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Call For Paper is Open for Vol.6 No.1 March 2021, Please submit paper Vol. 5 No. 1 - (March 2020)




DOI

Paper Title

10.21058/gjecs.2020.51001

Analysis of Components and Circuit for FMCW Radar System

Author Name

Volume No., Issue No., Year, & Page No.

SAQIB AHMED, FAHIM AZIZ UMRANI, AND ABDUL BASSIT SURAHIO

Vol. 5, No. 1, March 2020, pp. 1-11

Abstract:

In this paper, the components required for FMCW (Frequency-Modulated Continuous Wave) RADAR (Radio Detection and Ranging) system operating at 3GHz i.e. modulator circuit and video amplifier circuit are designed and tested. For this purpose, the RF (Radio Frequency) components were selected from microstrip trainer kit MST-523 of Feedback Instruments. The working of each component is analyzed through VNA (Vector Network Analyzer) and spectrum analyzer. The patch antennas provided in the kit were used as transmitter and receiver operating at 3 GHz. The modulator circuit is built with variable frequency and amplitude to modulate the signal from VCO (Voltage Controlled Oscillator). The triangular wave is selected for this purpose to modulate the VCO. The received signal is measured on spectrum analyzer and received power is measured at every integral multiple of wavelength to check working state of antenna and change in power. The Low pass filter circuit is also designed to filter out the higher frequencies after the mixer stage. The RADAR range is calculated based on measurement of the system.

Keywords:

Radar, Frequency Modulated Continuous Wave, Patch Antenna

Full Text:

References:

  1. Umrani, F.A., Chowdhry, B.S., and Umrani, A.W., “Simulation Design of Doppler Filter Bank (DFB) for Pulsed DopplerRadar to Measure Wind Velocities”, Mehran University Research Journal of Engineering & Technology, Volume 29, No. 1, pp. 91-98, Jamshoro, Pakistan, January, 2010.
  2. El-Mokdad, S., Khrayzat, M., and Bazzi, A., “FMCW Implementation on LabVIEW”, International Conference on Computer and Application, 2018.
  3. Hussein, M., Abd-Almageed, W., Ran, Y., and Davis, L., “Real-Time Human Detection, Tracking, and Verification in Uncontrolled Camera Motion Environments”, IEEE 4th International Conference on Computer Vision Systems, pp. 41-41, January, 2006.
  4. Ralston, T.S., Charvat, G.L., and Peabody, J.E., “Real-Time Through-Wall Imaging Using an Ultrawideband Multiple-Input Multiple-Output (MIMO) Phased Array Radar System”, IEEE International Symposium on Phased Array Systems and Technology, pp. 551–558, October, 2010.
  5. Stove, A.G., “Linear FMCW Radar Techniques”, IEEE Proceedings-F, Volume 139, No. 5, pp. 825-830, 1992.
  6. Pyo, G., Kim, C.-Y., and Hong, S., “Single Antenna FMCW Radar CMOS Transceiver IC”, IEEE Transactions on Microwave Theory and Techniques, Volume 2, pp. 305-310, 2016.
  7. Charvat, G.L., Fenn, A.J., and Perry, B.T., "The MIT IAP Radar Course: Build a Small Radar System Capable of Sensing Range, Doppler, and Synthetic Aperture (SAR) Imaging", IEEE Radar Conference, pp. 0138-0144, Atlanta, GA, 2012.
  8. Eid, A.M., “System Simulation of RF Front-End Transceiver for Frequency Modulated Continuous Wave Radar”, International Journal of Computer Application, Volume 75, pp. 16-22, August, 2013.
  9. Gurbuz, S.Z., Ozcan, M.B., Panm, A.B., Demirhan, S., Hayran, Z., Karaduman, M.C., and Seyfioglu, M.S., “Target Detection and Ranging with the 2.4 GHz MTT Coffee Can Radar”, IEEE 22nd Conference on Signal Processing and Communications Applications, pp.1450-1453, 2014.
  10. Başarslan, O., and Yaldız. E., “Implementation of FMCW Radar for Training Applications”, 4th International Conference on Electrical and Electronic Engineering, pp. 304-308, Ankara, 2017.
  11. Icoz, D., "Milimeterwave FMCW Radar Design", Master's Thesis, Middle East Technical University, December, 2009.

DOI

Paper Title

10.21058/gjecs.2020.51002

Mechanism for Ensuring Teacher’s Presence in Classroom Using Deep Learning

Author Name

Volume No., Issue No., Year, & Page No.

RIZWANA MAHAR, AND GHULAM MUSTAFA MEMON

Vol. 5, No. 1, March 2020, 12-18

Abstract

Education is one of the main components for the growth of a state, and there is not much research done in order to improve its quality especially in Pakistan. To improve Education quality the starting point is to boost Teaching Mechanism especially Government Schools where teacher are generally not present in class. Biometrics is not a valid system to use, as Teachers have found ways to tamper this type of attendance system. Using camera raises privacy issues as well as significant man or machine power is required to solve this problem. This paper presents a speaker recognition system applied to the problem of teacher identification in “ghost schools”. The system uses a neural speaker embedding system that maps the audio lectures to a hyperspace where teacher similarity is measured by a cosine distance. We also present a corpus of audio lectures collected from 5 different teachers of 5 different courses. The dataset can be used for the evaluation of teacher identification system and contains audio lectures both for enrollment and testing. Our proposed system achieves an accuracy of 67% on the test set of the above mentioned corpus. We have made our code and corpus publicly available for reproducible research.

Keywords:

Speaker Recognition, Speaker Verification, Teacher Identification.

