A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Barman, Bandana
- Report on 7th Nabanita-Satish Chandra Biswas Memorial Lecture 2020 and 4th Dr. Murali Mohan Biswas Memorial Lecture 2020 held on 26th September 2020
Authors
1 EC Member, ISEC, IN
Source
Indian Science Cruiser, Vol 35, No 1 (2021), Pagination: 63-64Abstract
No Abstract.- Report on Celebration of World Space Week, 2020 on October 10 2020
Authors
1 EC Member, ISEC, IN
Source
Indian Science Cruiser, Vol 35, No 1 (2021), Pagination: 64-65Abstract
No Abstract.- Report of National Webinar, “National Science Day” held on 28th February, 2021
Authors
1 Assistant Secretary, ISEC, IN
2 Secretary, ISEC, IN
Source
Indian Science Cruiser, Vol 35, No 5 (2021), Pagination: 57-58Abstract
No Abstract.Keywords
No Keywords.- Report of the Webinar on “Virtual Water”
Authors
1 Assistant Secretary, ISEC, IN
Source
Indian Science Cruiser, Vol 35, No 5 (2021), Pagination: 59-59Abstract
No Abstract.Keywords
No Keywords.- Report of "8th Nabanita-Satish Chandra Biswas Memorial Lecture 2021 and 5th Dr. Murali Mohan Biswas Memorial Lecture 2021" held on 10th November 2021
Authors
1 Assistant Secretary, ISEC, IN
Source
Indian Science Cruiser, Vol 35, No 6 (2021), Pagination: 62-64Abstract
No Abstract.
Keywords
No Keywords.- Report of the Two-Day National Seminar on “Role of Women Scientists in Developing Science and Society in India” Held on January 13-14 2020
Authors
1 Assistant Secretary, ISEC, IN
2 Secretary, ISEC, IN
Source
Indian Science Cruiser, Vol 35, No 4 (2021), Pagination: 60-64Abstract
No Abstract.Keywords
No Keywords.- Real Time Leaf Disease Detection Using Deep Learning Method
Authors
1 Kalyani Government Engineering College, Nadia, West Bengal, IN
Source
Indian Science Cruiser, Vol 35, No 4 (2021), Pagination: 28-33Abstract
Due to regular occurrences of hot and humid climate of the country, crops are destroyed by invasion of certain diseases. As a result, entire farm gets affected and huge loss and damage happens for the farmers. This paper focuses on developing a system which detects at the onset of any disease by continuous monitoring of leaves. In addition, a moisture measuring device is also fitted which allows the microcontroller to spray water from a tank whenever there is a shortage. Secondly, leaf disease detection system achieved by deep learning, also instruct a second microcontroller to spray desired amount of pesticide as and where required. A web application made for this also instructs farmers what should be their next procedure whenever a certain disease is detected.The accuracy ofmodel is 94% when trained and tested on leaf dataset.Keywords
CNN, Arduino IDE, Moister Sensor, OpenCV, Leaf Disease.References
- C H Chavan and P V Karande, Wireless Monitoring of Soil Moisture, Temperature & Humidity Using Zigbee in Agriculture, International Journal of Engineering Trends and Technology, Vol 11, No 10, page 493-497, 2014.
- Labidi, A Chouchaine and A K Mami, Control of Relative Humidity Inside an Agricultural Greenhouse, 18th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), page 109-114, 2017. doi: 10.1109/STA.2017.8314955
- B N Getu and H A Attia, Automatic Control of Agricultural Pumps Based on Soil Moisture Sensing, AFRICON-2015, page 1-5, 2015. doi: 10.1109/AFRCON.2015.7332052
- W Zhuang, J Zhi and L G Hong, Temperature and Humidity Measure-Control System Based on CAN and Digital Sensors, International Forum on Information Technology and Applications, page 548-550, 2009. doi: 10.1109/IFITA.2009.126
- R Romero, J L Muriel, I García and D M de la Peña, Research on Automatic Irrigation Control: State of the Art and Recent Results, Agricultural Water Management, Vol 114, page 59-66, 2012.
