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Modi, Hardik
- Implementation of Image in Painting Technique using Different Types of PDEs
Abstract Views :141 |
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Authors
Affiliations
1 Charotar University of Science and Technology, Changa-388421, Gujarat, IN
2 University of Tennessee at Chattanooga, TN, US
1 Charotar University of Science and Technology, Changa-388421, Gujarat, IN
2 University of Tennessee at Chattanooga, TN, US
Source
Digital Image Processing, Vol 4, No 14 (2012), Pagination: 792-796Abstract
The objective of this paper is to implement image inpainting technique using different types of PDEs. Image Inpainting is a technique of modifying an image in an undetectable form. Its most often used to repair an image, although it can easily be used to remove unwanted objects. The modification of images in a way that is non-detectable for an observer who does not know the original image is a practice as old as artistic creation itself. An effective technique for image inpainting has been developed based on partial differential equation (PDE). Instead of solving the problem in frequency domain, this rather new approach evaluates images in time domain. The basic concept starts from the impression of diffusion as a physical process and draws an analogy between the image inpainting process and the diffusion. Images can be comparable to heat, fluid, and gas which spontaneously move from the area of high concentration to the area of lower concentration. The PDEs operate in much the same way that trained restorers do: They propagate information from the structure around a hole into the hole to fill it in. We have implemented image inpainting technique using Heat Equation, Perona-Malik Equation and Cahn-Hillard Equation and compared generated results.
Keywords
Image In Painting, Partial Differential Equation (PDE) ,Laplacian Operator, Heat Equation, Perona- Malik Equation, Cahn-Hilliard Equation, Matlab.
- Comparative Study of Different Methods for Brain Tumor Extraction from MRI Images using Image Processing
Abstract Views :165 |
PDF Views:0
Authors
Neha Baraiya
1,
Hardik Modi
1
Affiliations
1 Charotar University of Science and Technology, Changa - 388421, Gujarat, IN
1 Charotar University of Science and Technology, Changa - 388421, Gujarat, IN
Source
Indian Journal of Science and Technology, Vol 9, No 4 (2016), Pagination:Abstract
Background/Objectives: The objective of this paper is to study various segmentation methods implemented using MATLAB and to compare accuracy of each. Statistical Analysis/Findings: Preprocessing is required for better segmentation, as it removes noise and makes images having equal attribute so that accuracy to segment can be increased. Segmentation using Thresholding, region based segmentation and watershed segmentation, all the methods are performed and Comparison of accuracy of all the methods has been calculated on basis of actual tumor part and segmented tumor part. Morphological operations are used in all the methods in order to avoid noise part of segmented image and to have higher accuracy. Accuracy of the three methods which are region based, thresholding and watershed are 87.48, 91.34 and 92.76 respectively. Here we have used all T2-weighted Magnetic Resonance Imaging (MRI) images as it is noninvasive technique and having high contrast between tumor and normal part. Application/Improvement: Segmented tumor with higher efficiency leads to help doctor in anatomy and pathology to classify tumor type so that treatment could be started accordingly as soon as possible.Keywords
Brain Tumor, Image Processing, Mri, Morphological Operations, Thresholding, Tumor ExtractionFull Text
- Comparative Analysis of Segmentation of Tumor from Brain MRI Images Using Fuzzy C-Means and K-Means
Abstract Views :185 |
PDF Views:2
Authors
Affiliations
1 Charotar University of Science and Technology, Changa - 388421, Gujarat, IN
1 Charotar University of Science and Technology, Changa - 388421, Gujarat, IN
Source
Fuzzy Systems, Vol 10, No 1 (2018), Pagination: 14-18Abstract
Background/Objectives: Main aim of this study is to test the advantages and failure of each under varying conditions and finally discover which algorithm is excellent in segmentation of tumor. Statistical Analysis/Findings: In this research work, FCM (Fuzzy C-Means) which is representative object based method and centroid based K-Means, the two important clustering algorithms clustering algorithms are compared, both the methods are clustering based methods. Comparison of both methods has been done in respect of computational cost and accuracy of segmented tumor. Here, testing of both the techniques over 35 images, and got accuracies as: 79.15% by FCM and 94.72% by k-means technique and calculated time elapsed by each technique which is same for all the images and that is: 0.019 second for k-mean and 0.027 second for FCM. It is the challenging task to accurately segment tumor region because of its unpredictable shape and appearance. Application/Improvement: The segmented image contains less but effective information, so ultimately for analysis one may need less memory space and time to process on image. Hence segmented image having high accuracy is considered to direct classification task instead of original brain MRI (Magnetic Resonance Imaging).Keywords
Clustering, Fuzzy C-Means, K-Means, Magnetic Resonance Imaging, Segmentation, Tumor.References
- . Performance Evaluation and Comparative Analysis of Proposed Image Segmentation Algorithm Rajiv Kumar1 and A. M. Arthanariee2
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- . Sasirekha, N., and K. R. Kashwan. "Improved segmentation of MRI brain images by denoising and contrast enhancement." Indian Journal of Science and Technology 2015, 8(22).
