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Call For Paper is Open for Vol.5 No.2 September 2020, 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.
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  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.

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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.
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  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.
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