Full Text:

References:

  1. Niemi-Laitinen, T., Saastamoinen, J., Kinnunen, T., and Fränti, P., “Applying MFCC-Based
  2. Automatic Speaker Recognition to GSM and Forensic Data”, Proceedings of 2nd Baltic Conference on Human Language Technologies, Tallinn, Estonia, pp. 317-322, April, 2005.
  3. Thiruvaran, T., Ambikairajah, E., and Epps, J., “FM Features for Automatic Forensic Speaker Recognition”, Proceedings of 9th Annual Conference of the International Speech Communication Association, 2008.
  4. Prince, S.J., and Elder, J.H., “Probabilistic Linear Discriminant Analysis for Inferences About Identity”, Proceedings of IEEE 11th International Conference on Computer Vision, pp. 1-8, October, 2007.
  5. Matějka, P., Glembek, O., Castaldo, F., Alam, M.J., Plchot, O., Kenny, P., and Černocky, J., “Full-Covariance UBM and Heavy-Tailed PLDA in i-Vector Speaker Verification”, Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 4828-4831, May, 2011.
  6. Cumani, S., Plchot, O., and Laface, P., “Probabilistic Linear Discriminant Analysis of i-Vector Posterior Distributions”, Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 7644-7648, May, 2013.
  7. Ghalehjegh, S.H., and Rose, R.C., “Deep Bottleneck Features for i-Vector Based Text-Independent Speaker Verification”, Proceedings of IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 555-560, December, 2015.
  8. Lei, Y., Scheffer, N., Ferrer, L., and McLaren, M., “A Novel Scheme for Speaker Recognition Using a Phonetically-Aware Deep Neural Network”, Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1695-1699, May, 2014.
  9. Variani, E., Lei, X., McDermott, E., Moreno, I.L., and Gonzalez-Dominguez, J., “Deep Neural Networks for Small Footprint Text-Dependent Speaker Verification”, Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 4052-4056, May, 2014.
  10. Heigold, G., Moreno, I., Bengio, S., and Shazeer, N., “End-to-End Text-Dependent Speaker Verification”, Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 5115-5119, March, 2016.
  11. Snyder, D., Ghahremani, P., Povey, D., Garcia-Romero, D., Carmiel, Y., and Khudanpur, S., “Deep Neural Network-Based Speaker Embeddings for End-to-End Speaker Verification”, Proceedings of IEEE Spoken Language Technology Workshop, pp. 165-170, December, 2016.
  12. Li, C., Ma, X., Jiang, B., Li, X., Zhang, X., Liu, X., and Zhu, Z., “Deep Speaker: An End-to-End Neural Speaker Embedding System”, arXiv Preprint arXiv, [ISN: 1705.02304], 2017.
  13. Schroff, F., Kalenichenko, D., and Philbin, J., “Facenet: A Unified Embedding for Face Recognition and Clustering”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 815-823, 2015.

DOI

Paper Title

10.21058/gjecs.2020.51003

Machine Control Using Hand Gesture Recognition

Author Name

Volume No., Issue No., Year, & Page No.

QURRAT-UL-AIN, 1SHAH NAWAZ TALPUR, NOOR-UZ-ZAMAN LAGHARI, AND MUHAMMAD SAEED RATTAR

Vol. 5, No. 1, March 2020, 19-28

Abstract

Human to machine collaboration is an important game changer in industries as well as in daily lives. Hand gesture recognition assists this interaction by automating the process. This collaboration is supported by proposing system which can recognize human hand gestures and perform tasks based on those gestures. This paper presents the system that uses hand gesture recognition to control machine. The machine executes programmed task according to gestures recognized. The hand gestures are recognized using vision-based approach. The defined gestures are static and carried out by bare hands. The recognition of the system involves hand segmentation, feature extraction, and classification. The hand is segmented using bounding box in webcam preview, and features are extracted by determining signature of hand and histogram of signature. The classification process uses supervised feed forward neural network system to train some samples of different gestures. Once gesture is recognized, the programmed tasks start to execute. The proposed system has found to have an average accuracy of 92.6% for the classification of gestures.

Keywords:

Gesture Recognition, Human Computer Interaction, Hand Detection, Feature Extraction, Neural Networks.

Full Text:

References:

