Source: http://imanagerpublications.com/viewarticles/5/863/Vol9Iss1
Timestamp: 2019-04-22 20:13:18+00:00

Document:
*-*** BE Graduate, Department of Electronics and Telecommunication Engineering, Pimpri Chinchwad College of Engineering, Nigdi, Pune,Maharashtra, India.
**** Assistant Professor, Pimpri Chinchwad College of Engineering, Nigdi, Pune, Maharashtra, India.
This paper deals with the upgradation of CNC machine and its flexibility for modern technologies. In this paper, designing of control panel and its implementation is discussed. Different components are mounted in control panel to protect the system in case of power failure and protect motors against phase down. Almost all the industries use PLCs now-a-days because it is highly compatible, reduces wiring, and makes the design more compact and reliable. This is as up-gradation of CNC machine. Generally, PLCs are programmed in “ladder logic”, which strongly resembles a schematic diagram of relay logic. Different ladder logics are developed for performing different operations. This research work can be extended further by emerging it with IIoT, i.e. Industrial Internet of Things, where we can monitor CNC machines from remote place.
. Ansar Md, A. H., Alamoodi, M. A. H. S. M., Mahreen, S., Sultana, T., & Rahman Uzair, M. A. (2016). Features and applications of CNC Machines and Systems. International Journal of Science, Engineering and Technology Research (IJSETR), 5(3), 717-726.
. Dhatrak, S. A., & Gond, V. J., (2016). Restructuring control system of top surface grinding machine for improved performance. International Journal of Electrical and Electronics Engineers, 8(2), 321-325.
. Hindalekar, M. A. R., & Patil, M. S. (2015). Up – gradation of conventional control panel with PLC for the total utilization of machine. International Journal on Recent and Innovation Trends in Computing and Communication, 3(2), 461-465.
. Pabla, B. S., & Adithan, M. (2007). CNC Machines. New Age Publishers, NewDelhi.
. Rajput, R., & Sarathe, A. K. (2016). Comparative study of CNC Controllers used in CNC Milling Machine. American Journal of Engineering Research (AJER), 5(4), pp. 54-62.
. Wadhawe, A. S., & Kurtadikar, R. P. (2014). Automation of ash handling system with event logger. International Journal of Computer Trends and Technology (IJCTT), 18(5), 231-237.
* Research Scholar, Department of Electronics and Communication Engineering, S.V.U. College of Engineering, S.V. University, Tirupati,Andhra Pradesh, India.
** Professor, Department of Electronics and Communication Engineering, S.V.U. College of Engineering, S.V. University, Tirupati,Andhra Pradesh, India.
The MST radar is situated at National Atmospheric Research Laboratory (NARL), Gadanki, Andhra Pradesh. This radar is used to investigate the atmospheric dynamics in the regions of Mesosphere, Stratosphere, and Troposphere (MST). MST radar was developed with an active phased antenna array consisting of 1024 Yagi-Uda antenna elements and operated by a frequency of 53 MHz. In this article, the authors introduce new hybrid window functions by using a combination of Kaiser and Blackman windows. The new hybrid window functions exhibit low side lobe levels and narrow beam width of main lobe so that the spectral leakage is minimum. The proposed window based algorithms has been applied to MST radar time series data to compute Doppler power spectrum. After computing Doppler spectrum, the wind parameters like Zonal U, Meridional V, and Wind velocity W can be calculated from the Doppler profile. The obtained wind velocity components of the MST radar data is validated through the Global Positioning System (GPS) Radiosonde data.
. Anandan, V. K. (2001). Atmospheric Data Processor- Technical and User Reference Manual. Tirupati, India: NMRF, DOS Publication.
. Anandan, V. K., Balamuralidhar, P., Rao, P. B., & Jain, A. R. (1996). A method for adaptive moments estimation technique applied to MST radar echoes. In Proc. Prog. Electromagn. Res. Symp. (pp. 360-365).
. Anandan, V. K., Ramachandra Reddy G., & Rao, P. B. (2001). Spectral analysis of atmospheric signal using higher orders spectral estimation technique. IEEE Trans. Geosci. Remote Sens., 39(9), 1890-1895.
