iosr-JECE   A Conference on Wireless Communication and Android Apps "WiCAA–15"

Abstract: Light reach nearly universally so communication can also go along with light freely. Light Fidelity is a branch of wireless communication which is a rising technology. By using visible light as transmission medium, Li-Fi provides wireless communication. I propose using common and cheap fluorescent lamps to transmit information for navigation, because using fluorescent lamps to provide light is so popular, widespread, and cost-effective in existing buildings. In this paper I have described a novel light-sensor-based information transmission system. In a novel manner, fluorescent light is used as the medium to transmit information, which is encoded by using a pulse width modulation technique. The user will receive the information which is encoded in light through a photo detector, after a data stream of information is received and processed, it will be fed into the computer through the serial port for further processing. The proposed system can be used in indoor supervision and navigation applications.
Keywords - Arduino Compatible Development Board, Electronic Ballast, Fluorescent Light, Photo receiver, Wearable Computer.

[1]. Adrian David Cheok and Li Yue. "a Novel light-sensor-based information transmission system for indoor positioning and navigation". IEEE transactions on instrumentation and measurement, 60(1), january 2011.
[2]. Josef Shwartz Frank Deicke. "li-fi a new paradigm in wireless communication". EFY, 2012.
[3]. V.Roja M.Mounika N.Navyatha, T.M.Prathyusha. "Li-fi (light fidelity)". International Journal of Scientific and Engineering Research,, 4, 2013.
[4]. Ruchi Garg. "li-fi data on light instead of online". Anveshanam a National Journal of Computer Science and Applications, 1, 2013.

Abstract: Major road accidents occur at night on curve roads and glare occurred from the headlights of front incoming vehicles. Night time driving with conventional headlamps is particularly dangerous: approximately 25% of the driving is done at night but 55% of the driving mishaps occur during this period. Due to this constrain, a need to understand ansubstitutetechnology solution. Adaptive front light system (AFS) helps get better driver's visibility at night time hence achieving improve safety. The purpose of this work is to design and build an AFS model. From the results, it is concluded that the headlamp swings in horizontal track by sensing steering angle and vertical by sensing distance between subject vehicle and next vehicle. Accuracy, consistency and availability of the components were few considerations during the conceptualization stage.
Keywords-Adaptive Front Lighting System, Conventional Light System, FPGA,Steering Angle,Sensing distance.

[1] MeftahHrairi and Anwar B. Abu Bakar "Development of an Adaptive Headlamp Systems",International Conference on Computer and Communication Engineering (ICCCE 2010), 11-13 May 2010, Kuala Lumpur, Malaysia.
[2] SnehalParhad "Development Of Automotive Adaptive Front Lighting System", Proceedings of IRF International Conference, 5& 6 February 2014, Pune India. ISBN: 978-93-82702-56-6
[3] T. Aoki, H. Kitamura, K. Miyagawa, and M. Kaneda, (1997). "Development of active headlight system,", (SAE Technical Paper Series No. 970650). Warrendale, PA: Society of Automotive Engineers, 1997.
[4] H. Hogrefe and R. Neumann, "Adaptive Light Pattern – A new way to improve Light Quality," SAE 970644, 1997
[5] Dr Joachim Damasky, Dr ArnHosemann, "The Influence of the Light Distribution of Headlamps on Drivers Fixation Behaviour at Night", SAE 980319, 1998.

Abstract: "Steganography" is a Greek origin word which means "hidden writing". Steganography word is classified into two parts: Steganos which means "secret or covered" (where you want to hide the secret messages) and the graphic which means "writing" (text). Steganography is a powerful technique to hide secretes data inside the cover object. We are going to capture image then it is passed to discrete wavelet transform for sub band coding. After that using LSB stenography we are going to embedding secret data using inverse discrete wavelet transform stego image is extracted with secret data and original image is reconstructed. FPGA Spartan 3 kit is used for implementation.
Keywords: Steganography, least significant bit, Encryption, FPGA.

[1] K. Prasad., V. Jyothsna., S. Raju and S. Indraneel, "High Secure Image Steganography in BCBS Using DCT and Fractal Compression," International Journal of Computer Science and Network Security, vol. 10 No.4, April 2010.
[2] E. Walia, P. Jain, Navdeep, "An Analysis of LSB& DCT based Steganography", Global Journal of Computer Science and Technology, April, 2010, Vol. 10, pp. 4-8.
[3] Mohd,B.J., Abed,S., Al-Hayajneh., Alouneh,S "FPGA hardware of the LSB steganography method",Computer ,information and telecommunication system(CITS),2012 International conferenceon,vol., no.,1,4,14-16 May 2012
[4] B. Weaver, Now You See It, Scientific Computing 24.6 (May 2007): 18-39.
[5] B. Glass, Hide in Plain Sight, PC Magazine 21.18 (15 Oct. 2002): 75.