- . Baraiya N, and Modi. H. "Comparative Study of Different Methods for Brain Tumor Extraction from MRI Images using Image Processing."Indian Journal of Science and Technology (2016). 9 (4), pp. 1-5
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- Automated Irrigation Using IOT:A Comparative Technical Analysis
Abstract Views :197 |
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Authors
Affiliations
1 Department of Electronics & Communication, Charotar University of Science and Technology, Changa-388421, IN
1 Department of Electronics & Communication, Charotar University of Science and Technology, Changa-388421, IN
Source
Fuzzy Systems, Vol 10, No 1 (2018), Pagination: 19-23Abstract
Irrigation in the modern day high agricultural production demanding society plays a direct role in ensuring the balanced cycle of production versus demand. The productivity and the sustainability of balanced outcome can be better managed by implementation of automated control. The project here focuses on to implement a n automated control using certain threshold value ranges on the basis of data collected by the sensors and also provide the data reading to be checked by the farmer in order to realize the necessary details by the basis of any wireless communication system. In this paper, we are using various sensors like soil moisture sensor and humidity and feed the sensor output to the Arduino Uno through zigbee networking protocol. The Arduino processes on input data and actuates the motor driver and other reply to operate the motor which is interface with water pump. Using GSM system, the output is also display on the web server which provides all user control to act remotely. The data is also stored in memory which are utilize for drawing various graphs.Keywords
Internet of Things (IOT), Fuzzy System, Wi-Fi Routers, Arduino Uno, GSM/GPRS Module, Electromagnetic Valves, Zigbee Networking, PAN, Raspberry PI, ESP8266, etc.References
- . Kansara, Karan, Vishal Zaveri, Shreyans Shah, Sandip Delwadkar, and Kaushal Jani. "Sensor based Automated Irrigation System with IOT: A Technical Review." International Journal of Computer Science and Information Technologies 6, no. 6 (2015).
- . Sahu, Chandan Kumar, and Pramitee Behera. "A low cost smart irrigation control system." In Electronics and Communication Systems (ICECS), 2015 2nd International Conference on, IEEE, 2015, pp. 1146-1152.
- . Malge, Sangamesh, and Kalyani Bhole. "Novel, low cost remotely operated smart irrigation system." In Industrial Instrumentation and Control (ICIC), 2015 International Conference on, IEEE, 2015, pp. 1501-1505.
- . Zaier, Riadh, Slim Zekri, Hemanatha Jayasuriya, Abbas Teirab, Nabil Hamza, and Hamed Al-Busaidi. "Design and implementation of smart irrigation system for groundwater use at farm scale." In Modelling, Identification and Control (ICMIC), 2015 7th International Conference on, IEEE, 2015, pp. 1-6.
- . Srinivasa, K. G., Nabeel Siddiqui, and Abhishek Kumar. "ParaSense--A Sensor Integrated Cloud Based Internet of Things Prototype for Real Time Monitoring Applications." In Region 10 Symposium (TENSYMP), 2015 IEEE, IEEE, 2015, pp. 53-57.