  1. Mitra, S., and Acharya, T., “Gesture Recognition: A Survey”, IEEE Transactionson Systems, Man, and Cybernetics Part-C, Applied Review, Volume 37, No. 3, pp. 311-324, 2007.
  2. Lee, H.B., and Lim, H., “Hand Tracking and Gesture Recognition System for Human-Computer Interaction Using Low-Cost Hardware”, Multimedia Tools and Applications, Volume 74, No. 8, pp.2687-2715, 2013.
  3. Burns, A.M., and Mazzarino, B., “Finger Tracking Methods Using Eyesweb”, Gesture in Human-Computer Interaction and Simulation, pp. 156–167, 2006.
  4. Dias, J.M.S., Nande, P., Barata, N., and Correia, A., “OGRE: Open Gestures Recognition Engine”, IEEE Proceedings on 17th Brazilian Symposium on Computer Graphics and Image Processing, pp. 33-40, 2004.
  5. Han, S.I., Mi, J.Y., Kwon, J.H., Yang, H.K., and Lee, B.G., “Hand Tracking and Gesture Recognition System for Human-Computer Interaction Using Low-Cost Hardware”, Vision Based Hand Tracking for Interaction, 2008.
  6. Hasan M.M., and Mishra, P.K., “Real Time Fingers and Palm Locating Using Dynamic Circle Templates”, International Journal of Computer Applied, Volume 41, No. 6, pp. 33-43, 2012.
  7. Bimber, O., “Continuous 6DOF Gesture Recognition: A Fuzzy Logic Approach”, Proceedings of WSCG’99, Volume 1, pp. 24-30, 1999.
  8. Ren, Y., and Zhang, F., “Hand Gesture Recognition Based on Meb-SVM”, IEE 2nd International Conference on Embedded Software and Systems, Computer Society, Los Alamitos, pp. 344–349, 2009.
  9. Joseph, J., and LaViola Jr., “A Survey of Hand Posture and Gesture Recognition Techniques and Technology”, Master Thesis, Science and Technology, Center for Computer Graphics and Scientific Visualization, USA, 1999.
  10. Shin J.-H., Lee, J.-S., Kil, S.-K., Sehn, D.-F., Ryn, J.-G., Lee, E.-H., Min, H.-K., and Hon, S.-H., “Hand Region Extraction and Gesture Recognition Using Entropy Analysis”, International Journal of Computer Science and Network Security, Volume 6, Issue 2A, February, 2006.
  11. Kim, H., and Fellner D.W., “Interaction with Hand Gesture for a Back-Projection Wall”, Proceedings of Computer Graphics International, pp. 395-402, 19 June, 2004,
  12. Sun, J., Ji, T., Zhang, S., Yang, J., and Ji, G., “Research on the Hand Gesture Recognition Based on Deep Learning. 12th International Symposium on Antennas, Propagation and EM Theory, 2018.
  13. Acharya, R.U., “Advances in Cardiac Signal processing”, Berlin, Springer, 2007.
  14. Agarwal, R., Raman, B., and Mittal, A., “Hand Gesture Recognition Using Discrete Wavelet Transform and Support Vector Machine”, 2nd International Conference on Signal Processing and Integrated Networks, 2015.
  15. Kumar, G., and Bhatia, P., “A Detailed Review of Feature Extraction in Image Processing Systems”, 4th International Conference on Advanced Computing & Communication Technologies, [DOI: 10.1109/ACCT.2014.74], 2014.

DOI

Paper Title

10.21058/gjecs.2020.51004

Implementation of Policy Based Routing in MikroTik

Author Name

Volume No., Issue No., Year, & Page No.

AYUB LAGHARI, NAFEESA BOHRA, AND ABDUL LATIF MEMON

Vol. 5, No. 1, March 2020, 29-36

Abstract

In this paper proper BW (Bandwidth) utilization is predicted using policy making decisions by using MikroTik router. Future network demands, high BW as number of devices are increasing day by day and to ensure this suitable network mechanism is needed with the help of policy making scenario. Implementing policies in firewall refrain the unauthorized access and BW is not being efficiently utilized. The results suggest that MikroTik router provides built-in firewall that is not only user-friendly in policy making but also provide QoS (Quality of Service) and makes an efficient utilization of BW resources. Policy based routing facilitates proper network management. Through policy application, network BW can be used appropriately, misuse of BW can be prevented which in turn can enhance QoS.

Keywords:

MikroTik, Routing, Firewall, Bandwidth, Quality of Service.

Full Text:

References:

  1. Lubis, A., and Siahaan, A.P.U., “WLAN Penetration Examination of the University of Pembangunan Panca Budi”, International Journal of Engineering Trends and Technology, Volume 37, No. 3, pp. 165-168, 2016.
  2. Manesh T, Bhraguram, T.M., Rajaram, R., and Bhadran, V.K., “Network Forensic Investigation of HTTPS Protocol”, International Journal of Modern Engineering Research, Volume 3, No. 5, pp. 3096-3106, 2013.
  3. Ariyanto, H., andSiahaan, A.P.U., “Intrusion Detection System in Network Forensic Analysis and Investigation”, Journal of Computer Engineering, Volume 18, No. 6, pp. 115-121, 2016.
  4. Mollick, P., Biswas, S., Halder, A., and Salmani, M., “Mikrotik Router Configuration Using IPv6”, International Journal of Innovative Research in Computer, Volume 4, No. 2, pp. 2002007, 2016.
  5. Muhammad, D.L.S., Melva, P., and Siahaan, A.P.U., “MikroTik Bandwidth Management to Gain the Users Prosperity Prevalent”, International Journal of Emerging Trends & Technology in Computer Science Volume 42. Pp. 218-222, [DOI: 10.14445/22315381/IJETT-V42P243], 2016.
  6. Abdullah, I.M., “Bandwidth Management in Router for DHCP Protocol”, International Journal of Scientific and Engineering Research, Volume 10, pp. 1343-1346, [DOI: 10.14299/ijser.2019.03.03], 2019.
  7. Jules, T., and Christiane, F., “Failure of Networks and Network Management”, Strategies in Failure Management,[DOI: 10.1007/978-3-319-72757-8_14], June, 2018.
  8. Thato, S., Murtala, A., Rajalakshmi, S., Olefile, P., and Ontiretse, B., “Policy-Based Network Management in BIUST Network”, American Journal of Engineering & Applied Sciences, Volume [DOI: 10.661-668. 10.3844/ajeassp.2017.661.668], 2017.
  9. Lymberopoulos, L., Lupu, E., and Sloman, M., “An Adaptive Policy-Based Framework for Network Services Management”, Journal of Network and Systems Management, Volume 11, No. 277, 2003.
  10. Choudhary, A.R., “Service Intelligence through Agile Information Controls”, Bell Labs Technical Journal, Volume 8, No. 4, pp. 61-70, 2003.
  11. Choudhary, A.R., “Policy-Based Network Management”, Bell Labs Technical Journal, Volume 9, pp. 19-29, [DOI: 10.1002/bltj.20002], 2004.
  12. Strassner, J., Moore, B., Moats, R., and Ellesson, E., “Policy Core LDAP Schema”, Internet Engineering Task Force, Internet Draft, October, 2002.

