iosr-JEEE   National Conferecne on "Emerging Research Trends in Electrical, Electronics & Instrumentation" (ERTEEI'17)

Abstract: T h i s p a p e r in t r o d u c e s a n in t e l l i g e n t d e s i g n w h i c h w o u l d t r a n s f o r m t h e lif e o f fi s h e r m e n c o m m u n i t y in In d i a . O n e o f t h e p r o b l e m s fa c e d b y t h e In d i a n fi s h e r m e n i s t h e a r r e s t m a d e b y t h e n e i g h b o u r i n g c o u n t r y . T h i s i s b e c a u s e of t h e la c k of k n o w l e d g e of t h e i r p o s i t i o n a c r o s s t h e s e a . T h i s p r o b l e m c a n b e o v e r c o m e w i t h t h e d y n a m i c lo c a t i o n o f t h e v e s s e l b y u s i n g t h e Gl o b a l P o s i t i o n i n g S y s t e m ( G P S ) a n d t h e El e c t r o n i c C o n t r o l U n i t t h a t u s e s PI C 1 6 F 8 7 7 A M I C R O C O N T R O L L E R al o n g w i t h a w e a t h e r f o r e c a s t .

Keywords: PI C 1 6 F 8 7 7 A , , G P S , T r a n s m i t t e r , M o t o r , R e c e i v e r , S e n s o r s ( H u m i d i y , T e m p e r a t u r e , L D R ) , Zi g b e e ,

[1]. Bla c k m a n a n d R.P a p o l i, "D e s i g n a n d Analy s i s of Mo d e r n Tr a c k i n g Sy s t e m s " . Bos t o n , M.A: Arti c Ho u s e , 1 9 9 9 . "N a v i g a t i o n Ale r t Sys t e m for Fi s h e r m a n U si n g La b- Vie w " Int e r n a t i o n a l Jou r n a l of E n g i n e e r i n g a n d Te c h n i c a l Re s e a r c h (IJETR) IS S N : 23 2 1 - 0 8 6 9 ,
[2]. "M ul t i P u r p o s e M a r i n e Wir e l e s s N e t w o r k s Fo r Fis h e r m a n Aid And Ot h e r Appli c a t i o n s " In t e r n a t i o n a l Jou r n a l of E n g i n e e r i n g Re s e a r c h & Te c h n o l o g y (IJERT ) IS S N : 22 7 8 - 0 1 8 1 IJERTV 2I S 7 0 0 1 6 Vol. 2 Iss u e 7, July – 20 1 3
[3]. "DE S I G N O F BORD E R ALERT SYST E M 2 (0 2) , M a r c h - Apr il 20 1 4 , IS S N 2 3 2 1 - 25 4 3
[4]. "Bo r d e r Ale r t Sys t e m for Fi s h e r m e n Usi n g GP S Sys t e m "Wo r l d Jou r n a l of Re s e a r c h a n d Revi e w (WJRR) IS S N : 2 4 5 5 - 39 5 6 , Volu m e - 2, Iss u e - 5, Ma y 20 1 6 P a g e s 01- 04 Asif Iq b a l Mull a , S u s h a n t h K J, P r a s h a n t h Ku m a r H R, Abu- Ba k r S h a m e e z
[5]. G P S - b a s e d ve s s e l po si t i o n m o n i t o r i n g a n d dis p l a y sys t e m . Ae r o s p a c e a n d Ele c t r o n i c Sys t e m s Ma g a z i n e , IE E E , Jul 19 9 0 .