- . Anagnostopoulos, Theodoros, Arkady Zaslavsy, Alexey Medvedev, and Sergei Khoruzhnicov. "Top--k Query Based Dynamic Scheduling for IoT-enabled Smart City Waste Collection." In Mobile Data Management (MDM), 2015 16th IEEE International Conference on, vol. 2, IEEE, 2015, pp. 50-55.
- . El-Bendary, N., Fouad, M.M.M., Ramadan, R.A., Banerjee, S. and Hassanien, A.E., 2013. Smart environmental monitoring using wireless sensor networks. Wireless Sensor Networks: From Theory to Applications; El Emary, IMM, Ramakrishnan, S., Eds, pp.731-755.
- . Nagothu, Sudheer Kumar. "Weather based Smart watering system using soil sensor and GSM." In Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave), World Conference on, IEEE, 2016, pp. 1-3.
- . Suba, G. Merlin, Y. M. Jagadeesh, S. Karthik, and E. Raj Sampath. "Smart irrigation system through wireless sensor networks." ARPN Journal of Engineering and Applied Sciences 10, no. 17 (2015).
- . Rane, Ms Deweshvree, P. G. Scholar-VLSI, and Sevagram BDCE. "Review paper based on automatic irrigation system based on RF module." PG Scholar-VLSI, Sevagram, Wardha, india, IJAICT, ISSN (2014): 2348-9928.
- . Raut, R., H. Varma, C. Mulla, and Vijaya Rahul Pawar. "Soil Monitoring, Fertigation, and Irrigation System Using IoT for Agricultural Application." In Intelligent Communication and Computational Technologies, Springer, Singapore, 2018, pp. 67-73.
- . D. H. Stefanov and Z. Bien, The Smart House for Older Persons and Persons with Physical Disabilities: Structure, Technology Arrangements, and Perspectives, IEEE Trans- actions On Neural Systems And Rehabilitation Engineering, Vol. 12, No. 2, June 2004, pp. 228-250.
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- . M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, and M. Zaharia, “A view of cloud computing,” Commun. ACM, vol. 53, no. 4, (2010), pp. 50– 58.
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- IoT Based Air Pollution and Garbage Monitoring System
Abstract Views :188 |
PDF Views:0
Authors
Affiliations
1 Department of Electronics & Communication, Charotar University of Science and Technology, Changa-388421, IN
1 Department of Electronics & Communication, Charotar University of Science and Technology, Changa-388421, IN
Source
Fuzzy Systems, Vol 10, No 2 (2018), Pagination: 40-44Abstract
The Internet on Things(IOT) offerings are changing urban communities by enhancing framework, making more proficient and financially savvy metropolitan administrations, improving open transportation, and keeping residents protected and more occupied with the group. Garbage creates unhygienic condition for the people and creates bad odor around the surroundings. It is detrimental some deadly diseases & human illness. Air pollution is the world’s deadliest environmental problem. The main source of air pollution happens due to vehicles. It affects men, animals, plants, forests, and also has a solids effect on atmosphere. It is detrimental to human health causing major respiratory disorders. In Previous years in city all Trash can is full or not is check by municipal worker manually and in air pollution there are measure harmful gases are separately to avoid all such situations I am going to implement a project called IOT Based Air Pollution & Garbage Monitoring system in which i will monitor the Air Quality and garbage detection over a web server using internet and use MQ-135 gas Sensor is measure all gases like CO2, smoke, alcohol, NH3 and benzene.Keywords
Internet on Things (IOT), Fuzzy System, Garbage Control, Air Pollution, MQ-135 Gas Sensor, Ultrasonic Sensor.References
- . Khodve, Shilpa R., and A. N. Kulkarni. "Web Based Air Pollution Monitoring System (Air Pollution Monitoring Using Smart Phone)." International Journal of Science and Research (IJSR) 5, no. 3 (2016), pp. 266-269.
- . Rushikesh, Ramagiri, and Chandra Mohan Reddy Sivappagari. "Development of IoT based vehicular pollution monitoring system." In Green Computing and Internet of Things (ICGCIoT), 2015 International Conference on, IEEE, 2015, pp. 779-783.
