Archive

Abstract:

The aim of the article is to develop a system, which uses microcontroller as central part to control the system and monitor the electrical parameters of power generator. In this project microcontroller take the value of frequency and voltage from Analogue to digital converter (ADC) which is interfaced with Potential Transformer(P.T). For counting the frequency, the output of operational amplifier will be measured through microcontroller, in case the value of current shows any abnormal behavior the system will first display the load cut off reason on LCD and then cut off the load for the sake of protection through relay. Another feature of this project is the Auto transfer switch (ATS). If the power from utility companies is available then the generator will be in off state and whenever electrical power from the utility company is suspended, the ATS system will automatically turn on the generator and transfer the load to generator and as the power will be restored from utility company the generator will be automatically turn off and the load will be transferred to the mains line.

Keywords:

Power generator,Protection system, Auto transfer switch,Potential Transformer,Current Transformer,LCD,Analogue to digital converter,Water and Power Development Authority (WAPDA),

Refference:

I.G. Huang, M. Ramesh, T. Berg, and E. Learned-Miller. Labeled faces in the wild: A database for studying face recognitionin unconstrained environments. University of Massachusetts,Amherst, Technical Report 07-49, 2007.

II.Muhammad ZulqarnainAbbasi, M. Aamir Aman, Hamza Umar Afridi, Akhtar Khan. Electrical Engineering Department, IQRA National University, Peshawar, Pakistan.“Sag-Tension Analysis of AAAC Overhead Transmission lines for Hilly Areas” International Journal of Computer Science andInformation Security (IJCSIS), Vol. 16, No. 4, April 2018.

III.Muhammad Aamir Aman, Muhammad Zulqarnain Abbasi, Murad Ali, Akhtar Khan.Department of Electrical Engineering, IQRA National University, Peshawar, Pakistan. To Negate the influences of Un-deterministic Dispersed Generation on Interconnection to the Distributed System considering Power Losses of the system. J.Mech.Cont.& Math. Sci., Vol. -13, No. -3, July -August (2018) Pages 117 –132 .

IV.Muhammad Aamir Aman, Muhammad Zulqarnain Abbasi, HamzaUmar Afridi, Mehr-e-Munir, Jehanzeb Khan.Department of Electrical Engineering, IQRA National University, Peshawar, Pakistan.Photovoltaic (PV) System Feasibility for Urmar Payan a Rural Cell Sites in Pakistan. J.Mech.Cont.& Math. Sci., Vol. -13, No. -3, July -August (2018) Pages 173 –179.

V.P. Hua, G. Viola and S. Drucker. Face recognition using discriminativelytrained orthogonal rank one tensor projections.In Proc. CVPR, 2007.

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Abstract:

Pounding refers to the collision of adjacent structures during strong ground motions. Actually when an earthquake occurs, the structures which are having different dynamic properties vibrate out of phase and collide with each other resulting in heavy damage of life and property. The main reason behind pounding between structures is the insufficient separation distance provided between the buildings. Different countries having different codes provide different rules and regulations regarding the separation distance that needs to be given between the structures to prevent pounding but the insufficient land area and high land prices especially in metropolitan cities, the separation distance is bound to be given very less in order to have effective use of land area during construction. So, in order to prevent the structures from colliding with each other, some cost effective mitigation measures like RCC cross bracings and RC shear wall have been discussed in this study. SAP2000 v19 software has been used for modelling and analysis of the structures. This study includes two types of frame structures i.e. (i) bare frame structure and (ii) structure having infill walls in the form of diagonal strut in it. The mitigation methods such as use of shear wall and bracings proved to be effective in all the cases. Also the best location of bracings and shear wall has been studied by placing them at various locations in the structures and observe the amount of displacement that is being reduced.

Keywords:

Pounding,diagonal strut,SAP2000 software,RCC Bracings,Shear wall,

Refference:

I.Abhina NK and Neeraja Nair (2016), “Evaluation of Seismic Pounding between Adjacent RC Building,” IJIRST, Vol. 3, no. 4, pp. 138–147.