Abstract: The efficient administration and treatment of municipal and industrial waste water is crucial to sustaining community health and a clean, safe environment. Distributed Control System (DCS) is an archetype of automated control system that is distributed throughout the plant to provide commands to different parts of various machines. This paper proposes a DCS software application on waste water treatment plant model. By enhancing this technique we can interface to the process hardware, to create a comprehensive real-time application for a modern waste water operation. DCS acts as an entire framework in accordance with usual engineering practices for programming, simulating and testing the distributed network, thus enables the user to monitor and control the online processes even from a remote place.
Keywords: BOD, Control Valve, DCS, Reverse Osmosis, TDS

[1] Nathan Mogaru, P.E., John Robinson and Luke Matus, "Seamless Replacement of a DCS in a Large Waste Water Treatment Plant: Lessons Learned from the City of Fort Worth Village Creek Water Reclamation Plant", 2013 ISA Water / Waste Water and Automatic Controls Symposium, Aug 6-8, 2013 – Orlando, Florida, USA.
[2] O. Bernard e, Telemac, "An integrated system to remote monitor and control anaerobic waste water treatment plants through the internet", Proc.of 10th IWA World Congress on Anaerobic Digestion (AD10), (Montreal, Canada, vol. 1, pp. 318-323, 29 August-2 September 2004.
[3] Dhote Jayashree, Chavhan Arvind and Ingole Sangita, "Design of Laboratory Based Waste Water Treatment Plant", Int. Res. J. of Science and Engineering, 2014; Vol. 2 (3): 104-111

Abstract: Smart farming makes a tremendous contribution for food sustainability for 21st century. Our project vertical farming is the practice of producing food in vertically stacked layers, vertically inclined surfaces and/or integrated in other structures using Controlled environment agricultural technology, where all environmental factors are controlled. These facilities utilize artificial control of light, environmental control like humidity, temperature, gases, fertigation. Controlled-environment agriculture (CEA) is a technology-based approach toward food production. The aim of CEA is to provide protection and maintain optimal growing conditions throughout the development of the crop. Production takes place within an enclosed growing structure where the growth conditions are maintained. The crops are grown based on hydroponics method of soilless farming.
Keywords- Controlled environment agricultural technology, Hydroponics, Smart farming, Vertical farming,

[1]. Migdall, S.; Klug, P.; Denis, A; Bach, H., "The addition al value of hyperspectral data for smart farming," Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International , vol., no.,pp.7329,7332, 22-27 July 2012
[2]. Jhuria, M.; Kumar, A; Borse, R., "Image processing for smart farming:Detection of disease and fruit grading," Image Information Processing(ICIIP), 2013 IEEE Second International Conference on , vol., no.,pp.521,526, 9-11 Dec. 2013
[3]. Nattapol Kaewmard, Saiyan Saiyod, "Sensor Data Collection and Irrigation Control on Vegetable Crop Using Smart Phone and Wireless Sensor Networks for Smart Farm", 2014 IEEE Conference on Wireless Sensors (ICWiSE), pp. 106-112, October,26-28 2014
[4]. Castello C.C., Fan J. , Davari A. and Ruei-Xi Chen, "Optimal sensor placement strategy for environmental monitoring using Wireless Sensor Networks," System Theory (SSST), 2010 42nd Southeastern Symposium on , vol., no., pp.275,279, 7-9 March2010

Abstract: Fish keeping is a popular trend nowadays. People from all the age groups like to keep fish at their homes, offices etc. for decoration purpose or as a hobby. Commercial fish farming and ornamental fish farming has become very popular. Therefore it's important to automate aquariums/ponds as it is difficult to check the conditions of an aquarium manually. During periodic intervals, water needs to be changed, the fish needs to be fed, the temperature, pH level and water level of the aquarium needs to be maintained. The project, 'Smart Aquarium' is developed using PLC (Programmable Logic Controller) and SCADA (Supervisory Control and Data Acquisition) to automatically control and maintain parameters such as temperature, pH, water level, lighting, feeding, and dissolved oxygen level.
Keywords - PLC, Aquarium. ,pH, temperature, lighting, level, feeder