- . Montanaro, Teodoro, Fulvio Corno, Carmelo Migliore, and Pino Castrogiovanni. "SmartBike: an IoT Crowd Sensing Platform for Monitoring City Air Pollution." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 6 (2017), pp. 3602-3612.
- . Dev, Abhishek, Maneesh Jasrotia, Muzammil Nadaf, and Rushabh Shah. "IoT Based Smart Garbage Detection System." International research journal of engineering and technology (IRJET) 3 (2016).
- . Kumar, N. Sathish, B. Vuayalakshmi, R. Jenifer Prarthana, and A. Shankar. "IOT based smart garbage alert system using Arduino UNO." In Region 10 Conference (TENCON), 2016 IEEE, IEEE, 2016, pp. 1028-1034.
- . Bharadwaj, Abhay Shankar, Rainer Rego, and Anirban Chowdhury. "IoT based solid waste management system: A conceptual approach with an architectural solution as a smart city application." In India Conference (INDICON), 2016 IEEE Annual, IEEE, 2016, pp. 1-6.
- . Srinivasa, K. G., Nabeel Siddiqui, and Abhishek Kumar. "ParaSense--A Sensor Integrated Cloud Based Internet of Things Prototype for Real Time Monitoring Applications." In Region 10 Symposium (TENSYMP), 2015 IEEE, IEEE, 2015, pp. 53-57.
- . Anagnostopoulos, Theodoros, Arkady Zaslavsy, Alexey Medvedev, and Sergei Khoruzhnicov. "Top--k Query Based Dynamic Scheduling for IoT-enabled Smart City Waste Collection." In Mobile Data Management (MDM), 2015 16th IEEE International Conference on, vol. 2, IEEE, 2015, pp. 50-55.
- . Yadav, Shambala S. Salunkhe Madhuri D., and Vrushali V. Kulkarni. "IOT Based Waste Monitoring For Smart City." International Journal Of Engineering And Computer Science 6, no. 4 (2017).
- . Wang, H. "Toward a Green Campus with the Internet of Things-the Application of Lab Management." world 7 (2013): 8.
- . El-Bendary, N., Fouad, M.M.M., Ramadan, R.A., Banerjee, S. and Hassanien, A.E., 2013. Smart environmental monitoring using wireless sensor networks. Wireless Sensor Networks: From Theory to Applications; El Emary, IMM, Ramakrishnan, S., Eds, pp.731-755.
- . R. Nagarajan and R. Dhanasekaran, "Implementation of wireless data transmission in monitoring and control," 2013 International Conference on Communication and Signal Processing, Melmaruvathur, 2013, pp. 83-87.
- . Hao Chen, Xueqin Jia and Heng Li, "A brief introduction to IoT gateway," IET International Conference on Communication Technology and Application (ICCTA 2011), Beijing, 2011, pp. 610-613.
- . Gong, Chunye, et al. "The characteristics of cloud computing." 2010 39th International Conference on Parallel Processing Workshops. IEEE, 2010.
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- . L. Atzori, A. Iera, and G. Morabito, “From “smart objects” to “social objects”: The next evolutionary step of the internet of things,” Communications Magazine, IEEE, vol. 52, 2014, pp. 97-105.
- Wireless Robot Interaction Based on Gesture Identification
Abstract Views :185 |
PDF Views:2
Authors
Affiliations
1 Charotar University of Science and Technology, Changa - 388421, Gujarat, IN
1 Charotar University of Science and Technology, Changa - 388421, Gujarat, IN
Source
Fuzzy Systems, Vol 10, No 3 (2018), Pagination: 64-67Abstract
In this digital world human-machine interaction and synchronization is becoming extensive and increased a lot. So, with the new technologies reduced the gap between human and machine to relieve the standard of living. Gesture has played a vital role in deteriorating this gap. This paper is contract with intention and functioning of “WIRELESS ROBOT INTERACTION BASED ON GESTURE IDENTIFICATION” using accelerometer. It controlled with hand gesture and wirelessly controls robot using accelerometer with a small low cost. The algorithm for gesture identification has been developed to substitute the method of usual controlling procedure of robots by way of button etc.Keywords
Gesture, Accelerometer, Robotics, Micro-Controller.References
- . Goodrich, Michael A., and Alan C. Schultz. "Human-robot interaction: a survey." Foundations and trends in human-computer interaction 1, no. 3 (2007): 203-275.