- J Rhee, I Jungho and C Gregory, Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data, Remote Sensing of Environment. Vol 114, page 2875-2887, 2010. https://doi.org/10.1016/j.rse.2010.07.005
- E Olakunle, A Rahman, T Orikumhi, I Leow, C Yen and H Mohammad, An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges, IEEE Internet of Things Journal, page 1, 2018. https://doi.org/10.1109/JIOT.2018.2844296
- W Ning, N Zhang and M Wang, Wireless Sensors in Agriculture and Food Industry—Recent Development and Future Perspective, Computers and Electronics in Agriculture, Vol 50, Page 1-14, 2006. https://doi.org/10.1016/j.compag.2005.09.003
- M Mekala and P Viswanathan, A Novel Technology for Smart Agriculture Based on IoT with Cloud Computing, page 75-82, 2017. https://doi.org/10.1109/I-SMAC.2017.8058280
- R Mundada and V Gohokar, Detection and Classification of Pests in Greenhouse Using Image Processing, IOSR J. Electr. Commun. Engg, Vol 5, page 57-63, 2013. https://doi.org/10.9790/2834-565763
- G Bhadane, S Sharma and V B Nerkar, Early Pest Identification in Agricultural Crops Using Image Processing Techniques, International Journal of Electrical, Electronics and Computer Engineering, Vol 2, No 2, page 77-82, 2013.
- A Saeed, N Adnan and S A Basit, Pest Detection and Control Techniques Using Wireless Sensor Network: A Review, Journal of Entomology and Zoology Studies, Vol 3, page 92-99, 2015.
- J Peng, X Hongbo, H Zhiye and W Zheming, Design of a Water Environment Monitoring System Based on Wireless Sensor Networks, Sensors, Vol 9, No 8, page 6411-6434, 2009. https://doi.org/10.3390/s90806411
- Development of a System Model for Home Automation
Authors
1 Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani- 741235, IN
Source
Journal of the Association of Engineers, India, Vol 86, No 3-4 (2016), Pagination: 45-52Abstract
Home automation system introduces a computerization or automation of some electrical and electronic systems. This system includes complete automation of lights, temperature control, door security, etc. In this paper, a hardware system is developed and installed to monitor and control various home appliances. It can control the appliances based on its configuration. This system automatically turns on lights at a specified time in the evening. It can measure light intensity in a room by using hardware sensor and turn on bulb when light intensity becomes low. The system can allow a person to control appliances from a remote location by internet.Keywords
Home Automation, Arduino Uno Board, Relay, LCD Monitor, Keypad, Servo Motor, IR Sensor, Light Sensor, Temperature Sensor, Wires.References
- Javale, D., Mohsin, M., Nandanwar, S. and Shingate, M., Home Automation and Security System Using Android ADK, International Journal of Electronics Communication and Computer Technology (IJECCT), 2013, Vol. 3(2), pp.382-385.
- Kovatsch, M., Weiss, M. and Guinard, D., Embedding Internet Technology for Home Automation, In the proceedings of IEEE conference on Emerging Technologies and Factory Automation (ETFA), 2010, DOI:10.1109/ETFA.2010.5641208, pp.1-8.
- Jivani, M.N., GSM Based Home Automation System Using App-Inventor for Android Mobile Phone, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2014, Vol. 3(9), DOI:10.15662/ijareeie.2014.0309042, pp.12121-12128.
- Madsen, T.M., Home Automation Systems Integration, Software Engineering Master Thesis, Department of Computer Science, Aalborg University, Spring, 2010.
- Kaur, I., Microcontroller Based Home Automation System with Security, International Journal of Advanced Computer Science and Applications, Vol.1 (6), pp.60-65, 2010.
- Castro, J. and Psota, J., The Specification, Design, and Implementation of a Home Automation System, Introductory Digital Systems Laboratory Final Project, 2004, Massachusetts Institute of Technology, pp.1-25.
- Naqvi, S.H.R., Raja, S.M.T., Khan, M.U.A. and Athar, T., Home Automation via Bluetooth (Using Android Platform), pp.1-16, 2011.
- Panth, S. and Jivani, M., Home Automation System (HAS) using Android for Mobile Phone, International Journal of Electronics and Computer Science Engineering, International Journal of Electronics and Computer Science Engineering, 2014,Vol. 3(1), pp. 1-11.