[1]. International Journal of Science and Research (IJSR) ISSN (Online):2319-7064 Impact Factor (2012): 3.358 Volume 3 Issue 9, September 2014 www.ijsr.net Licensed Under Creative Commons Attribution CC By Design and Control of Aquarium Water Management System using Programmable Logic Controller (PLC).
[2]. Development of Automatic Fish Feeder By Md. Nasir Uddin, Mm Rashid, Mg Mostafa, Belayet H, Sm Salam, Na Nithe, Mw Rahman & A Aziz, International Islamic University Malaysia
[3]. International Journal of Advanced Research (2015), Volume 3, Issue 7, 1476-1481 Assessment Of Agrochemicals Residue In Fish Ponds In Agricultural Areas Of Ifugao Province Nelson Latap, Dr. Chiemela F. Anyanwu, Dr. Ricardo L. Ildefonso.
[4]. Fish farm management and microcontroller based aeration control system Moataz K El-Nemr1*, Mohamed K El-Nemr2 .Department of Agricultural Engineering, Faculty of Agriculture, Kafrelsheikh University, Egypt; Department of Electrical power and Machines Engineering, Faculty of Engineering, Tanta University, Egypt) March, 2013.

Abstract: In petroleum industry most of its products are transported using pipelines which cover over a large geographical area. Several valve stations are built between the area it covers. Motor operated valves(MOV) are used in each of these stations. These MOV are operated from the control room using supervisory controllers like SCADA.Remote terminal units like PLC is installed in every control stations.PLC sends in the details regarding MOV present status and all other valid information to SCADA.Every station is remotely operated and the power supply is always live.N ow this method of always keeping the power line live leads to unwanted shut down of valve during power surges...............

[1]. Y. Liu, "Design of automatic control system for waterworks based on PLC," IEEE Trans. Antennas Propagation, to be published.2014W.
[2]. George Ellis "Basics of servo motor and drive "Control system design guide (fourth edition )2012.
[3]. National Communications System, "Supervisory Control and Data Acquisition (SCADA) Systems", October 2004 W.Bolton "Ladder and functional block programmng"2009 programmable logic controllers (fifth edition).
[4]. S.R.Venupriya, K.P.Thanusre, P.Saranya, "A Novel Method Of Induction Motor Speed Control Using PLC", International Journal for Research in Applied Science & Engineering Technology (IJRASET) , 3(2), 2015
[5]. Hugh Jack, "automating manufacturing systems with PLCs", April 2005

Abstract: Plastic is one of the most versatile materials of our modern age and yet the popularity of plastic is the problem. By recycling plastic, the amount produced and wasted can be reduced but whereas the process has short-term advantages for the environment, the long-term results aren't so pretty .So the ECO BIN comes with more advantages in recycling process. A 360 degree solution for the PET [4]waste generated at the source of consumption like malls, multiplexes, Hotels & resorts, corporate offices, plants, airports etc. It's a compact automatic and soundproof plastic bottle crushing[2] machine for end user. It Ensures 100% recycling at source of generation of waste.............

Keywords: Crushing machine, PET ,Plastic Recycler, Plastic Detector, PET Detector, Vending Machine.

[1]. Proceedings of International Journal of VLSI design & CommunicationSystems (VLSICS) Vol.3, No.2, April 2012.
[2]. Muhammad Ali Qureshi, Abdul Aziz & Hafiz Faiz Rasool "Design and Implementation of Automatic Ticket System using VerilogHDL" proceedings of international conference on Information Technology, pp- 707-712 IPCSIT vol.17 (2011) © (2011) IACSITPress, Singapore.
[3]. Nikita Khandelwal, Arjoo Agarwal, Harsha Jaisani, Kavita Bhagwani, Prachi Singh Kuntal, Mr. Arvind Sharma, "Change-ExchangeCurrency based Vending Machine using VHDL" proceedings of International Journal of Electronics and Computer ScienceEngineering 331 Available Online at www.ijecse.org ISSN- 2277-1956 [2012].
[4]. Michiko Amano "PET Bottle System in Sweden and Japan: an Integrated Analysisfrom a Life-Cycle Perspective" proceedings of Masters‟ Thesis for LUMES - Lund University International Master‟s Programed in Environmental Science 2003-2004

Abstract: Data mining techniques have been applied to sensor data in a wide range of application domains, such as healthcare monitoring systems, manufacturing processes, intrusion detection, database management, and others. Many data mining techniques are based on computing the similarity between two sensor data patterns. A variety of representations and similarity measures for multi attribute time series have been proposed in the literature. In this paper, we describe a novel method for computing the similarity of two multi attribute time series based on a temporal version of Smith–Waterman (SW), a well-known bioinformatics algorithm..............