. Anandan, V. K., Pan, C. J., Rajalakshmi, T., & Reddy, G. R. (2004, November). Multitaper spectral analysis of atmospheric radar signals. In Annales Geophysicae 22(11), 3995-4003.
. Eappen, N. I., Reddy, T. S., & Reddy, G. R. (2016). Semiparametric algorithm for processing MST Radar Data. IEEE Transactions on Geoscience and Remote Sensing, 54(5), 2713-2721.
. Hooper, D. A. (1999). Signal and noise level estimation for narrow spectral width returns observed by the Indian MST radar. Radio Science, 34(4), 859-870.
. Rao, V. V. M. J., Rao, D. N., Ratnam, M. V., Mohan, K., & Rao, S. V. S. (2003). Mean vertical velocities measured by Indian MST radar and comparison with indirectly computed values. Journal of Applied Meteorology, 42(4), 541-552.
. Kaiser, J. F. (1974). Nonrecursive digital filter design using the Io-sinh window function. In Proc. 1974 IEEE International Symposium on Circuits & Systems (pp. 20- 23).
. Muralidhar, P. V., Nataraj, D., Lokesh Raju, V., & Naik, S. K. (2010, July). Implementation of different FIR high pass filters using fractional Kaiser window. In Signal Processing Systems (ICSPS), 2010 2nd International Conference on (Vol. 2, pp. V2-651). IEEE.
. Rao, D. U. M., Reddy, T. S., & Reddy, G. R. (2014). Atmospheric radar signal processing using principal component analysis. Digital Signal Processing, 32, 79-84.
. Reddy, T. S., & Reddy, G. R. (2010a). MST radar signal processing using cepstral thresholding. IEEE Transactions on Geoscience and Remote Sensing, 48(6), 2704-2710.
. Reddy, T. S., & Reddy, G. R. (2010b). Spectral analysis of atmospheric radar signal using filter banks-polyphase approach. Digital Signal Processing, 20(4), 1061-1071.
. Thatiparthi, S. R., Gudheti, R. R., & Sourirajan, V. (2009). MST radar signal processing using wavelet-based denoising. IEEE Geoscience and Remote Sensing Letters, 6(4), 752-756.
* BE Graduate, Department of Electronics and Communication Engineering, K.S. School of Engineering and Management, VTU,Bangalore, Karnataka, India.
** Assistant Professor, Department of Electronics and Communication Engineering, K.S. School of Engineering and Management, VTU,Bangalore, Karnataka, India.
Paralyzed people find it difficult to communicate their intent to serve outside world. Hence, this work provides a Mind Controlled communication system that uses Brain Computer Interface which can bypass different communication channels like neurons’ electrical activity, muscles, and thoughts to supply direct communication and management between the physical devices and human brain by translating dissimilar patterns of brain activity into instructions followed by the conversion of predefined text on the screen into voice. The proposed system consists of: Amplifier Filter circuits and LabVIEW software for signal processing and acquisition. The main aim of this work is to develop a brain computer interface system called BCI system that allows the paralyzed people to communicate their intention without any difficulty, provided it is more superior which may assist disabled folks in their everyday life. Different messages are put up on the visual stimulation screen and made to flicker at different frequencies. When patient looks at one of the flickering frequency on the screen, the same frequency is generated in the visual cortex of the human brain and the frequency is then determined by signal acquisition system and translates the predefined assigned message to voice.
. Bender, T., Kjaer, T. W., Thomsen, C. E., Sorensen, H. B., & Puthusserypady, S. (2013, July). Semi-supervised adaptation in SSVEP-based brain-computer interface using tri-training. In Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE (pp. 4279-4282). IEEE.
. Besio, W. G., Cao, H., & Zhou, P. (2008). Application of tripolar concentric electrodes and prefeature selection algorithm for brain–computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 16(2), 191-194.
. Chan, F. H., Yang, Y. S., Lam, F. K., Zhang, Y. T., & Parker, P. A. (2000). Fuzzy EMG classification for prosthesis control. IEEE Transactions on Rehabilitation Engineering, 8(3), 305- 311.