- Kyas, O., How To Smart Home: A Step by Step Guide Using Internet, Z-Wave, KNX & Open Remote: A Key Concept Book, Key Concept Press, 2013.
- Palaniappan, S., Hariharan, N., Kesh, N.T., Vidhyalakshimi, S. and Deborah, S.A., Home Automation Systems - A Study, International Journal of Computer Applications,2015, Vol. 116(11), pp. 11-18.
- Arul, S. B., Wireless Home Automation System Using Zigbee, International Journal of Scientific & Engineering Research,2014, Vol. 5( 12), pp. 133-138.
- Ramlee, R.A., Leong, M.H., Singh, R.S.S., Ismail, M.M., Othman, M.A., Sulaiman, H.A., Misran, M.H. and Said, M.A.M., Bluetooth Remote Home Automation System Using Android Application, The International Journal of Engineering And Science (IJES), 2013, Vol. 2(1), pp. 149-153.
- Report of Celebration of National Mathematics Day with the theme, “From Statistics to Machine Learning” held on 22nd December 2021
Authors
1 Assistant Secretary, ISEC, IN
Source
Indian Science Cruiser, Vol 36, No 1 (2022), Pagination: 55-57Abstract
No Abstract.Keywords
No Keywords.- Metabolic Pathway of Hereditary Cancer Disease from PPI-network of DEGS Detected using Mean-of-Mean Method
Authors
1 Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani-741235, Nadia, West Bengal, IN
Source
Reason-A Technical Journal (Formerly Reason-A Technical Magazine), Vol 19 (2020), Pagination: 64-80Abstract
Uncontrolled growth of cells often results Cancer disease in human body. When it is eventually transmitted throughout the generations in a family, it is referred to as hereditary diseases. Metabolic pathway explains several chemical reactions occurred for growth of a disease and KEGG pathway analysis identifies key genes involved with that disease. At first, differentially expressed genes (DEGs) are detected from cancer gene microarray time series datasets using a new and simple method, Mean of Mean (MoM). The MoM concept is developed from Gregor Johann Mendel's First Law of Heredity or Segregation rule of heredity. Highly expressed (HG) and lowly expressed (LG) genes in two different groups of microarray dataset are identified first using MoM. Then DEGs are found by implementing intersection operation between HG and LG genes. Performance of MoM method is analyzed by Support Vector Machine classifiers (SVMs) on some binary class cancer microarray data samples. Then all results are compared with the performance of other statistical parametric and nonparametric hypothetic tests. It is noticed that performance ofMoMis better than other statistical methods in almost all data sets. Finally, Protein-Protein Interaction Networks (PPINs) are constructed within identified DEGs using web based tool. Lastly, KEGG pathway analysis is performed for all proteins involved in PPINs to obtain list of key genes for growth of cancer disease.Keywords
Hereditary Disease, Cancer, Mean of Mean (MoM), DEGs, SVM Classifier, PPI-Networks, Metabolic Pathway, KEGG Pathway.References
- Bandyopadhyay, S., Mallik, S. and Mukhopadhyay, A., A Survey and Comparative Study of Statistical Tests for Identifying Differential Expression from Microarray Data, IEEE/ACM Transactions on Computational Biology and Bioinformatics, IEEE ACM, Vol. 11, No.1, 2013. DOI: 10.1109/TCBB.2013.147.
- Burges, C.J.C., A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery, Vol. 2, pp.121–167, 1998.
- Chen, Z., Liu, J., Ng, H.K.T., Nadarajah, S., Kaufman, H.L., Yang, J.Y. and Deng, Y., Statistical Methods on Detecting Differentially Expressed Genes for RNA-Seq Data, BMC Systems Biology, Vol. 5, No.S1, 2011. DOI: 10.1186/1752-0509-5-S3-S1.
- Dudoit, S., Yang, Y.H. Callow, M.J. and Speed, T.P. , Stat ist ical Methods for Identifying Differentially Expressed Genes in Replicated cDNA Microarray Experiments, Statistica Sinica, Vol. 12, No.1, pp.111-139, 2002.