II. Abbas Moustafa and Sayed Mahmoud (2014), ” Damage assessment of adjacent buildings under earhquake loads”, Engineering Structures, Vol. 61, pp.153-165.

III. Chenna Rajaram and Ramancharla Pradeep Kumar (2012), “Comparison of codal provisions on pounding between adjacent buildings”, IJESE, Vol. 05, pp. 72-82.

IV. Chetan J. Chitte, Anand . Jadhav and Hemraj R. Kumavat (2014), “Seismic pounding between adjacent building structures subjected to near field ground motion”, IJRET, Vol. 03, special issue 09.

V.C. GL and R. P. Dhakal(2012), “Turner FM.Building pounding damage observed in the 2011 Christchurch earthquake”, Earthquake Engineering and Structural Dynamics, Vol. 41, no. 5, pp. 893–913.

VI.Hemant B. Kaushik, Durgesh C. Rai and Sudhir K. Jain (2007), “Stress-strain characteristics of clay brick masonry under uniaxial compression”, JMCE © ASCE, Vol. 19, no. 9, pp. 728-739.

VII.K.V. Spiliopoulus and S.A. Anagnostopoulus (1992), “Earthquake induced pounding in adjacent buildings”, 10thWCEE, ISBN: 9054100605.

VIII. M. Bhavan, 03 February 2016 1 All Members of the Civil Engineering Division Council , CEDC 2 All Members of the Earthquake Engineering Sectional Committee , CED 39 and its sub-committees and Panels , CED 39 : 4 , CED 39 : 10 , CED 39 : 4 / P-1 an,” 2016

IX. Pradeep Karanth, Ravindranatha, Shivananda S.M, Suresh H.L (2016), “Pounding effect in building”, IJIRS, Vol. 5, Special issue 9.

X. Panayiotis C. Polycarpou, Petros Komodromus and Anastasis C. Polycarpou (2013), “A nonlinear impact model for simulating the use of rubber shock absorbers for mitigating the effects of structural pounding during earthquakes”, Earthquake Engineering and Structural Dynamics, Vol. 42, pp. 81-100.

XI. Puneet Kumar MS, S Karuna (2015), “Effect of sismic pounding between adjacent buildings amd mitigation measures”, IJRET, Vol. 04, no. 07, pp. 208-216.

XII. Ravindranatha, Tauseef M Honnyal, Shivananda S.M, H Suresh (2014), “A study of seismic pounding between adjacent buildings,” IJRET, Vol. 2002, pp. 795–799.

XIII. Stavros A. Anagnostopoulus and Konstantinos V. Spiliopoulus (1996), “Measures against earthqauke pounding between adjacent buildings”, 11thWCEE, ISBN: 0080428223.

XIV. Stavros A. Anagtostopoulus (1988), “Pounding of buildings in series during earthquakes”, Earthquake Engineering and Structural Dynamics, Vol. 16, pp. 443-456.

 

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Abstract:

The important facts that causes the shortfall in the supply of electrical energy in Pakistan is discussed in this research work. The basic causes due to which the decline in supply and a review about the energy potential in Pakistan were analyzed. It is also investigated that how much important is to utilize the renewable energy and how it will be useful to tackle the shortfall. The solution for that problem is given i.e. to construct small hydro- electric power station on the run of river. This paper will be very helpful for minimizing the shortfall of electricity in Pakistan. To tackle the energy crisis different solutions were given, that is further divided into three terms. Short term solution, Medium term solution, and Long term solution .In short term solution ,the line losses will be reduced, and Power generating capacity will be improved. In medium term solution, the renewable energy resources will be installed. And in long term solution, the thermal power fuel, the myth of Thar coal, stand-alone power projects will be replaced and also the national grid will be dismantled to overcome these crisis.