DOI

Paper Title

10.21058/gjecs.2020.51005

LVCMOS Based Design OF Energy Efficient RAM On FPGA

Author Name

Volume No., Issue No., Year, & Page No.

Shikha Sen, Apurv Rana, Suryansh Dabas

Vol. 5, No. 1, March 2020, 37-46

Abstract

In our proposed work, we are making our RAM to behave energy efficient by reducing the overall power usage by implementing it on FPGA. LVCMOS15 and LVCMOS25 have been used to observe the power reduction , which includes clock power ,logic power, signal power, i/o power, leakage power and Total power. Power reduction of about 61.4% is seen at the capacitance of 500pF by testing the RAM on LVCMOS25 and then in LVCMOS15. Also the leakage power is comparatively much lower at -33 degree Celsius with the capacitance load of 1000pF in LVCMOS25. The FPGA is tested on different temperatures including standard room temperature of 25 degree celsius, -33 degree and 48.9 degree celsius which is environmentally extreme temperature. The results are obtained by using Xilinx ISE 14.7 simulator with verilog hardware description language.

Keywords:

LVCMOS, Energy Efficiency, computer hardware, RAM, FPGA, Capacitance.

Full Text:

References:

  1. Verma, G., Moudgil, A., Garg, K., & Pandey, B. (2015, March). Thermal and power aware Internet of Things enable RAM design on FPGA. In 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 1537-1540). IEEE.
  2. Choi, S., & Prasanna, V. K. (2003, September). Time and energy efficient matrix factorization using fpgas. In International Conference on Field Programmable Logic and Applications (pp. 507-519). Springer, Berlin, Heidelberg.
  3. Saxena, A., Bhatt, A., & Patel, C. (2018, February). SSTL IO Based WLAN Channel Specific Energy Efficient RAM Design for Internet of Thing. In 2018 3rd International Conference On Internet of Things: Smart Innovation and Usages (IoT-SIU) (pp. 1-5). IEEE.
  4. Kalia, K., Pandey, B., & Hussain, D. M. A. (2016, October). SSTL based thermal and power efficient RAM design on 28nm FPGA for spacecraft. In 2016 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE) (pp. 313-317). IEEE.Authors
  5. Sharma, R., Rohilla, L., Garg, R., Sharma, A., Dabbas, S., & Nibber, A. (2016). Frequency Based Energy Efficient Motley RAM Design on 40nm FPGA Technology. International Journal of Control and Automation, 9(8), 89-96.
  6. Bharti, M., Kumari, D., & Verma, P. C. (2019). 28nm FPGA HSTL IO Standard Green RS Flip Flop Design for AI Based Processor.
  7. Ullah, I., Ullah, Z., & Lee, J. A. (2018). EE-TCAM: An Energy-Efficient SRAM-Based TCAM on FPGA. Electronics, 7(9), 186.
  8. Kiat, W. P., Mok, K. M., Lee, W. K., Goh, H. G., & Achar, R. (2020). An energy efficient FPGA partial reconfiguration based micro-architectural technique for IoT applications. Microprocessors and Microsystems, 73, 102966.
  9. Choi, S., Scrofano, R., Prasanna, V. K., & Jang, J. W. (2003, February). Energy-efficient signal processing using FPGAs. In Proceedings of the 2003 ACM/SIGDA eleventh international symposium on Field programmable gate arrays (pp. 225-234).
  10. Choi, S., & Prasanna, V. K. (2003, September). Time and energy efficient matrix factorization using fpgas. In International Conference on Field Programmable Logic and Applications (pp. 507-519). Springer, Berlin, Heidelberg.

DOI

Paper Title

10.21058/gjecs.2020.51006

Automated VS. Manual Testing: A Scenario Based Approach Towards Application Development

Author Name

Volume No., Issue No., Year, & Page No.

Mehwash Rafiqa, Dr. Rehan Ashraf, Haris Abid

Vol. 5, No. 1, March 2020, 47-55

Abstract

In order to produce the quality product, assessment is made during and at the end of Software development process to check whether software is error free and to ensure whether specified requirements of the Software are met. This practice is called Software Testing. In order to cope time and resource constraints of this modern era, now a day new way of testing called automated testing is working against manual testing. In automated testing, pre-scripted tests are executed by software tool. A software tool is used to test or check software execution. Actions are pre-recorded and predefined then playback is performed through an automated testing tool, comparison of the results with the expected behavior is made and the success or failure is reported. There exists different mode of testing like unit testing, integration testing and functional testing and each one works in different perspective. Not all types of testing can be automated but a few can be. In this paper various types of testing that can be automated are discussed and how they work in different scenario. The way in which different automated testing tools perform these testing are discussed in this paper.

Keywords:

Automated Testing, Types Of Automated Testing, How Automated Testing.