Abstract: Recently, the interaction between humans and robots has become an important issue for extending the use of robots. The purpose of this project is to show how a real-time human-robot interaction with hand gesture recognition which can be constructed using color recognition System. Hence, it is required that the following two major questions be resolved in this study. Firstly, either staying in indoor or outdoor lighting conditions, the robot must be able to detect and spot hand gestures with a specific color from data of robot vision system. Secondly, it is necessary that the robot also can interpret hand gestures performed by humans.
Keywords: Digital Image Processing, Hand Gesture Control, Open Cv, Color Recognition.

[1] IEEE Paper 978-0-7695-3845-7 / 09 : A color edge detection Algorithm in RGB Color Space
[2] Gesture Recognition Wikipedia en.wiipediaorg/wii/Gesture_ recognition
[3] J. F. Canny, .A computational approach to edge detection,. IEEE Trans.Pattern Anal. Machine Intell., vol. 8, no. 6, pp. 679.698, Nov. 1986.
[4] C. L. Novak and S. A. Shafer, "Color edge detection," in Proc. DARPA Image Understanding Workshop, 1987, pp. 35–37.
[5] R. Nevatia, "A color edge detector and its use in scene segmentation," IEEE Trans. Syst., Man, Cybern., vol. SMC-7, no. 11, pp. 820–826, Nov. 1977.

Abstract: Due to fast life style Heart related problems are increasing day by day and it's very important that disease related to heart can be diagnosed easily by simple medical techniques. These diseases can be diagnosed by ECG (Electrocardiogram) signals. ECG measures electrical potentials from the body surface with contact electrodes, thus it is treated as one of the important signals. The ECG recording is often deteriorated by several factors such as power line interference and baseline wander noise. Various noises have to be removed for better clinical evaluation. This paper reviews the ways in which noise occurs & methodologies to remove such addition in the ECG signal. Some parameters like, Power Spectral Density (PSD), average power and signal to noise ratio (SNR) are calculated from ECG signal and compared with the performance of different methods used for removal of ECG noise.
Keywords: ECG, power line interference, FIR filters, Windows, Power spectrums.

[1] RavindraPratapNarwaria,"Reduction of noise from ecg signal using fir low pass filter with various window techniques", Current Research in Engineering, Science and Technology (CREST) Journals, ISSN 2320-706X
[2] Cramer E, McManus CD, Neubert D., "Estimation and removal of power line interference in the electrocardiogram: A comparison of digital approaches", Comput Biomed Res. 1987 Feb; 20(1):12-28
[3] Mahesh chavan, * RA.Agarwala, ** M.D.Uplane"Suppression of Baseline Wander and power line interference in ECG using Digital IIR Filter" , Issue 2, Volume 2, 2008 356 Manuscript Received August 3, 2007; Revised Received December 5, 2007
[4] Mbachu C.B and Offor K.J, "reduction of powerline noise in ecg signal using fir digital filter implemented with hammingwindow", International Journal of Science, Environment and Technology, Vol. 2, No 6, 2013, 1380 – 1387 ISSN 2278-3687 (O)
[5] RinkyLakhwani, J.P. Saini, "DESIGN AND COMPARISION OF DIGITAL FILTERS FOR REMOVAL OF BASELINE wandering from ecg signal", 2013 5th International Conference on Computational Intelligence and Communication Networks, 978-0-7695-5069-5/13 $26.00 © 2013 IEEE

Abstract: This paper presents a eye pupil control system for an supply powered wheelchair, based on EMG (Electromyography) signals recorded from eye pupil and muscle voltage levels. One-dimensional continuous EMG signals are obtained, amplified, filtered and translated with 32-bit microcontroller into number of control commands like forward, backward, left, right, for the wheelchair that supports multi-directional control and EOG signals detected from eye pupils are used to adjust rotation or speed of the motor. The system works under switching basis to choose either control state or non-control state so that a user are easily do any muscle activity related to eye within non control state. The proposed system is reliable, easy to set up, easy to use and less costly as compared to earlier systems.
Keywords - EMG, EOG, Hands free.

[1] A. Fattouh, M. Sahnoun and G. Bourhis' "Force Feedback Joystick Control of a Powered Wheelchair: Preliminary Study,"IEEE Transactions, Vol. 7, No. 4, pp. 443-451, 2004.
[2] S.D.Suryawanshi and J.S.chitode and S.S.Pethkar, "Voice operated Wheelchair", International Journal of Advanced Research in Computer science and software engineering, Vol. 3 No.5 pp. 487-490 May 2013.
[3] Ericka Janet Rechy-Ramirez, Huosheng Hu and Klaus McDonald-Maier, "Head movements based control of an intelligent wheelchair in an indoor environment," IEEE International Conference on robotics bioinformatics, pp. 1463-1469, Dec 2012.
[4] Lai Wei and Huosheng Hu, "EMG and Visual based HMI for hands-free control of an intelligent wheelchair," Proceedings of the 8th World Congress on Intelligent Control and Automation, Jinan, China, Vol. 1458, pp. 1027-1032, July2010.
[5] Rajesh Kannan Megalingam, Ramesh Nammily Nair, Sai Manoj Prakhya, "Automated Voice based Home Navigation System for the Elderly and the Physically Challenged", proc.ICACT2011 pp. 603-08, Feb.2011.