Keywords:

Power generating capacity,Energy Crisis,Supply and demand,Renewable Energy,Energy Sources,

Refference:

I. Five steps to solving Pakistan’s energy crisis–The Express Tribune Blog, By
Adnan Khalid Rasool Published: March 3, 2012
II. Muhammad Zulqarnain Abbasi, M. Aamir Aman, Hamza Umar Afridi, Akhtar
Khan. Electrical Engineering Department, IQRA National University,
Peshawar, Pakistan.“Sag-Tension Analysis of AAAC Overhead Transmission
lines for Hilly Areas” International Journal of Computer Science and
Information Security (IJCSIS), Vol. 16, No. 4, April 2018.
III. National Transmission and despatch company, Power System Statistics,2016-
2017
IV. Pakistan Energy Year Book, (2017)
V. US Department of Energy 2002
VI. World Bank report 2017
VII. WAPDA Annual Report 2016-17, Water and Power Development Authority
Pakistan. Department of energy, office of energy efficiency and Renewable
Energy Geothermal Energy Program.

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Abstract:

Intra domain supervised classification of online reviews is vastly analysed by current studies. At the same time, the level of performance declines when training is performed with one domain and testing with reviews of a different domain. The main fact behind this reduction is the domain distribution difference and the feature vector difference. Also the semantic of each word in a corpus differs based on its usage in domains. The objective of this study is to propose a new similarity based feature weighting technique for text reviews for enhancing the accuracy of inter domain classification. Different training and testing domains are weighted by proposed probability based statistical techniques for the classification by Support Vector Machine (SVM) and Transductive Support Vector Machine (TSVM). TSVM performs much better for this cross domain classification. The fact behind the performance of TSVM is its Transductive learning even with the small training set. The correlation between source and target domain and its influence on classification accuracy are analysed in detail using the outcome of existing feature weighting and proposed weighting techniques.

Keywords:

Text processing,Feature weighting, Transductive Support Vector Machine,Cross domain classification,

Refference:

I.Andreevskaia, A., & Bergler, S. (2008). When specialists and generalists work together: Overcoming domain dependence in sentiment tagging.Proceedings of ACL-08: HLT, 290-298.

II.Blitzer, J., Dredze, M., & Pereira, F. (2007). Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. InProceedings of the 45th annual meeting of the association of computational linguistics(pp. 440-447).

III.Bollegala, D., Weir, D., & Carroll, J. (2011, June). Using multiple sources to construct a sentiment sensitive thesaurus for cross-domain sentiment classification. InProceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1(pp. 132-141). Association for Computational Linguistics.

IV.Bollegala, D., Weir, D., & Carroll, J. (2013).Cross-domain sentiment classification using a sentiment sensitive thesaurus.IEEE transactions on knowledge and data engineering,25(8), 1719-1731.

V.Brindha, G. R., Swaminathan, P., & Santhi, B. (2016). Performance analysis of new word weighting procedures for opinion mining.Frontiers of Information Technology & Electronic Engineering,17(11), 1186-1198.

VI.Chenlo, J. M., Hogenboom, A., & Losada, D. E. (2014). Rhetorical structure theory for polarity estimation: An experimental study.Data & Knowledge Engineering,94, 135-147.

VII.Deng, Z. H., Luo, K. H., & Yu, H. L. (2014). A study of supervised term weighting scheme for sentiment analysis.Expert Systems with Applications,41(7), 3506-3513.

VIII.Gao, S., & Li, H. (2011, October). A cross-domain adaptation method for sentiment classification using probabilistic latent analysis. InProceedings of the 20th ACM international conference on Information and knowledge management(pp. 1047-1052). ACM.

IX.He, Y., Lin, C., & Alani, H. (2011, June). Automatically extracting polarity-bearing topics for cross-domain sentiment classification. InProceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1(pp. 123-131). Association for Computational Linguistics.

X.Jiang, J., & Zhai, C. (2007, November). A two-stage approach to domain adaptation for statistical classifiers. InProceedings of the sixteenth ACM conference on Conference on information and knowledge management(pp. 401-410). ACM.