Keywords: Bioinformatics algorithm, bioinformatics algorithm

[1] C. K. Narayanan and D. J. Cook, "Activity recognition on streaming sensor data," Pervasive Mobile Comput., vol. 10, pp. 138–154, 2014
[2] M. Popescu and A. Mahnot, "Early illness recognition in older adults using in-home monitoring sensors and multiple instance learning," Methods Inf. Med., vol. 51, no. 4, pp. 359–367, 2012.

Abstract: Represents a more efficient and scalable approach toinfer users' sensitive information without checking the content ofWi-Fi traffic. Secondly, meta-data based demographics inferencecan work on both unencrypted and encrypted traffic. In this study, we present a novel approach to infer userdemographic information by exploiting the meta-data of Wi-Fitraffic. DIP extracts four kinds of features from realworldWi-Fi traffic and proposes a novel machine learning basedinference technique to predict user demographics. Results show that, for unencrypted traffic, DIP can predict genderand education level of users with an accuracy of 78% and 74%respectively.

[1] J. Hu, H. J. Zeng, H. Li, C. Niu and Z. Chen, "Demographic prediction based on user's browsing behavior," In Proc. of WWW'07, ACM, 2007.
[2] B. Bi, M. Shokouhi, M. Kosinski, T. Graepel, "Inferring the demographics of search users: Social data meets search queries," In Proc. Of WWW'13, ACM, 2013.
[3] L. Breiman, "Random forests," Machine learning 45.1 (2001): 5-32.
[4] A. K. Das, P. H. Pathak, N. C. Chuah and P. Mohapatra, "Contextual localization through network traffic analysis," In Proc. of INFOCOM'14, IEEE, 2014.
[5] M. Srivatsa and M. Hicks. Deanonymizing mobility traces: Using social network as a side-channel. In CCS'12, ACM, 2012.

Abstract: Broadcast encryption is a type of encryption scheme first proposed by Amos Fiat and Moni Naor in 1993. Their original goal was to prove that two devices, previously unknown to each other, can agree on a common key for secure communications over a one-way communication path. Broadcast encryption allows for devices that may not have even existed when a group of devices was first grouped together to join into this group and communicate securely. This paper describes broadcast encryption in general, a brief survey on the same, how a few different broadcast encryption schemes work, their merits and demerits.
Keywords: Broadcast encryption, Braid groups, Asymmetric group key agreement, Dynamic asymmetric group key agreement

[1] S.Berkovits, How to Broadcast a secret, Advances in Cryptology - Euro-crypt'91,Lecture Notes in Computer Science 547, Springer, 1991, pp.536-541.
[2] M.Naor, A. Fiat, Broadcast Encryption, Advances in Cryptology - Crypto 93',Lecture Notes in Computer Science 773, Springer, 1994, pp. 480-491.
[3] E.Gafni, J.Staddon and Y.L. Yin, Efficient methods for integrating traceability and broadcast encryption, Proc. Advances in Cryptology - Crypto '99, LNCS 1666, Springer, 1999, 372-387.
[4] Shamir, A., "How to Share a Secret", Communications of the ACM, vol. 22, NO.11, November 1979, pp. 612613.
[5] J.A. Garay, J. Staddon and A. Wool, Long-Lived Broadcast Encryption. Advances in Cryptology - CRYPTO'2000, Lecture Notes in Computer Science, vol 1880 , pp. 333-352, 2000

Abstract: The main stages of video processing includes acquisition, compression, storage, and transport of video data. Distortions can be introduced at various stages of video processing. This leads to a loss in the quality of videos which include both the picture quality as well as audio quality. So a system must be designed to effectively manage the video content at all processing stages aiming to minimize distortions thereby improving the video quality. This paper takes a look at the most current aspect in video processing which includes.................

