Lorrain. The cockcroft-walton voltage multiplying circuit. Review of Scientific Instruments, 24(3):221–226, 1953.
X. Ehsan Hajiesmaili and David R Clarke. Reconfigurable shape-morphing dielectric elastomers using spatially varying electric fields. Nature communications, 10(1):183, 2019.
XI. Hui Zhang, Min Dai, and Zhisheng Zhang. The analysis of transparent dielectric elastomer actuators for lens. Optik, 178:841–845, 2019.

XII. Jianing Wang, Sjoerd WH de Haan, JA Ferreira, and Peter Luerkens. Complete model of parasitic capacitances in a cascade voltage multiplier in the high voltage generator. In 2013 IEEE ECCE Asia Downunder, pages 18–24. IEEE, 2013
XIII. Jinrong Li, Yang Wang, Liwu Liu, Sheng Xu, Yanju Liu, JinsongLeng, and Shengqiang Cai. A biomimetic soft lens controlled by electrooculographic signal. Advanced Functional Materials, page 1903762, 2019.
XIV. Lukas M¨uller and Jonathan W Kimball. High gain dc–dc converter based on the cockcroft–walton multiplier. IEEE Transactions on Power Electronics, 31(9):6405–6415, 2015.
XV. Manxin Chen, Changqing Yin, Poh Chiang Loh, and Adrian Ioinovici. Improved large dc gain converters with low voltage stress on switches based on coupled-inductor and voltage multiplier for renewable energy applications. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2019.
XVI. NA Azmi, RC Ismail, SS Jamuar, SAZ Murad, MNM Isa, WY Lim, and MA Zulkifeli. Design of dc high voltage and low current power supply using cockroft-walton (cw) voltage multiplier. In 2016 3rd International Conference on Electronic Design (ICED), pages 13–17. IEEE, 2016.
XVII. Nor AAzmi, Sohiful AZ Murad, Azizi Harun, and Rizalafande C Ismail. 5v to 6kv dc-dc converter using switching regulator with Cockcroft Walton voltage multiplier for high voltage power supply module. Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering), 12(2):162–171, 2019.
XVIII. Qiao Chen, Bin Zi, Zhi Sun, Yuan Li, and QingsongXu. Design and development of a new cable-driven parallel robot for waist rehabilitation. IEEE/ASME Transactions on Mechatronics, 2019.
XIX. RosaliaMoreddu, Daniele Vigolo, and Ali K Yetisen. Contact lens technology: From fundamentals to applications. Advanced healthcare materials, page 1900368, 2019.
XX. Sohiful Anuar Zainol Murad, Nor Afiqah Azmi, Azizi Harun, and Tun Zainal Azni Zulkifli. A novel 1.6 kv high voltage low current stepup dc-dc converter with cockcroft-walton voltage multiplier for power supply modules. Jurnal Teknologi, 81(5), 2019.
XXI. Stephen J Vincent and Daddi Fadel. Optical considerations for scleral contact lenses: A review. Contact Lens and Anterior Eye, 2019.
XXII. Trace Langdon. Very low power cockcroft-walton voltage multiplier for rf energy harvesting applications. 2019.
XXIII. Yu Qiu, Elric Zhang, Roshan Plamthottam, and Qibing Pei. Dielectric elastomer artificial muscle: Materials innovations and device explorations. Accounts of chemical research, 52(2):316–325, 2019.

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