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Jayanthy, S.
- Li-Fi Technology based Fleet Vanguardand Security
Abstract Views :186 |
PDF Views:0
Authors
Affiliations
1 ME Embedded System Technologies, Sri Ramakrishna Engineering College, Coimbatore – 641022, Tamil Nadu, IN
2 Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, Coimbatore – 641022, Tamil Nadu, IN
1 ME Embedded System Technologies, Sri Ramakrishna Engineering College, Coimbatore – 641022, Tamil Nadu, IN
2 Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, Coimbatore – 641022, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 11 (2016), Pagination:Abstract
Objectives: This paper ensures to enhance the quality of Intelligent Transportation System by combining the technologies of GPS and Li-Fi to provide an effective vehicle communication and protection between the fleet of vehicles. Using Li-Fi the data stream is transmitted in form of bits through visible light. Methods: A low power microcontroller TI MSP430F5529 is interfaced with Li-Fi module through which data such as speed and direction of vehicle is transferred between the leading and following vehicle. In case of vehicle goes out of range, the location of the vehicle is informed using GPS and GSM technology to the leading vehicle and the base station. This Li-Fi communication happens between the vehicles at high speed while the urgent messages can be transferred through the GSM module which overcomes the RF band limitation. Findings: In our prototype the usage of LEDs yields high speed data transmission, improves energy savings and requires less maintenance and also the Li-Fi technology is safer as it eliminates the harmful radiation intrusion. High directional communication ensures good message integration with ideal reachability and reduced latency. Applications: As the designed module combines the illumination, communication and tracking purposes together, the operating costs of the vehicles is highly reduced. Our prototype will provide a reliable and anKeywords
Fleet, LED, Li-Fi, Phototransistor, Vehicle Communication, Visible LightFull Text
- ARM Based Security System Using Linear Discriminant Analysis
Abstract Views :199 |
PDF Views:0
Authors
Affiliations
1 Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, IN
1 Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, IN
Source
ICTACT Journal on Microelectronics, Vol 3, No 3 (2017), Pagination: 417-424Abstract
This paper presents an ARM Based Security system using facial recognition Techniques. Also a comparison analysis between Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Linear Discriminant Analysis (LDA) algorithms for facial recognition implemented in ARM Cortex M4 processor is done. A training database is created with images of all the authenticated users. The image of the user, whose identity is to be authorized, is captured using a webcam and the ARM microcontroller processes the algorithms to convert the images into vectors and components which are then compared with the images existing in the training database. Results are displayed in a LCD. To compare the performance of the algorithms ORL image database is used. The performance parameters used for comparison are Recognition rate and Cumulative Match score (CMS). The experimental results indicate that LDA algorithm outperforms in terms of recognition accuracy and CMS and gives best results under different illumination conditions, various expressions and poses. It can also be observed that the execution time decreases drastically when executed in ARM Cortex M4 microcontroller compared to execution in MATLAB.Keywords
Facial Recognition, Linear Discriminant Analysis, ARM Cortex M4 Microcontroller, Execution Time, Recognition Accuracy.
References
- Adnan Affandi, Mohammed Awedh, Mubashshir Husain and Ahmed Alghamdi, “RFID and Face Recognition based Security and Access Control System”, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 2, No. 11, pp. 27-34, 2013.
- Michel Owayjan, Amer Dergham, Gerges Haber, Nidal Fakih, Ahmad Hamoush, Elie Abdo, “Facial Recognition Security System”, Proceedings of International Conference on Electrical, Electronics and Optimization Techniques, pp. 75-81, 2016
- C.A. Athira, Hashlin P Jose, T. Jini John and Aswathi Wilson, “Security Alert using Face Recognition”, International Journal of Advances in Computer Science and Technology, Vol. 5, No. 12, pp. 176-179, 2016.
- J. Shankar Kartik, K. Ram Kumar and V.S. Srimadhavan, “Security System with Face Recognition and Embedded Network Video Monitoring Terminal”, International Journal of Security, Privacy and Trust Management, Vol. 2, No. 5, pp. 53-59, 2015.
- Sameerchand Pudaruth, Faugoo Indiwarsingh and Andrakant Bhugun, “A Unified Intrusion Alert System using Motion Detection and Face Recognition”, Proceedings of 2nd International Conference on Machine Learning and Computer Science, pp. 17-20, 2013.
- Chathunika Gamage and Lasantha Seneviratne, “Development of a Learning Algorithm for Facial Recognition under Varying Illumination”, Proceedings of International Conference on Information and Automation for Sustainability, pp. 119-126, 2014.
- Chengyun Liu, Zhenxue Chen, Faliang Chang and Kaifang Wang, “Face Recognition Algorithm based on Improved Facial Model”, Proceedings of International Conference on Natural Computation, pp. 927-931, 2014
- Maria Rosario D. Rodavia and Orlando Bernaldez, “Web and Mobile based Facial Recognition Security System using Eigenfaces Algorithm”, Proceedings of IEEE International Conference on Teaching, Assessment, and Learning for Engineering, pp. 86-92, 2016.