XI.Manning, C.,Raghavan, P.,andSchütze,H.(2008) Introduction to Information Retrieval, Cambridge University Press, ISBN:0521865719

XII.Pan, S. J., Ni, X., Sun, J. T., Yang, Q., & Chen, Z. (2010, April). Cross-domain sentiment classification via spectral feature alignment. InProceedings of the 19th international conference on World wide web(pp. 751-760). ACM.

XIII.Pang, B., Lee, L., & Vaithyanathan, S. (2002, July). Thumbs up?: sentiment classification using machine learning techniques. InProceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10(pp. 79-86). Association for Computational Linguistics.

XIV.Raaijmakers, S., & Kraaij, W. (2010, January). Classifier calibration for multi-domain sentiment classification. InICWSM.

XV.Tan, S., Wu, G., Tang, H., & Cheng, X. (2007, November). A novel scheme for domain-transfer problem in the context of sentiment analysis. InProceedings of the sixteenth ACM conference on Conference on information and knowledge management(pp. 979-982). ACM.

XVI.Van de Camp, M., & Van den Bosch, A. (2012). The socialist network.Decision Support Systems,53(4), 761-769.

XVII.Vapnik, V. (2013).The nature of statistical learning theory. Springer science & business media.

XVIII.Wang, B. K., Huang, Y. F., Yang, W. X., & Li, X. (2012). Short text classification based on strong feature thesaurus.Journal of Zhejiang University SCIENCE C,13(9), 649-659.

XIX.Wei, C. P., Lin, Y. T., & Yang, C. C. (2011). Cross-lingual text categorization: Conquering language boundaries in globalized environments.Information Processing & Management,47(5), 786-804.

XX.Wei, C. P., Yang, C. S., Lee, C. H., Shi, H., & Yang, C. C.(2014). Exploiting poly-lingual documents for improving text categorization effectiveness.Decision Support Systems,57, 64-76.

XXI.Wu, Q., Tan, S., & Cheng, X. (2009, August). Graph ranking for sentiment transfer. InProceedings of the ACL-IJCNLP 2009 Conference Short Papers(pp. 317-320). Association for Computational Linguistics.

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Abstract:

Digital delta sigma modulator (DDSM) is integral part of the divider of PLL based fractional –N frequency synthesizer. The output of DDSM is notorious for spurious tones in its output. Generally, the inherent periodicity of DDSM is considered the main reason for generation of these tones. The recent researched focus on the role of linear feedback shift register (LFSR) based pseudorandom dither which is added with input of DDSM to break its periodicity. Since, an ideal random sequence cannot be realized; the periodic nature of LFSR dither itself is considered a sour to energize these spurious tones appearing at the output of synthesizer. The self-dithering technique is claimed to perform the efficient dithering of the input of DDSM without using LFSR. In this paper we investigate the use of self-dithering technique with MASH 1-1-1 & EOFM mash that is claimed to be as effective as LFSR dither.

Keywords:

Power generating capacity,Energy Crisis,Supply and demand,Renewable Energy,Energy Sources,

Refference:

I. Five steps to solving Pakistan’s energy crisis–The Express Tribune Blog, By
Adnan Khalid Rasool Published: March 3, 2012
II. Muhammad Zulqarnain Abbasi, M. Aamir Aman, Hamza Umar Afridi, Akhtar
Khan. Electrical Engineering Department, IQRA National University,
Peshawar, Pakistan.“Sag-Tension Analysis of AAAC Overhead Transmission
lines for Hilly Areas” International Journal of Computer Science and
Information Security (IJCSIS), Vol. 16, No. 4, April 2018.
III. National Transmission and despatch company, Power System Statistics,2016-
2017
IV. Pakistan Energy Year Book, (2017)
V. US Department of Energy 2002
VI. World Bank report 2017
VII. WAPDA Annual Report 2016-17, Water and Power Development Authority
Pakistan. Department of energy, office of energy efficiency and Renewable
Energy Geothermal Energy Program.