Full Text:

References:

  1. Abha et al.,.“A Comparison of RANOREX and QTP Automated Testing Tools and their impact on Software Testing”. International Journal of Engineering, Management, Sciences, Vol. 1, No. 1, 2014.
  2. Andrea et al.,. “Generating TCP/UDP network data for automated unti test generation”. ESEC/FSE 2015 proceedings of the 2015 10th joint meeting on foundations, ISBN:978-1-4503-3675-8, 2015, pp 155-165.
  3. Chirstopher and Hyunsook “Model-Based Exploratory Testing: A Controlled Experiment “. 2014 IEEE 7th international conference on software testing, ISBN 978-1-4799-5790-3., Acc No. 14363377, 2014.
  4. Earl et al.,. “The oracle problems in Software testing”. IEEE transaction on software engineering, Vol. 41, No. 5, 2015, pp 507-525.
  5. Kan et al.,. “A method of minimum reusability estimation for automated software testing”. Journal of shanghai jiaotong University science, Vol. 18, No. 3, 2013, pp 360-365.
  6. Rod et al.,. “The The Writing Pal Intelligent Tutoring System: Usability Testing and Development“, Computers and composition, Vol. 34, 2014, pp 39-59.
  7. Sebastian et al.,. “Techniques for improving regression testing in continuous integration development environment”. FSE 2014proceedings of the 22nd ACM SIGSOFT international symposium on foundations of software engineering, ISBN:978-1-4503-3056-5, pp 235-245.
  8. Vahid and Mika, “When and what to automate in software testing”. Information and software technology, Vol. 76, 2016, pp 92-117.
  9. Yuhua et al.,.“The Efficient Automated Program Repair through Fault-Recorded Testing Prioritization”. 2013 IEEE
  10. international conference on software maintenance, ISBN:1063-6773, pp 180-189. s

DOI

Paper Title

10.21058/gjecs.2020.51007

Owasp Approach to Secure Authentication of Online Child Registration

Author Name

Volume No., Issue No., Year, & Page No.

Danilo Alberto Chávez Espíritu

Vol. 5, No. 1, March 2020, pp. 56-75

Abstract:

The objective of this research is to securely authenticate online registration of minors in the Nominee Register as a contribution to reducing the social gap in early childhood, considering that the information provided by RENIEC is information that must have high levels of security, which however should not be an obstacle to access from any place more distant places where the Government should have a presence, in this sense the OWASP (Open Web Application Security Project), open project, which is referenced by many standards of security, as a result of this implementation it was possible to securely authenticate online registration, significantly reducing vulnerabilities in all application phases, which has enabled strategic alliances with MEF and MINSA to bring technology safely to the different parts of Peru where there was little presence of the State which has allowed in the Nominal Register with the registration of children from early childhood to reduce the social gap in relation to inclusion.

Keywords:

OWASP, Web technology, Nominal Register, Security, Social gap

Full Text:

References:

  1. Avella Villamil, A. M. (2015). ¿QUE EFICACIA HA TENIDO LA POLÍTICA PÚBLICA COLOMBIANA DE PRIMERA INFANCIA "DE CERO A SIEMPRE"? Tesis, UNIVERSIDAD MILITAR NUEVA GRANADA, RELACIONES INTERNACIONALES Y ESTUDIOS POLÍTICOS, Bogotá. Colombia.
  2. Corona, I. (2010). Deteccion de ataques a la Web. Universidad de Cagliari, Departamento de Ingenieria Electrica y Electronica. Cagliari: Universidad de Cagliari.
  3. Dominguez, L. A. (2012). Análisis de Sistemas de Información (Primera ed.). (R. T. MILENIO, Ed.) Tlalnepantla: RED TERCER MILENIO. doi:978-607-733-105-6
  4. Klooster, K. (2016). Applying a Security Testing Methodology: a Case Study. UNIVERSITY OF TARTU, Institute of Computer Science. Tartu: UNIVERSITY OF TARTU.
  5. Lay Lisboa, S. L. (2015). LA PARTICIPACIÓN DE LA INFANCIA DESDE LA INFANCIA La Construcción de la Participación Infantil a Partir del Análisis de los Discursos de Niños y Niñas. Tesis Doctoral, Universidad de Universidad de Valladolid, Facultad de Educación y Trabajo Social Departamento de Pedagogía, Segovia, España.
  6. MINSA. (2018). Ministerio de Salud. Recuperado el 14 de Enero de 2018, de http://www.minsa.gob.pe/portalweb/02estadistica/estadistica_26.asp
  7. Ñique Morazzani, V. A. (2016). IMPLEMENTACIÓN DE SOLUCIÓN DE AUTENTICACIÓN SEGURA BASADA EN DOBLE FACTOR EN UNA ENTIDAD DEL ESTADO. Tesis, Universidad San Ignacio de Loyola, FACULTAD DE INGENIERÍA, Carrera de Ingeniería Informática y de Sistemas, Lima, Perú.
  8. OWASP. (s.f.). Recuperado el 14 de Enero de 2018, de https://www.owasp.org/index.php/Proyectos_OWASP
  9. Pérez Capdevila, J. (2018). Tecnoweb2. Recuperado el 14 de Enero de 2018, de http://tecnoweb2.com/tecnologias-web
  10. Pérez Porto, J. (2016). Definicion. Recuperado el 14 de Enero de 2018, de https://definicion.de/brecha-social/
  11. Scandroglio, B., López, J., & San José, C. (2008). La Teoría de la Identidad Social: una síntesis crítica de sus fundamentos, evidencias y controversias. Paper, Universidad Autónoma de Madrid, Madrid. Obtenido de http://www.psicothema.es/pdf/3432.pdf
  12. Singh Bisht, P. P. (2011). Improving Web Security by Automated Extraction of Web Application Intent. University of Illinois, Computer Science. Chicago: University of Illinois.
  13. Thornberry, G. (2015). PALESTRA PORTAL DE ASUNTOS PÚBLICOS DE LA PUCP. (PUCP, Ed.) Recuperado el 14 de Enero de 2018, de http://repositorio.pucp.edu.pe/index/bitstream/handle/123456789/11954/quien_soy_yo_Thornberry.pdf?sequence=1
  14. Thulin, P. (2015). Evaluation of the applicability of security testing. Linköpings universitet, Department of Computer and Information Science. Linköping: Institutionen för datavetenskap.
  15. Urquizo Limache, G. R. (2016). La capacitación de los registradores civiles impartida por el Registro Nacional de Identificación y Estado Civil (RENIEC) y su eficiencia en la función registral. Tesis de magister en Gerencia Social, PONTIFICIA UNIVERSIDAD CATÓLICA DEL PERÚ, ESCUELA DE POSGRADO, Lima, Perú.