- Ma, P., Zhong, W. and Liu, J.S., Identifying Differentially Expressed Genes in Time Course Microarray Data, Statistics in Biosciences, Vol. 1, No.144, 2009.
- Heydebreck,A.V., Statistical Tests for Differential Gene Expression,Technical Report.
- Lin, C.J., Support Vector Machines for Data Classification, Technical Report, National Taiwan University, February 9, 2004.
- Markowetz, F., Classification by Support Vector Machines, Technical Report, Max-Planck-Institute for Molecular Genetics, Berlin, 2003.
- Moore, A.W., Cross-Validation for Detecting and Preventing Overfitting, Technical report, Carnegie Mellon University. http://www.cs.cmu.edu/~awm/tutorials.
- Mukherjee, S., Classifying Microarray Data Using Support Vector Machines, In: Berrar, D.P., Dubitzky,W. and Granzow, M. (Eds.) A Practical Approach to Microarray Data Analysis, Springer. DOI: 10.1007/0-306-47815-3_9
- Ospina, L. and Kleine, L.L., Identification of Differentially Expressed Genes in Microarray Data in a Principal Component Space, Springer Plus, Vol. 2, No.60, 2013.
- Rakotomamonjy, A., Support Vector Machines andArea under Roc Curve, September 1, 2004.
- Smyth, G.K., Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments, Statistical Applications in Genetics and Molecular Biology,Vol. 3, 2004.
- Soneson, C. and Delorenzi, M.,AComparison of Methods for Differential Expression Analysis of RNA-Seq Data, BMC Bioinformatics, Vol. 14, 2013.
- Tan, Y.D., Fornage, M. and Fu Y.X., Ranking Analysis of Microarray Data: A Powerful Method for Identifying Differentially Expressed Genes, Genomics, Vol. 88, No.6, pp.846-854, 2006.
- Vapnik, V.N., An Overview of Statistical Learning Theory, IEEE Transactions on Neural Networks, Vol. 10, 1999.
- Wang, J., Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications, Technical Report, Montclair State University, USA.
- Zhu, W., Zeng, N. and Wang, N., Sensitivity, Specificity, Accuracy, Associated Confidence Interval and Roc Analysis with Practical Implementat ions, Heal th Care and Life Sciences, Proceedings ofNESUG2010, 2010.
- Barman, B. and Mukhopadhyay, A., Extracting Biological Significant Subnetworks from ProteinProtein Interactions Induced by Differentially Expressed Genes of HIV-1 Vpr Variants, International Journal of System Dynamics Applications, Vol. 4, No.4, pp. 35-51, 2015.
- Biswas, P. and Barman, B., An Approach to Identify Gene Markers Relating to Viral Carcinogenesis Using Data Mining Tools, Indian Science Cruiser, Vol. 33, No.2, pp.24-32, 2019. DOI: 10.24906/isc/2019/v33/i2/183890.
- Barman, B. and Mukhopadhyay, A., Detection of Differentially Expressed Genes in Wild Type HIV-1 Vpr and Two HIV-1 Mutant Vprs, FICTA, Vol. 1, pp.597-604, 2014
- Development of an Intelligent Decision Support System for Identifying a Person by Voice Signal Response
Authors
1 Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani, Nadia, IN
Source
Indian Science Cruiser, Vol 30, No 4 (2016), Pagination: 41-45Abstract
In this paper, a hierarchical schematic model is developed to identify a person. The system is based on persons' voice signal responses. The proposed system detects a person by their voice signal. In the modern age, the incidental description is used as a resource for criminal investigation. In the designing model, an experimental circuit is used to obtain frequency response of different voice signals. After that, an algorithm is developed and implemented in programming language 'C' to identify the particular voice signal response.