Keywords: Compression, Full-reference, MS-SSIM, Optical Flow, Steganography

[1]. Manasa K. and Sumohana S. Channappayya, ""An Optical Flow-Based Full Reference Video Quality Assessment Algorithm,‟‟ IEEE transactions on image processing, vol. 25, no. 6, june 2016.
[2]. Z. Wang "Rate scalable foveated image and video communications,"Ph.D. dissertation, Dept. Elect. Comput. Eng., Univ. Texas Austin, Austin, TX, USA, Dec. 2001.
[3]. Z. Wang, L. Lu, and A. C. Bovik, "Video quality assessment based on structural distortion measurement," Signal Process., Image Commun., vol. 19, no. 2, pp. 121–132, 2004.
[4]. H. R. Sheikh and A. C. Bovik, "A visual information fidelity approach to video quality assessment," in Proc. 1st Int. Workshop Video Process. Quality Metrics Consum.Electron, 2005, pp. 23–25.
[5]. Provos, N., Honeyman, P: Hide and Seek: An Introduction to Steganography. IEEE Security & Privacy Magazine 1 (2003).

Abstract: Diabetic retinopathy is one of the leading disabling diseases in eye, it will be a leading causes of preventable blindness in the world. Early diagnosis of diabetic retinopathy enables timely treatments. In order to achieve this a major concern will have to be invested into automated screening programs. For automated screening programs of diabetic retinopathy, image processing and analysis algorithms have to be developed. Candidate objects are first located by applying a dark object filtering process. Then singular spectrum analysis process detects the microaneurysm...............

Keywords: Image processing, microaneurysms, multilayered dark object filtering,singular spectrum analysis

[1]. World Health Organization, International Diabetes Feder tion, diabetes",http://www.who.int/mediacentre/factsheets/fs312/en/index.html (Jun. 5, 2013).
[2]. B. Sujithkumar, and V. Singh, (2012) "Automatic Detection of Diabetic Retinopathy in Non-dilated RGB Retinal Fundus Images", International Journal of Computer Applications, Vol. 47, No. 19, pp.26-32.
[3]. VISION 2020: The Right to Sight - Diabetic Retinopathy [Online].Available:http://www.vision2020.org/main.cfm?type etinopathy =WIBDIEBETIC.
[4]. Fadzil, MH Ahmad, Lila Iznita Izhar, and Hanung Adi Nugroho. "Determination of foveal avascular zone in diabetic retinopathy digital fundus images."Computers in biology and medicine 40.7 (2010): 657-664
[5]. Petra Vars´anyi, Zsolt Fegyv´ariManual Microaneurysm Detection Support with Size- and Shape-based Detection.

Abstract: In This project Electro Encephalogram is a tool for communication between human and robot. EEG is the first method to find epilepsy(1), sleep disorder, comma encephalopathy's and brain death.EEG is an electrophysiological monitoring method to record electrical activity of brain. Here channel between humnan and robotr is IOT, Commonly used channels are air, cables etc. IOT ensures worldwide communication over internet.EOG is the sub division of EEG which can get a voltage level from near human eyes ,using this potentials we can control the robot or robotic arm. It can be used in artificial intelligence with IOT.
Keywords: EEG-Electro Encephalogram, IOT-Internet of thinks.