DOI

Paper Title

10.21058/gjecs.2020.51008

Digital Threat Detection Model to Mitigate Cybersecurity Risks in Organizations

Author Name

Volume No., Issue No., Year, & Page No.

Felix Eloy Jimenez Chuque

Vol. 5, No. 1, March 2020, pp. 76-90

Abstract:

Cybersecurity is a fundamental aspect of networks, computers, software, and data. The objective is to establish a Digital Threat Detection Model to mitigate cybersecurity risks in organizations by evaluating the influence of cybersecurity infrastructure on the global cybersecurity index. Without sufficient security, these assets are vulnerable to malicious threats (Xiong & Lagerström, 2019). The technological platforms located in the data centers of the organization itself or through service providers locally or in the cloud make available communications services, databases, application servers, and managed security services, among others. Multiple times we find that the published services are compromised by different types of digital threat that compromises the operational continuity of the organization or even its reputation. Digital services must be permanently attended even more if we have users who permanently consult the services offered on the platforms. Every service published on the Internet is always attacked by different forms or digital variants that violate or paralyze the applications that serve the public. In this sense, it is important to understand ways and ways to identify a digital threat; the criteria used to differentiate correct traffic from another that violates or intends to violate our platforms, as well as knowing how our digital services are organized, the technological infrastructure, both network architecture, and security.

Keywords:

Cybersecurity, technology platforms, data centers, service providers, threats

Full Text:

References:

  1. Avgerou, A., & Stamatiou, Y. (2015). Privacy awareness diffusion in social networks. IEEE Security and Privacy, 13(6), 44-50.
  2. Brotby, K., & Hinson, G. (2013). PRAGMATIC Security Metrics: Applying Metametrics to Information Security. Auerbach Publications.
  3. C. Bronk. (2016). Cyber threat: the rise of information geopolitics in U.S. national security. ABC-CLIO.
  4. Collaborative Cyber Threat Intelligence. (2017). Collaborative Cyber Threat Intelligence.
  5. Dawson, M., & Omar, M. (2015). New threats and countermeasures in digital crime and cyber terrorism. IGI Global.
  6. Dube, R. (2008). Hardware-Based Computer Security Techniques to Defeat Hackers: From Hackers:
  7. Khajuria, S., Sørensen, L., & Skouby, K. (2017). Cybersecurity and Privacy - Bridging the Gap.
  8. Kohnke, A. (2017). Implementing Cybersecurity. Auerbach Publications.
  9. Kulesza, J., & Balleste, R. (2015). Cybersecurity and Human Rights in the Age of Cyberveillance.
  10. Nelson, B., Philips, A., & Steuart, C. (2016). Guide To Computer Forensics and Investigation:
  11. Sha, K., Striegel, A., & Song, M. (2016). Security, Privacy and Reliability in Computer Communications and Networks.
  12. Tripathy, B., & Baktha, K. (2018). Security, Privacy, and Anonymization in Social Networks: Emerging Research and Opportunities. IGI Global.

Vol. 5 No. 2 - (September 2020)

DOI

Paper Title

10.21058/gjecs.2020.52001

Design and Analysis of Efficient Electromechanical Braking System (EBS)

Author Name

Volume No., Issue No., Year, & Page No.

Atif Saeed, Hashim Soomro, Zain Haider Khan, Mufaddal Arif, Ali Raza

Vol. 5, No. 2, September 2020, pp. 1-10

Abstract:

Using electromagnetic induction, the electromagnetic brake slowly keeping slow the moving vehicle under which it will produce resistance. The Friction brakes create a pressure on to the two separate objects to reduce the vehicle speed in a Controlled mode. The magnet current turns in the form of plate heat which definitely lowers the kinetic energy. In this magnetic type of braking device, as the driver applies force on the brake pedal, a pulsating D.C. current is sent to the power pack. As per the driver’s requirement a proportionate torque is produced to decelerate the vehicle. In oil braking or air braking systems even a small leakage can result in complete brake failure and cause accidents In previous braking system we use oil which is a natural element and it has many other usages so if we exchange our hydraulic braking system with electromagnetic braking system so it may help us in economic growth and it is a clean and affordable energy.

Keywords:

Friction brakes, Electromagnetic induction, V-belt, Electromagnetic brake

Full Text:

References:

  1. H. Heisler, “Brake system,” in Advanced Vehicle Technology, 2002.
  2. A. Saeed, S. Sattar, F. Mumtaz, and M. A. Khan, “International Journal of Multidisciplinary Studies and Innovative Technologies Step towards sustainable energy: practices and approaches.”
  3. B. Liu, W. H. Li, P. B. Kosasih, and X. Z. Zhang, “Development of an MR-brake-based haptic device,” Smart Mater. Struct., 2006.
  4. Muhammad Atif Saeed; Imran Amin; Farhan Mumtaz, “Energy management using wireless technologies: A comprehensive study,” in 9th International Renewable Energy Congress (IREC), 2018.
  5. K. Maheswaran, V. Jebaraj, and T. Prabaharan, “Emergency Braking system for Electrical Overhead Travelling Crane,” Int. J. Recent TRENDS Eng. Res., 2019.
  6. Y. Yasa, E. Sincar, B. T. Ertugrul, and E. Mese, “A multidisciplinary design approach for electromagnetic brakes,” Electr. Power Syst. Res., 2016.
  7. A. S. Puttewar, N. U. Kakde, H. A. Fidvi, and B. Nandeshwar, “Enhancement of Braking System in Automobile Using Electromagnetic Braking,” IOSR J. Mech. Civ. Eng., 2014.