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- . AJANI, Mohit K.; DETHADIYA, Bhavin R.; MODI, Hardik P. Implementation of Different Types of Patterns using Propeller Display through Microcontroller. Programmable Device Circuits and Systems, [S.l.], v. 4, n. 8, p. 375-378, Jun. 2012. ISSN 0974 – 9624. Available at: . Date accessed: 10 Feb. 2018.
- . Kang, Seong-Pal, and Jayantha Katupitiya. "A hand gesture controlled semi-autonomous wheelchair." In Intelligent Robots and Systems, 2004.(IROS 2004). Proceedings. 2004 IEEE/RSJ International Conference on, vol. 4, pp. 3565-3570. IEEE, 2004.
- . Goyal, Diksha, and Dr SPS Saini. "Accelerometer based hand gesture controlled wheelchair." International Journal on Emerging Technologies 4, no. 2 (2013): 15-20.
- . Bhuiyan, Moniruzzaman, and Rich Picking. "Gesture-controlled user interfaces, what have we done and what’s next." In Proceedings of the Fifth Collaborative Research Symposium on Security, E-Learning, Internet and Networking (SEIN 2009), Darmstadt, Germany, pp. 25-29. 2009.
- . Wachs, Juan Pablo, Mathias Kölsch, Helman Stern, and Yael Edan. "Vision-based hand-gesture applications." Communications of the ACM 54, no. 2 (2011): 60-71.
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- . Qian, Kun, Jie Niu, and Hong Yang. "Developing a gesture based remote human-robot interaction system using Kinect." International Journal of Smart Home 7, no. 4 (2013).
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- . AccelarometerAvailable:http://www.analog.com/en/products/mems/accelerometers/adxl335.html#product-overview
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- . Kaura, Harish Kumar, Vipul Honrao, Sayali Patil, and Pravish Shetty. "Gesture controlled robot using image processing." Int. J. Adv. Res. Artif. Intell 2, no. 5 (2013): 69-77
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- Fingerprint Based Vehicle Ignition System
Abstract Views :178 |
PDF Views:1
Authors
Affiliations
1 Department of Electronics & Communication, Charotar University of Science and Technology, Changa-388421, IN
1 Department of Electronics & Communication, Charotar University of Science and Technology, Changa-388421, IN
Source
Fuzzy Systems, Vol 10, No 3 (2018), Pagination: 68-72Abstract
Biometric systems have overtime served as robust security mechanisms in various domains. Fingerprints would those most seasoned What's more the vast majority broadly utilized manifestation for biometric distinguishing proof. A basic venture in exploring its preferences is will embrace it for utilization Similarly as An type for security done now existing systems, for example, such that vehicles. This Examine worth of effort concentrates on the utilization of fingerprints to vehicle ignition, concerning illustration contradicted of the customary system for utilizing keys. Those model framework Might a chance to be isolated under the taking after modules: finger impression dissection programming module that acknowledges fingerprints images; fittings interface module and the igniter module. The finger impression distinguishment product empowers fingerprints from claiming substantial clients of the vehicle will make selected previously, a database. In front of whatever client cam wood lighted the vehicle, his/her finger impression picture may be matched against the fingerprints in the database same time clients with no match in the database are kept starting with igniting those vehicle. Control for those igniter of the vehicle may be attained toward sending proper signs of the parallel port of the PC Also consequently of the interface control circuit. This approach would be fruitful to users who want to possess valid and authenticated entry.Keywords
Fingerprint Sensor, Fuzzy Control System, LCD, Microcontroller, Relay Motor, Scanner.References
- . Omidiora, E. O., O. A. Fakolujo, O. T. Arulogun, and D. O. Aborisade. "A Prototype of a Fingerprint Based Ignition Systems in Vehicles." European Journal of Scientific Research62, no. 2 (2011), pp. 164-171.
- . Schwaighofer, Anton. "Sorting it out: Machine learning and fingerprints." Special Issue on Foundations of Information Processing of TELEMATIK 1 (2002), pp.18-20.