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Abstract:

Recommendations help humans in making decisions and hence contribute in increase of user satisfaction. For good recommendations; the recommender should be more precise. From past decades, Collaborative Filtering (CF) has been explored by researchers because of its efficiency and effectiveness. The main objective of CF is to find most similar items using various similarity measures. This research paper proposes improvised mobile recommender that significantly increases accuracy for recommended right plans for mobile users using similarity fusion. Experimental results show that the proposed recommender using similarity fusions provide better recommendation quality.

Keywords:

Recommender System, Cellular networks,Similarities,data plans,Similarity fusion,

Refference:

I.Anand, S. S., & Mobasher, B. Intelligent techniques for web personalization. In Proceedings of the 2003 International Conference on Intelligent Techniques for Web Personalization (pp. 1–36). Springer-Verlag(2003).

II.Blondel VD, Decuyper A, Krings G,” A survey of results on mobile phone datasets analysis”. EPJ Data Sci , pp 4-10(2015).

III.C. Porcel, E. Herrera-Viedma,“Dealing with incomplete information in a fuzzy linguistic recommender system to disseminate information in university digital libraries”, Knowledge-Based Systems, vol. 23, No. 1, pp 32–39 (2010).

IV.C. Porcel, J.M. Moreno, E. Herrera-Viedma,” A multi-disciplinar recommender system to advice research resources in university digital libraries”, Expert Systems with Applications, vol. 36 , No. 10, pp 12520–12528 (2009).

V.Deshpande, M., Karypis, G. “Item-Based Top-N Recommendation Algorithms”. ACM Trans. On Information Systems(2004).

VI.Hill, W., Stead, L., Rosenstein, M., and Furnas, G., “Recommending and evaluating choices in a virtual community of use”, Proceedings of the ACM(CHI’95), New York, 1995, pp. 194-201,(1995).

VII.Kantor, P. B., Ricci, F., Rokach, L., & Shapira, B. (2011). Recommender systems handbook. Springer.

VIII.Mahmood, T., & Ricci, F. Improving recommender systems with adaptive conversational strategies. In Proceedings of the 20th ACM Conference on Hypertext and Hypermedia (pp. 73–82) (2009).

IX.Marlin, B. “Collaborative Filtering: A Machine Learning Perspective”. Phd thesis. University of Toronto ( 2004).

X.Meenakshi, Prof. Pravin Nimbalkar,” An Improvised Recommendation System on Top-N, Unrated and Point of Interest Recommendations Regularized with User Trust and Item Ratings”,IJRCCE,Vol. 5, Issue 8,( 2017).

XI.Miritello G, Rubén L, Cebrian M, Moro E,” Limited communication capacity unveils strategies for human interaction”. Sci Rep 3:1950 (2013).

XII.M.Kavitha devi, P.Venkatesh, ̳An ImprovedCollaborative Recommender System„2009 First International Conference on Networks &Communications-© 2009 IEEE DOI 10.1109/NetCoM.2009.69(2009).

XIII.Neetu Singh, Puneet Kumar & Anil Kumar Dahiya,” RWYW: Recommend What You Want -A Recommender for Mobile Plans”, International Journal of Innovations & Advancement in Computer Science,Vol. 7, No.2 , pp 135-144 ( 2018).

XIV.Neetu Singh, V.K Jain,”A novel Item Recommender for mobile plans”.IJCSIS , Vol. 16 No. 9,(2018).

XV.Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P.,and Riedl, J., “GroupLens: an open architecture for collaborative filtering of Netnews”, Proceedings of the CSCW conference, Chapel Hill, NC, pp.175-186.( 1994).

XVI.Rongfei, J., Maozhong, J., & Chao, L. A new clustering method for collaborative filtering. In International Conference on Networking and Information Technology (pp. 488–492) (2010).

XVII.Rucker, J., and Polanco, M.J., “Personalized navigation for the Web”, Communications of the ACM, March, 40(3), pp. 73-75,(1997).

XVIII.Saaty, T.L., “Fundamentals of decision making andpriority theory with the analytic hierarchy process”,RWS Publications, Pittsburgh, PA, (1994).