- A System Model Design to Match Fingerprints by Extracting Minutiae
Authors
1 Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani, Nadia, IN
Source
Indian Science Cruiser, Vol 30, No 2 (2016), Pagination: 42-47Abstract
In the modern technologies the fingerprints are one of the most advanced biometric technologies and these are used as the proofs of a person's evidence. In a fingerprint, the varieties of categories of minutiae are present. The most popular categories of minutiae are used in the fingerprint verification system. In this paper, a indigenous design model is proposed for fingerprint analysis by extracting minutiae of image. Correlation coefficient between the two fingerprint images is calculated after doing the preprocessing on input image until to get suitable minutiae. Additionally, the two important categories of minutiae and the bifurcation are used in this paper.- Designing Predictive Model to Find Displacements of a Moving Object:Longitudinal and Rotational
Authors
1 Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani-741235, Nadia, West Bengal, IN
Source
Journal of the Association of Engineers, India, Vol 87, No 1-2 (2017), Pagination: 16-25Abstract
The calculation of motion with vertical, horizontal and angular displacement of a moving object is important to find the rotational characteristics of that particle. In this paper, algorithm is generated to find these displacements. For this, a 3-D object (Balloon) which is falling from a vertical height with its attributes and characteristics is considered. Algorithm is coded and implemented by the software MATLAB R2013a. As results, the distance covered by the object and the velocity of motion are calculated. The design of a predictive model to detect all displacements with the angular shift of moving object is also demonstrated in this paper.Keywords
Image Processing, Rasterization, RGB Image, GRAY Image, BINARY Image, Shadow Negation, Matrix Manipulation, Image Scaling.References
- Gonzalez, R. C. and Woods, R. E., Digital Image Processing, 3rd edition, Pearson Education, Inc, Pearson Prentice Hall, 2008.
- Guo, G., Dyer, C. R. and Zhang, Z., Linear Combination Representation for Outlier Detection in Motion Tracking, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, DOI: 10.1109/CVPR.2005.214, pp. 76-83, 2005.
- Caspi, Y. and Irani, M., Alignment of Non-overlapping Sequences, International Journal of Computer Vision, Vol. 48(1), pp. 39-51, 2002.
- Torr, P., Motion Segmentation and Outlier Detection, PhD thesis, Department of Engineering Science, University of Oxford, 1995.
- Jacobs, D., Linear Fitting with Missing Data: Applications to Structure-from-motion and Its Characterizing Intensity Images, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, DOI: 10.1109/CVPR.1997.609321, pp. 206-212, 1997.
- Rechardson, I., Bystrom, M., Frutos, M. D. and Kannangara, S., Dynamic Transform Replacement in an H.264 CODEC, in the Proceedings of IPIC2008, pp. 2108-2111, 2008.
- Wang, W. H. A., and Tung, C. L., Dynamic Hand Gesture Recognition using Hierarchical Dynamic Bayesian Networks Through Low-level Image Processing, in the Proceedings of IEEE International Conference on Machine and Cybernetics, pp. 3247-3253, 2008.
- Trentacoste, M., Heidrich, W., Whitehead, L., Seetzen, H. and Ward, G., Photometric Image Processing for High Dynamic Range Displays, Journal of Visual Communication and Image Representation, Vol. 18, pp. 439-451, 2007.
- Analysis and Recognition of Facial Image by Eigenface Approach
Authors
1 Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani, Nadia, IN
Source
Indian Science Cruiser, Vol 31, No 1 (2017), Pagination: 51-55Abstract
In this paper, algorithm is developed based on eigenface algorithm to recognize facial image. After implementing the algorithm a reference image identified from a predefined facial image dataset. In preprocessing stage the images are resized to a consistent size. The performance of algorithm is analyzed by using pose and size of training dataset. It is seen from the result that robustness of the eigenface algorithm is good.References
- R. Chellappa, C. Wilson, and S. Sirohey, “Human and machine recognition of faces: A survey”, Proceedings of the IEEE, vol. 83, no. 5, pp. 705{740, 1995.
- A. Samal and P. Iyengar, “Automatic recognition and analysis of human faces and facial expressions: A survey”, Pattern Recognition, vol. 25, pp. 65{77, 1992.
- R. Duda and P. Hart, “Pattern Classication and Scene Analysis”, Wiley, New York, 1973.
- L. Sirovitch and M. Kirby, “Low-dimensional procedure for the characterization of human faces”, J. Optical Soc. of America A, vol. 2, pp. 519{524, 1987.
- M. Turk and A. Pentland, “Eigenfaces for recognition”, J. of Cognitive Neuro science, vol. 3, no. 1, 1991.