. Chang, B. C., & Seo, B. H. (2009, February). Development of new brain computer interface based on EEG and EMG. In Robotics and Biomimetics, 2008. ROBIO 2008. IEEE International Conference on (pp. 1665-1670). IEEE..
. Chi, Y. M., & Cauwenberghs, G. (2009, September). Micropower non-contact EEG electrode with active common-mode noise suppression and input capacitance cancellation. In Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE (pp. 4218-4221). IEEE.
. Diez, P. F., Mut, V. A., Perona, E. M. A., & Leber, E. L. (2011). Asynchronous BCI control using high-frequency SSVEP. Journal of Neuroengineering and Rehabilitation, 8(1), 39.
. Dilshad, A., Uddin, V., Naz, U., Parveen, S., Javid, T., & Memon, A. M. (2016). On the development of a novel, Plug and Play SSVEP-EEG based General Purpose Human- Computer Interaction device. Asian Journal of Engineering, Sciences & Technology, 1-5.
. Fukuda, O., Tsuji, T., Kaneko, M., & Otsuka, A. (2003). A human-assisting manipulator teleoperated by EMG signals and arm motions. IEEE Transactions on Robotics and Automation, 19(2), 210-222.
. Hasan, M. K., Mondal, C., Al Mahmud, N., & Ahmad, M. (2015, December). Performance analysis of SSVEP based wireless Brain computer Interface for wet and dry electrode. In Advances in Electrical Engineering (ICAEE), 2015 International Conference on (pp. 64-67). IEEE.
. Hazrati, M. K., Husin, H. M., & Hofmann, U. G. (2013, September). Wireless brain signal recordings based on capacitive electrodes. In Intelligent Signal Processing (WISP), 2013 IEEE 8th International Symposium on (pp. 8-13). IEEE.
. Jabode, J. S., & Shende, P. M. (2015). Literature review of Brain Computer Interface (BCI) using EEG signal. IJETT, 8(10), 1435-1467.
. Lesenfants, D., Habbal, D., Lugo, Z., Lebeau, M., Horki, P., Amico, E., ...& Laureys, S. (2014). An independent SSVEP-based brain–computer interface in locked-in syndrome. Journal of Neural Engineering, 11(3), 035002.
. Malmivuo, J., Ahokas, S., & Välkky, T. (2014, October). High-resolution EEG recording system using smart electrodes. In Electronic Conference (BEC), 2014 14th Biennial Baltic (pp. 21-24). IEEE.
. Oehler, M., Neumann, P., Becker, M., Curio, G., & Schilling, M. (2008, August). Extraction of SSVEP signals of a capacitive EEG helmet for human machine interface. In Engineering in Medicine and Biology Society, 2008. EMBS 2008 30th Annual International Conference of the IEEE (pp. 4495-4498). IEEE.
. Ramoser, H., Muller-Gerking, J., & Pfurtscheller, G. (2000). Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Transactions on Rehabilitation Engineering, 8(4), 441-446.
. Resalat, S. N., & Afdideh, F. (2012, December). Realtime monitoring of military sentinel sleepiness using a novel SSVEP-based BCI system. In Biomedical Engineering and Sciences (IECBES), 2012 IEEE EMBS Conference on (pp. 740-745). IEEE.
. Sullivan, T. J., Deiss, S. R., Jung, T. P., & Cauwenberghs, G. (2008, May). A brain-machine interface using dry-contact, low-noise EEG sensors. In Circuits and Systems, 2008. ISCAS 2008. IEEE International Symposium on (pp. 1986-1989). IEEE.
. Wang, Y. T., Wang, Y., Cheng, C. K., & Jung, T. P. (2012, August). Measuring steady-state visual evoked potentials from non-hair-bearing areas. In Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE (pp. 1806-1809). IEEE.
. Teng, C., Zhang, Y., & Wang, G. (2014). The removal of EMG artifact from EEG signals by the multivariate empirical mode decomposition. 2014 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) (pp. 873-876).

References: V. 
 V. 
 V. 
 V. 
 V. 
 V. 
 V. 
 V. 
 V.