[1]. Tatum, William O. (2014). Handbook of EEG interpretation. Demos Medical Publishing. pp. 155–190. ISBN 9781617051807. OCLC 874563370..
[2]. Niedermeyer E.; da Silva F.L. (2004). Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincott Williams & Wilkins. ISBN 0-7817-5126-8
[3]. Coherence of gamma-band EEG activity as a basis for associative learning Wolfgang H. R. Miltner1, Christoph Braun2, Matthias Arnold3, Herbert Witte3 & Edward Taub4Note that the place of publication,publisher, and year of publication are enclosed in brackets. Editor of book is listed before book title.
[4]. IoT based Monitoring and Control System forHome Automation Pavithra.D Proceedings of 2015 Global Conference on Communication Technologies(GCCT 2015)
[5]. Gamma Rhythms in the Brain, Xiaoxuan Jia, Adam Kohn PLoS Biol. 2011 Apr; 9(4): e1001045. Published online 2011 Apr 12. doi: 10.1371/journal.pbio.1001045

Abstract: A scheme is proposed to implement reversible data hiding (RDH) in encrypted images using logistic substitution encoding. The original image is encrypted by the content owner using logistic substitution encoding. Then the data hider modifies the bits taken from the encrypted image to accommodate the secret data. On the receiver side, the secret data can be extracted if the receiver is available with the embedding key only. If the receiver has the encryption key only, original image can be recovered. If both the embedding and encryption keys are available at the receiver side, he/she can extract the secret data and recover the original image using logistic substitution decoding. The scheme is a good choice for secure image transmission.
Keywords: Image encryption, image recovery, image transmission, reversible data hiding.

[1]. W. Liu, W. Zeng, L. Dong, and Q. Yao, Efficient compression of encrypted grayscale images, IEEE Trans. Image Process., vol. 19, no. 4, pp. 1097–1102, Apr. 2010.
[2]. S. Lian, Z. Liu, Z. Ren, and H. Wang, Commutative encryption and watermarking in video compression, IEEE Trans. Circuits Syst.Video Technol., vol. 17, no. 6, pp. 774–778, Jun. 2007.
[3]. M. Johnson, P. Ishwar, V. M. Prabhakaran, D. Schonberg, and K. Ramchandran, On compressing encrypted data, IEEE Trans. Signal Process., vol. 52, no. 10, pp. 2992–3006, Oct. 2004.
[4]. W. Puech, M. Chaumont and O. Strauss, A reversible data hiding method for encrypted images, Proc. SPIE 6819, Security, Forensics, Steganography, and Watermarking of Multimedia Contents X, 68191E, Feb. 26, 2008, doi:10.1117/12.766754.
[5]. X. Zhang, Reversible data hiding in encrypted images, IEEE Signal Process. Lett., vol. 18, no. 4, pp. 255–258, Apr. 2011.

Abstract: In modern days an efficient detection of brain tumor is being breathtaking challenge in medical field. An Automatic segmentation of brain images has a significant role in lessening the burden of manual labelling and increasing the strength of brain tumor diagnosis. Magnetic resonance imaging (MRI) has a high spatial resolution view of brain and it is a very powerful tool used to diagnose a wide range of disorders and proven to be a highly flexible imaging technique. This paper presents a reliable detection method based on CNN that reduces operators and errors. The Convolutional Neural Network (CNN) is used in convolving a signal or an image with kernels to obtain feature maps............

Keywords: Brain tumor, Brain tumor segmentation, Convolutional Neural Network, Clustering Magnetic Resonance Imaging

[1]. S. Bauer et al., "A survey of MRI-based medical image analysis for brain tumor studies ," Phys. Med. Biol., vol. 58, no. 13, pp.97–129, 2013.
[2]. B. Menze et al., "The multimodal brain tumor image segmentation benchmark (BRATS)," IEEE Trans. Med. Imag., vol. 34, no.10, pp. 1993–2024, Oct. 2015
[3]. B. H. Menze et al., "A generative model for brain tumor segmentation in multi-modal images," in Medical Image Computing and Comput.- Assisted Intervention-MICCAI 2010. New York: Springer, 2010, pp. 151–159
[4]. S. Bauer, L.-P. Nolte, and M. Reyes, "Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization," in Medical Image Computing and Comput.-Assisted Intervention-MICCAI 2011. New York: Springer, 2011, pp. 354–361.
[5]. C.-H. Lee et al., "Segmenting brain tumors using pseudo-conditional random fields," in Medical Image Computing and Comput.-Assisted