- M. Turk and A. Pentland, “Face recognition using eigenfaces”, in Proc. IEEE Conf. on Comp. Vision and Patt. Recog., 1991, pp. 586{591.
- Y. Moses, Y. Adini, and S. Ullman, “Face recognition: The problem of compensating for changes in illumination direction”, in European Conf. on Computer Vision, 1994,pp. 286{296.
- Y. Cheng, K. Liu, J. Yang, Y. Zhuang, and N. Gu, “Human face recognition method based on the statistical model of small sample size”, in SPIE Proc.: Intelligent Robot sand Computer Vision X: Algorithms and Techn., 1991, pp. 85{95.
- A. Lanitis, C.J. Taylor, and T.F. Cootes, “A united approach to coding and interpreting face images”, in Int. Conf. on Computer Vision, 1995, pp. 368{373.
- Q. Chen, H. Wu, and M. Yachida, “Face detection by fuzzy pattern matching”, in Int. Conf. on Computer Vision, 1995, pp. 591{596.
- I. Craw, D. Tock, and A. Bennet, “Finding face features”, in Proc. European Conf. on Computer Vision, 1992, pp. 92{96.
- Finger Print Recognition by Background Subtraction and Image
Authors
1 Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani- 741235, Dist. Nadia, West Bengal, IN
Source
Reason-A Technical Journal (Formerly Reason-A Technical Magazine), Vol 15 (2016), Pagination: 54-60Abstract
The fingerprint identification based on Image enhancement technique is essential for crime scene investigation, authentication of a person. The most challenging fields of computer aided design is to identify a person by his or her fingerprint. In this paper, the quality of each image in the input sequence is assessed and a clear fingerprint is selected from such a sequence for subsequent recognition. After preprocessing, an effective fingerprint image is extracted from the original image. Thereafter, features are extracted from image and those features are analyzed to match with a reference image feature. For this, an algorithm is developed and coded in MATLAB (R2015a).Keywords
Finger Print, Image Analysis, Gamma Correction, Image Enhancement.References
- Hong, L., Jain, A. K.,Pankanti, S. and Bolle, R., Fingerprint Enhancement, Proceedings ofIEEE Workshop on Applications of Computer Vision, Sarasota, FL, pp. 202-207, 1996.
- Hong, L., Wan, Y. and Jain, A.K., Fingerprint Image Enhancement: Algorithms and Performance Evaluation, IEEE Transactions on PAMI ,Vol. 20, No. 8, pp.777-789, 1998.
- Maio, D. and Maltoni, D., Direct gray-scale minutiae detection in fingerprints, IEEE Trans.Pattern Anal. and Machine Intell,Vol. 19(1), pp. 27-40, 1997.
- Jain, A.K., Hong, L., and Bolle, R, On-Line Fingerprint Verification, IEEE Trans. On Pattern Anal and Machine Intell, Vol. 19(4), pp. 302-314, 1997.
- Coetzee, L. and Botha, E. C., Fingerprint Recognition in Low Quality Images, Pattern Recognition, Vol. 26, No. 10, pp. 1441-1460, 1993.
- Lange ,L. and Leopold, G., Digital identification: It’s now at our fingertips, EEtimes at http://techweb.cmp.com/eet/ 823/, March 24, Vol. 946, 1997.
- Ratha, N.,Chen, S. and . Jain, A.K., Adaptive Flow Orientation Based Feature Extraction in Fingerprint Images, Pattern Recognition, Vol. 28, pp. 1657-1672, 1995.
- Zsolt, A. M., Vajna, K., and Leone, A., Fingerprint minutiae extraction from skeletonized binary images, Pattern Recognition, Vol.32, No.4, pp877-889, 1999.
- Hong, L., Automatic Personal Identification Using Fingerprints, Ph.D. Thesis, 1998.
- Germain, R., Califano, A., and Colville, S., Fingerprint matching using transformation parameter clustering, IEEE Computational Science and Engineering, Vol. 4, No. 4, pp. 42–49, 1997.