- . Engdahl, Tomi. "Parallel port interfacing made easy." (2005).
- . Graevenitz, G. A. "Introduction to fingerprint technology." A&S International 53 (2003), pp. 84-86.
- . Ito, Koichi, Ayumi Morita, Takafumi Aoki, Hiroshi Nakajima, Koji Kobayashi, and Tatsuo Higuchi. "A fingerprint recognition algorithm combining phase-based image matching and feature-based matching." In International Conference on Biometrics, Springer, Berlin, Heidelberg, 2006, pp. 316-325.
- . Omidiora, E. O. "A prototype of knowledge-based system for black face recognition using principal component analysis and fisher discriminant algorithms." Unpublished Ph. D Thesis, Department of Computer Science and Engineering, Ladoke Akintola University of Technology, Ogbomoso, Nigeria (2006)..
- . Omidiora, E. O., O. A. Fakolujo, O. T. Arulogun, and D. O. Aborisade. "A Prototype of a Fingerprint Based Ignition Systems in Vehicles." European Journal of Scientific Research62, no. 2 (2011), pp. 164-171.
- . Gill, Kiran Rana, and Joel Sachin. "Vehicle Ignition using Fingerprint Sensor."
- . Prashantkumar, R., V. C. Sagar, S. Santosh, and Siddharth Nambiar. "Two wheeler vehicle security system." International Journal of Engineering Sciences & Emerging Technologies 6, no. 3 (2013), pp. 324-334.
- . Bhatt, Bhumi, Purvi Kalani, Nayanaben Parmar, and Nikunj Shingala. "Smart Vehicle Security System Using GSM & GPS." International Journal of Engineering and Computer Science 4, no. 06 (2015).
- . Ratha, Nalini K., Shaoyun Chen, and Anil K. Jain. "Adaptive flow orientation-based feature extraction in fingerprint images." Pattern Recognition 28, no. 11 (1995), pp.1657-1672.
- . Farina, Alessandro, Zsolt M. Kovacs-Vajna, and Alberto Leone. "Fingerprint minutiae extraction from skeletonized binary images." Pattern recognition 32, no. 5 (1999), pp. 877-889.
- . Jain, Anil, Lin Hong, and Ruud Bolle. "On-line fingerprint verification." IEEE transactions on pattern analysis and machine intelligence 19, no. 4 (1997), pp. 302-314.
- . Maio, Dario, and Davide Maltoni. "Direct gray-scale minutiae detection in fingerprints." IEEE transactions on pattern analysis and machine intelligence 19, no. 1 (1997), pp. 27-40.
- . Washington, V.L., Driver Id Llc, 1997. Vehicle security system including fingerprint and eyeball part identification. U.S. Patent 5, 686,765.
- Motion Based Home Automation System
Abstract Views :168 |
PDF Views:2
Authors
Affiliations
1 Department of Electronics & Communication, Charotar University of Science and Technology, Changa-388421, IN
1 Department of Electronics & Communication, Charotar University of Science and Technology, Changa-388421, IN
Source
Fuzzy Systems, Vol 10, No 3 (2018), Pagination: 73-77Abstract
Home security is essential for occupant’s convenience and protection. At entry point the system should secured this is the main purpose to design this system .This paper aims to develop a low-cost means of home security system using sensors like motion sensor, PIR sensor etc. In this paper, The of temperature sensor is used to sensed the room temperature and peripherals likes fan, bulb, etc. are used to demonstrate the temperature control. The keypad is used to feed the password for system authentication. The light dependent resistor (LDR) resistor is used to measure the intensity of the light and based on that the mini bulb is controlled automatically. The motion sensor is used to detect the motion in room and automatically control the bulb. Thus the system ensures home security and automation using the various sensors.
Keywords
PIR Sensor, LDR Sensor, Fuzzy System LCD, Microcontroller, Relay, Motor.References
- . Amit Narote, Abhishek Dsilva, Selma Misquitta and Palak Khandelwal (2015), Smart home automation using arm11, International Journal of Current Engineering and Technology, vol. 5, pp. 472-476.
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