XIX.Sarwar, B. M., Karypis, G., Konstan, J., Riedl, J. 2001. “Item-Based Collaborative Filtering Recommendation Algorithms”.WWW (2001).

XX.The mahout website.” http://mahout.apache.org/

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Abstract:

Mobile communication has been transformed in Pakistan by issuance of Third Generation (3G) and Fourth Generation (4G) licenses. Introduction of new technologies has changed the mobile users existing lust for more data and at an extremely high transmission rate. The third and fourth generation technology transitions have enormously improved network performance as compared to old and legacy Time Division Multiple Access (TDMA) technology. Especially, the Long Term Evolution wireless network brings all set to convert the existing mobile networks into end-to-end IP networks. In this review paper, it will be considered what challenges have been faced by the mobile companies in Pakistan for migration of mobile wireless networks from existing technology to 3G CDMA (Code Division Multiple Access) and 4G LTE (Orthogonal frequency division multiplex) networks. The main challenges faced by managers for shifting from existing 2G infra-structures to new 3G and 4G infra-structures are network planning and achieving Quality of Service (QoS) parameter’s for this transition.

Keywords:

Second Generation,Third Generation,Fourth Generation,wireless network planning,Long Term Evolution,Internet Protoco,Code Division Multiple Access,Quality of Service,

Refference:

I.Aggarwal, P., Arora, P. & Neha (2013).Migration from 2G to 4G Mobile Technology. Advance in Electronic and Electric Engineering,3, 1251-1264.

II.Balasubramanian, D. (2006). QoS in cellular networks. Tech. Rep.

III.Bhatti, S. I. (2014, April 24). $1.1 billion raised from 3G, 4G auction. DAWN. Retrieved from http://www.dawn.com/news/1101760/11-billion-raised-from-3g-4g-auction.

IV.Dahiya, A. (2016, June 8).Evolution of Mobile Communication from 1(G) to 4G, 5G, 6G, 7G. Retrieved from https://www.linkedin.com/pulse/evolution-mobile-communication-from-1g-4g-5g-6g-7g-pmp-cfps.

V.Gabriel, C. (2012). Managing the new mobile data network. The challenge of deploying mobile broadband systems for profit. Retrieved from http://amdocs.com/Documents/wp-Managing-the-New-Mobile-Network.pdf,

VI.Gupta, P., & Patil, P. (2009). 4G-a new era in wireless telecommunication. Magister Program in S/W Engineering, Malardalen University.

VII.Janjua, S. (n.d.). 3G/4G LAUNCH IN PAKISTAN. Retrieved from http://www.abnamro.com.pk/2015/01/30/3g-4g-launch-in-pakistan/

VIII.Krendzel, A. (2005). Network planning aspects for 3G/4G mobile systems. Tampere University of Technology.

IX.Martin, C. (2012, October 9). What is 4G? A complete guide to 4G. Retrieved from http://www.pcadvisor.co.uk/feature/mobile-phone/what-is-4g-complete-guide-4g-3403880/.

X.Mishra, A. R. (2004). Fundamentals of cellular network planning and optimisation: 2G/2.5 G/3G… evolution to 4G. John Wiley & Sons.

XI.Mustaqim, M., Khan, K., & Usman, M. (2012). LTE-Advanced: requirements and technical challenges for 4G cellular network. Journal of Emerging Trends in Computing and Information Sciences, 3(5), 665-671.

XII.Pakistani Telecom Spectrum Auction. (2016). In Wikipedia. Retrieved December 15, 2016, from https://en.wikipedia.org/wiki/Pakistani_Telecom_Spectrum_Auction.

XIII.Rouse, M. (2009). 3G (third generation of mobile telephony). TechTarget. Retrieved from http://searchtelecom.techtarget.com/definition/3G.

XIV.Sequerah, A. (2014, November 17). Unified Network Planning, Network Optimization and Service Assurance for LTE. Retrieved from http://www.infovista.com/blog/index.php/2014/11/17/unified-network-planning-network-optimization-and-service-assurance-for-lte/

XV.Yusufzai, A. (2016). PTA Awards 4G Spectrum and License to Telenor Pakistan. Propakistani. Retrieved fromhttps://propakistani.pk/2016/07/15/pta-awards-4g-spectrum-and-license-to-telenor-pakistan.