Abstract: In biomedical field Medical image analysis is an important biomedical application, which is highly computational in nature and requires the help of the automated systems.In this paper, a simple algorithm for detecting the shape of tumor in brain MR Images is described. Brain tumor are created by an abnormal and uncontrolled cell division. It is an intracranial solid neoplasm. Magnetic resonance imaging (MRI) is another modern diagnostic imaging technique that produces cross-sectional images of brain. Unlike CT scans, MRI works without radiation. The MRI tool uses magnetic fields and a sophisticated computer to take high-resolution pictures of brain soft tissues. These images are visually examined by the physician for detection & diagnosis of brain tumor. So as assist to physician for calculate correct shape of tumor, this project uses computer aided method for detection of brain tumor.This method allows the segmentation of tumor tissue with accuracy using two algorithms. In addition, it also reduces the time for analysis. At the end gives result as tumor is extracted from the MR image and the shape also determined. The stage of the tumor is examined based on the amount of area calculated from the cluster.
Keywords-Cluster segmentation, Magnetic Resonance Imaging (MRI), Brain tumor, K-means, Fuzzy C means.

[1] Phooi Yee Lau, Frank C. T. Voon, and Shinji Ozawa, The detection and visualization of brain tumors on T2-weighted MRI images using multiparameter feature blocks, Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, 2005, pp-5104-5107.
[2] Dipali M. Joshi, Dr. N. K. Rana and V. M. Mishra, Classification of Brain Cancer Using Artificial Neural Network, International Conference on Electronic Computer Technology,2010,pp-112-116.
[3] Carlos Arizmendi, Juan Hernández-Tamames, Enrique Romero, Alfredo Vellido, Francisco del Pozo, Diagnosis of Brain Tumors from Magnetic Resonance Spectroscopy using Wavelets and Neural Networks, Annual International Conference of the IEEE EMBS, 2010, pp-6074-6077.
[4] Arpita Das, Mahua Bhattacharya, A Study on Prognosis of Brain TumorsUsing Fuzzy Logic and Genetic Algorithm Based Techniques, International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing,2009,pp-348-351.
[5] AndacHamamci, Nadir Kucuk, KutlayKaraman, KayihanEnginand GozdeUnal ,Tumor-Cut: Segmentation of BrainTumors on Contrast Enhanced MR Images for Radiosurgery Applications, IEEE Transactions on medical imaging, March 2012,vol 31.

Abstract: Environment monitoring has become a necessity because of global warming and climate change. All across the globe researchers are trying to monitor the environmental parameters of temperature, humidity and pollutants gases mere precisely in real time. Sensing system using sensor arrays has been developed to monitor indoor environment, however, these systems are costly and have not gained wide acceptance. Precise monitoring of building environment has a huge potential in terms of energy savings. In this paper, an effort has been made to develop a 4-channel energy efficient and low cost data logger for indoor environment. The Data logger is proposed to be developed with the use of PIC 18F4458. Real Time Clock and EEPROM are interfaced for sampled data with the instance of sampling time and month/date/year; these sampled data will be stored in the EEPROM to the note pad tabular form with the help of graphical user interface (GUI).
Keywords -Gas Sensors, GUI, Real Time Clock , ADC, Smart Transducer Interface, ZigBee

[1] E. Samakovlis, A. Huhtale, T. Bellander and M. Svartengren, "Valuinghealth Effects of Air Pollution Focus on Concentration-Response Functions," Journal of Urban Economics, Vol. 58, No. 2, 2005, pp. 230-249.
[2] J. D. Richard and G. S. Brager, "Thermal Comfort in Naturally Ventilated Buildings: Revisions to ASHRAE Standard 55," Energy and Buildings, Vol. 34, No. 6, 2002, pp. 549-561.
[3] A. Pardo, L. Camara, J. Cabre, A. Perera, X. Cano, S. Marco and J. Bosch, "Gas Measurement System Based
on IEEE 1451.2 Standard," Sensors and Actuators B, Vol. 116, No. 1, 2006, pp. 11-16.
[4] L. Camara, O. Ruiz, A. Herms, J. Samitier and J. Bosch, "Automatic Generation of Intelligent Instruments for
IEEE1451," Measurement, Vol. 35, No. 1, 2004, pp. 3-9.
[5] Microdaq, 3 March 2009. http://www.microdaq.com/datalogger/