- Sizhi Zhong, Youguang Chen and Shuchun Liu, “Facial Recognition using Local Feature Selection and Extended Nearest Neighbour Algorithm”, Proceedings of International Conference on Computational Intelligence and Design, pp. 328-331, 2014.
- Chaoqun Jing Tian and Liang Qijun Lei, “Camera Security Network Design and Realization based on PCA Facial Recognition Algorithms”, Proceedings of IEEE International Conference on Electronic Information and Communication Technology, pp. 68-75, 2016.
- Ala Eldin Omer and Adil Khurran, “Facial Recognition using Principal Component Analysis based Dimensionality Reduction”, Proceedings of International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering, pp. 73-78, 2015.
- J. Ye, R. Janardan and Q. Li, “Two-Dimensional Linear Discriminant Analysis”, Proceedings of International Conference on Advances in Neural Information Processing Systems, pp. 1569-1576, 2004.
- Riddhi A. Vyas and M. Shah, “Comparison of PCA and LDA Techniques for Face Recognition Feature based Extraction with Accuracy Enhancement”, International Research Journal of Engineering and Technology, Vol. 4, No. 6, pp. 3332-3336, 2017.
- Narpat A. Singh, Manoj B. Kumar and Manju C. Bala, “Face Recognition System based on SURF and LDA Technique”, International Journal Intelligent Systems and Applications, Vol. 2, pp. 13-19, 2016.
- A High Quality Embedded System for Assessing Food Quality using Histogram of Oriented Gradients
Abstract Views :375 |
PDF Views:1
Authors
Affiliations
1 Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, IN
1 Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, IN
Source
ICTACT Journal on Soft Computing, Vol 9, No 1 (2018), Pagination: 1781-1787Abstract
A low cost high quality system for accessing quality of food samples by finding the presence of fungus is proposed. Most of the food items kept for long intervals will have fungal infection in them. The proposed system uses Histogram of Oriented Gradients algorithm along with Support Vector Machine classifier to detect the presence of fungus. The features of the food samples captured in real time using a webcam are extracted using Histogram of Oriented Gradients algorithm. The extracted features are given to SVM classifier which compares these features with the trained one and displays the quality of food samples. The algorithms are implemented using ARM Cortex A-53 processor. Experimental results indicate that very good sensitivity and specificity is obtained and the execution time of the algorithms implemented in ARM processor is much lesser compared to the results obtained using MATLAB software.Keywords
Accuracy, Fungus Detection, Histogram of Oriented Gradients, OpenCV, Support Vector Machine.References
- M.W. Tahir et al., “Detection of Fungus through an Optical Sensor System using the Histogram of Oriented Gradients”, IEEE Sensors, Vol. 17, No. 16, pp. 5341-5349, 2017.
- J. Zhang, “Automatic Identification of Fungi in Microscopic Leucorrhea Images”, Journal of the Optical Society of America A, Vol. 34, pp. 1484-1489, 2017.
- S. Gunasekaran, “Computer Vision Technology for Food Quality Assurance”, Trends in Food Science and Technology, Vol. 7, pp. 245-256, 1996.
- V. Chelladurai et al., “Thermal Imaging for Detecting Fungal Infection in Stored Wheat”, Journal of Stored Products Research, Vol. 46, No. 3, pp. 174-179, 2010.
- J.D. Tallada et al., “Detection of Fungus Infected Corn Kernels using Near Infrared Reflectance Spectroscopy and Colour Imaging”, Grain Marketing and Production Research Center, Vol. 54, No. 3, pp. 1151-1158, 2011.
- E.A. Bauriegel et al., “Early Detection of Fusarium Infection in Wheat using Hyper-Spectral Imaging, Computers and Electronics in Agriculture, Vol. 75, No. 2, pp. 304-312, 2011.
- U. Siripatrawan et al., “Rapid Detection of Escherichia Coli Contamination in Packaged Fresh Spinach using Hyperspectral Imaging”, Talanta, Vol. 85, No. 1, pp. 276-281, 2011.
- G. Firrao et al., “Prediction of Milled Maize Fumonisin Contamination by Multispectral Image Analysis”, Journal of Cereal Science, Vol. 52, pp. 327-330, 2010.
- A. Bhargava, “Fruits and Vegetables Quality Evaluation using Computer Vision: A Review”, Journal of King Saud University, Computer and Information Sciences, 2018.
- G. Rajakumar, T. Ananth Kumar, T.S. Arun Samuel and E. Muthu Kumaran, “IOT Based Milk Monitoring System for Detection of Milk Adulteration”, International Journal of Pure and Applied Mathematics, Vol. 118, No. 9, pp. 21-32, 2018.
- Aman Naimat, “2018 could be the Year AI Kills Spam and Redefines Data Science”, Available at: https://venturebeat.com/2018/04/01/food-and-beveragecpgenterprise-ai//,2018.
- Adrie J.M. Beulens et al., “Possibilities for Applying Data Mining for Early Warning in Food Supply Networks”, Available at: ftp://ftp.iiasa.ac.at/pub/mda/Pubs/csm06/pres/beulens_pap.pdf.