XVI.Pakistan Telecommunication Authority. Annual Report 2016. Retrieved from http://www.pta.gov.pk/index.php?option=com_content&task=view&id=2265&Itemid=740

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Abstract:

Fatigue or sleep is a crucial factor in traffic accidents especially for long distance journeys. In this article, an innovative module depicts for automatic driver drowsiness detection based on heart rate and skin temperature. This system aims towards detecting and alert the driver to prevent accidents. Bothsensor performance has been utilized and modulated through the Arduino microcontroller and produce output. Achieve better accuracy for detecting sleep, a new method that is the combination of the heart rate sensor, as well as body temperature sensor, is proposed. Also, the proposed system can monitor the heart rate and body temperature continuously for detecting the health status of the driver also. Experimental results show high accuracy in each section which makes this system reliable for driver sleep detection and alarm system.

Keywords:

Driver drowsiness detection,Accident prevention,Heart rate sensor,Body temperature sensor,

Refference:

I.Alshaqaqi, B., Baquhaizel, A. S., Ouis, M. E. A., Boumehed, M., Ouamri, A., &Keche, M. (2013, May). Driver drowsiness detection system. In Systems, Signal Processing and their Applications (WoSSPA), 2013 8th International Workshop on (pp. 151-155). IEEE.

II.Banik, B. C., Ghosh, M., Das, A., Banerjee, D., Paul, S., & Neogi, B. (2017, March). Design of mind-controlled vehicle (MCV) &study of EEG signal for three mental states. In Devices for Integrated Circuit(DevIC), 2017 (pp. 808-812). IEEE.

III. Bhattacharjee, S., Das, Z., Das, A. K., Roy, S., & Neogi, B. (2014, January). An approach towards error less ECG signal equation basedon computational simulation aspect with modelingof cardiovascular disorder diagnosis. In Control, Instrumentation, Energy and Communication (CIEC), 2014 International Conference on (pp. 181-185). IEEE.

IV. Charles, A. C., Janet, C. Z., Joseph, M. R., Martin, C. M. E., & Elliot, D. W. (1980). Timingof REM sleep is coupledto the circadian rhythm of body temperature in man. Sleep, 2(3), 329-346.

V.Chieh, T. C., Mustafa, M. M., Hussain, A., Zahedi, E., &Majlis, B. Y. (2003, August). Driver fatigue detection using steering grip force. In Research and Development, 2003. SCORED 2003. Proceedings. Student Conference on (pp. 45-48). IEEE.

VI. Iampetch, S., Punsawad, Y., &Wongsawat, Y. (2012, December). EEG-based mental fatigue prediction for driving application. In Biomedical Engineering International Conference (BMEiCON), 2012 (pp. 1-5). IEEE.

VII. Lin, C. T., Wu, R. C., Liang, S. F., Chao, W. H., Chen, Y. J., & Jung, T. P. (2005). EEG-based drowsiness estimation for safety driving using independent component analysis. IEEE Transactions on Circuits and Systems I: Regular Papers, 52(12), 2726-2738.

VIII. Tian, Z., & Qin, H. (2005, October). Real-time driver’s eye state detection. In Vehicular Electronics and Safety, 2005. IEEE International Conference on (pp. 285-289). IEEE.

IX. Tsunoda, M., Endo, T., Hashimoto, S., Honma, S., &Honma, K. I. (2001). Effects of light and sleep stages on heart rate variability in humans. Psychiatry and clinical neurosciences, 55(3), 285-286.

X. Vitabile, S., De Paola, A.,&Sorbello, F. (2010, April). Bright pupil detection in an embedded, real-time drowsiness monitoring system. In Advanced Information Networking and Applications (AINA), 2010 24th IEEE International Conference on (pp. 661-668). IEEE.

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