DOI

Paper Title

10.21058/gjecs.2020.52002

Technology for developing educational films on medicine using highly realistic computer graphics and animation

Author Name

Volume No., Issue No., Year, & Page No.

Vladimir M. Ivanov, Mikhail E. Cherkasov, Nikolai V. Kalakutskiy

Vol. 5, No. 2, September 2020, pp. 11-18

Abstract:

The article discusses a project to create educational films in the field of medicine, using 3D technologies. Possible technologies, the impact of audiovisual content on the viewer, and technical features of the production of educational films are highlighted.

Keywords:

medicine, surgery, maxillofacial surgery, education, educational film, 3D graphics, visualization, public relations, distance education.

Full Text:

References:

  1. N. I. Miron. Psychological preparation of the surgeon for surgery. Current issues of teaching in higher education: theory and practice, pages 29–31. State University of the Altai Republic of Russia (2015)
  2. N. Hoffman. Physics and Math of Shading. Physically Based Shading in Theory and Practice. SIGGRAPH (2014) Aug 10; Vancouver, Canada
  3. Manjunatha SN, V. Darshan, and S. Jayashree. Health Related Advertisements in Audiovisual Media: A Content Analysis in Karnataka, India. International Journal of Medicine and Public Health 9, 2, 50–54 (2019)
  4. Monika Hestad, Branding and Product Design: An Integrated Perspective, New York, USA (2016)
  5. Theodore M. Porter, Trust in Numbers: The Pursuit of Objectivity in Science and Public Life, Princeton, UK (1995)
  6. Mario Russo, Polygonal Modeling: Basic and Advanced Techniques, Plano, USA (2006)

DOI

Paper Title

10.21058/gjecs.2020.52003

Neural Network Based Altitude-Hold Autopilot Control of an Aircraft

Author Name

Volume No., Issue No., Year, & Page No.

YUSRA BANO, MUKHTIAR ALI UNAR, AND ZEESHAN ALI

Vol. 5, No. 2, September 2020, pp. 19-34

Abstract:

Since the nature of mathematical model of an aircraft is extremely nonlinear and controlling the state variables have always been highly challenging tasks for the researchers, therefore, this paper presents an ANN (Artificial Neural Network) based control architecture to control the longitudinal dynamics of an aircraft by controlling the longitudinal motion of the aircraft. The proposed controller serves as an altitude-hold autopilot system which holds the aircraft at a desired altitude as assistance to the pilot. Using sigmoid activation function with multilayer network and back-propagation supervised learning; the NN (Neural Network) plant model is trained offline. The control algorithm is based on NN model predictive control architecture where the controller first predicts the future plant behavior for the given range of control inputs using NN plant model and then using optimization algorithm a set of potential control inputs are computed to optimize the future plant responses. The proposed model is tested for 500 and 1000 ft step inputs and a pulse input ranging from 0-1000 ft. The simulation results show that the model is working under altitude-hold autopilot mode smoothly without any overshoot and steady-state error.

Keywords:

Boeing 747, Altitude-Hold Autopilot, Longitudinal Control, Artificial Neural Network, Back-Propagation.

Full Text:

References:

  1. Nelson, R.C., “Flight Stability and Automatic Control”, 2nd Edition, McGraw Hill, 1998.
  2. Dmitriev, V.G., "The Flying-Wing Concept-Chances and Risks", AIAA International Air and Space Symposium and Exposition, Dayton, Ohio, July 2003.
  3. Yadav, A.K., and Gaur, P., “AI-Based Adaptive Control and Design of Autopilot System for Nonlinear UAVs”, SADHANA, Official Journal of the Indian Academy of Sciences, Volume 39, Issue 4, pp. 765–783, August, 2014.
  4. Nurbaiti, W., and Nurhaffizah, H., "Self-Tuning Fuzzy PID Controller Design for Aircraft Pitch Control", IEEE Explore 3rd International Conference on Intelligent systems, Modelling and Simulation, 2012.
  5. Chao, W., "Aircraft Autopilot Design Using a Sampled-Data Gain Scheduling Techniques", M.Sc. Thesis, School of Electrical Engineering and Computer Science & College of Engineering and Technology, Ohio University, March, 1999.
  6. Xin, C., Yi-Dong, Y., and Min, Z., "Study of an Intelligent PID Attitude Controller for UAV", Journal of Nanjing University of Aeronautics and Astronautics, Volume 6, pp., 33-35, 2003.
  7. Zhu, Y., "Longitudinal Control Laws Design for a Flying Wing Aircraft", M.Sc. Thesis, Cranfield University School of Engineering, February, 2012.
  8. Kuen, J., and Ma, D.-M., “An Autopilot Design for the Longitudinal Dynamics of a Low Speed Experimental UAV Using Two-Time-Scale Cascade Decomposition”, Department of Aerospace Engineering, Tamkang University, Tamsui, Taiwan, R.O.C.
  9. Das, A., Das, R., Mukhopadhyay, S., and Patra, A., “Sliding Mode Controller along with Feedback Linearization for a Nonlinear Missile Model”, IEEE Explore 1st International Symposium on Systems and Control in Aerospace and Astronautics, 2006.
  10. Franklin, G.F., Powell, J.D., and Emami-Naeini, A., “Feedback Control of Dynamic Systems”, 6th Edition, Prentice Hall, 2010.
  11. Hagan, M.T., Demuth, H.B., and De-Jesus, O., “An Introduction to the Use of Neural Networks in Control Systems”, International Journal of Robust Nonlinear Control, Volume 12, pp. 959-985, 2002.
  12. Furqan, I.J., Malik, A.N., and Haider, W., “Neural Network Based Aircraft Control”, Proceedings of IEEE Students Conference on Research and Development, Patrajaya, Malaysia, 13-14 December, 2010.
  13. Kim, S., and Horspool, K.R., “Aircraft Speed/Altitude Control Using a Sigma-Pi Neural Network”, American Institute of Aeronautics and Astronautics, SciTech Forum, Orlando, FL, 6-10 January, 2020.
  14. Beale, M.H., Hagan, M.T., and Demuth, H.B., “Neural Network ToolboxTM 7 User’s Guide, The MathWorks, 2010.
  15. Kumar, Y.V.P., Kiran, K.M.N.S., Yughdhar, S., and Raju, K.P., “Online Attitude Controlling of Longitudinal Autopilot for General Aviation Aircraft using Artificial Neural Networks” Nirma University International Conference on Engineering, Ahmedabad, India 28-30 November, 2013.
  16. Narendra, K.S., and Mukhopadhyay, S., “Adaptive Control Using Neural Networks and Approximate Models”, IEEE Transactions on Neural Networks, Volume 8, pp. 475–485, 1997.
  17. Narendra K.S., and Parthasarathy, K., “Identification and Control of Dynamical Systems Using Neural Networks”, IEEE Transactions on Neural Networks, Volume 1, pp. 4–27, 1990.
  18. Camacho, E.F., and Bordons, C., “Model Predictive Control”, Springer, London, 1998.

DOI

Paper Title

10.21058/gjecs.2020.52004

Application of Global Period Estimation in Periodic Signals for getting high performance during Robot Synchronization

Author Name

Volume No., Issue No., Year, & Page No.

Aiza Batool, Shabeer Ahmad

Vol. 5, No. 2, September 2020, pp. 35-42

Abstract:

In this paper, we have studied and simulated the general synchronization of control problems for both multiple inputs and multiple outputs. We have also addressed the nonlinear parameterizable uncertainties and get our results in asymptomatic convergence. We have proposed a new global identifier and get better results than previous research mentioned in literature. We have simulated the robot synchronization with our proposed period estimator. Our proposed global estimator is giving better results than the local period estimator.

Keywords:

Global Period; Periodic Signals; Robot Synchronization

Full Text:

References:

  1. Del Vecchio, D., Marino, R., & Tomei, P. (2002). Adaptive state feedback control by orthogonal approximation functions. International Journal of Adaptive Control and Signal Processing, 16(9), 635–652. https://doi.org/10.1002/acs.709
  2. Krstic, M., Kokotovic, P. V., & Kanellakopoulos, I. (1995). Nonlinear and adaptive control design. John Wiley & Sons, Inc..
  3. Naidu, D. S. (1998). Book review: Nonlinear Control Design: Geometric, Adaptive and Robust, R. Marino and P. Tomei, Prentice Hall International (UK) Limited, London, 1995, xiii+ 396 pp., ISBN 0‐13‐342635‐1. International Journal of Robust and Nonlinear Control, 8(4‐5), 459-460.
  4. Marino, R., & Verrelli, C. M. (2009). A global state feedback output regulating control for uncertain systems in strict feedback form. Systems & Control Letters, 58(9), 682–690. https://doi.org/10.1016/j.sysconle.2009.06.005
  5. Barron, A. R. (1993). Universal approximation bounds for superpositions of a sigmoidal function. IEEE Transactions on Information theory, 39(3), 930-945.
  6. Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of control, signals and systems, 2(4), 303-314.
  7. Moore, K. L. (2012). Iterative learning control for deterministic systems. Springer Science & Business Media.
  8. Arimoto, S., Kawamura, S., & Miyazaki, F. (1984, December). Bettering operation of dynamic systems by learning: A new control theory for servomechanism or mechatronics systems. In The 23rd IEEE Conference on Decision and Control (pp. 1064-1069). IEEE.
  9. Kawamura, S. (1984). Iterative learning control for robotic systems. Proc. of IECON'84, Tokyo, 393-398.
  10. Musavi, S. H. A., Chowdhry, B. S., Kumar, T., Pandey, B., & Kumar, W. (2015). IoTs enable active contour modeling based energy efficient and thermal aware object tracking on FPGA. Wireless Personal Communications, 85(2), 529-543.
  11. Dixon, W. E., Zergeroglu, E., Dawson, D. M., & Costic, B. T. (2002). Repetitive learning control: a Lyapunov-based approach. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 32(4), 538-545.
  12. Verrelli, C. M. (2011). Establishing improved convergence properties for the adaptive learning control. Automatica, 47(4), 865-867
  13. Verrelli, C. M. (2015). Repetitive learning control design and period uncertainties. Asian Journal of Control, 17(6), 2417-2426.
  14. Tomei, P., & Verrelli, C. M. (2016). Novel algorithms for the synchronization control of nonlinear systems. International Journal of Adaptive Control and Signal Processing, 30(4), 608-633.