- Sudiro, S. A., Paindavoine, M., and Kusuma, T. M., Simple Fingerprint Minutiae Extraction Algorithm Using Crossing Number On Valley Structure, Automatic Identification AdvancedTechnologies, IEEE Workshop on, Alghero, Italy, DOI: 10.1109/AUTOID.2007.380590, 2007.
- Parra, P., Fingerprint minutiae extraction and matching for identification procedure, University of California, San Diego La Jolla, CA (2004): 92093-0443, 2004.
- Zaeri, N., Minutiae-based Fingerprint Extraction and Recognition, Biometrics, Jucheng Yang (Ed.), ISBN: 978-953-307-618-8, InTech, http://www.intechopen.com/ bo oks/biomet r ics /minut iae-base dfingerprintextraction-and-recognition, 2011.
- Shin, J. H., Hwang, H. Y., and Chien, S. I., Minutiae Extraction from Fingerprint Images Using Run-Length Code, Proceedings of ISMIS 2003: Foundations of Intelligent Systems, pp. 577-584, 2003.
- Fronthaler, H., Kollreider, K., and Bigun, J., Local features for enhancement and minutiae extraction in fingerprints, IEEE Trans Image Process. Vol. 17(3), pp. 354-63, DOI: 10.1109/TIP.2007.916155, 2008.
- Singh, B., and Singh, I., Fingerprint Minutiae Extraction and Compression Using LZW Algorithm, International Journal for Scientific Research& Development| Vol. 2, Issue 07, ISSN (online): 2321-0613, 2014.
- Pawar, S., Ghodke, A., Gaikwad, B. P., and Wakhude, G. P., A Survey of Minutiae Extraction from Various Fingerprint Images, IJARCSSE, Vol. 6(6), pp. 169-173, 2016.
- Singh, I. and Sharma, R., A Survey on Fingerprint Minutiae Extraction, International Journal of Advance Research, Ideas and Innovations in Technology, Vol. 3(3), pp. 264-267, 2017.
- Construction and Comparison of Gene Regulatory Networks of Human Hiv-1 VPR Microarray Datasets by Radial Basis Neural Network Approach
Authors
1 Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani- 741235, Dist. Nadia, West Bengal, IN
Source
Reason-A Technical Journal (Formerly Reason-A Technical Magazine), Vol 15 (2016), Pagination: 61-69Abstract
Gene Regulatory Network (GRN) construction by using neural network approach is very important and useful approach for analyzing microarray gene expression microarray datasets. The human HIV-1 Vpr mutant microarray time series gene expression value carries the experimentally validated interaction records. Firstly, the subtractive clustering approach is used to cluster the microarray data. Secondly, GRN is constructed within cluster centers of HIV-1 Vpr mutant dataset using Radial Basis Neural Network approach. The optimized output of genetic network is found using genetic algorithm. Then the influence of range of cluster centers of data clusters and also GRN outputs are compared. It is found that proximity of gene expressed values in wild type cell line HIV-1 is higher than other two HIV-1 Vpr mutants. In this paper, the nature of network output functions are also identified.Keywords
AIDS, HIV-1 Vpr Mutants, Time Series Microarray Data, Subtractive Clustering, Genetic Network, Radial Basis Neural Network, Genetic Algorithm.References
- Zhao, R.Y.,Bukrinsky, M. and Elder, R.T., HIV-1 viral protein R (Vpr) and host cellular responses, The Indian Journal of Medical Research, Vol. 121, No.4, pp. 270-286, 2005.
- Yao, X.J.,Rougeau, N. and Duisit, G., Analysis of HIV-1 Vpr determinantsresponsible for cell growth arrest in Saccharomyces cerevisiae, Retrovirology, Vol. 1, No.21, 2004.
- Kogan, M. and Rappaport, J., HIV1Accessory ProteinVpr: Relevance in the pathogenesisof HIV and potential for therapeutic intervention, Retrovirology, Vol. 8, No.25, pp. 25-44, 2011.
- Sarafianos, S.G., Das, K. and Tantillo, C., Crystal structure of HIV-1 reversetranscriptase in complex with a polypurine tract RNA:DNA, The EMBO Journal, Vol.20, No.6, pp.1449-1461, 2001.
- Doan , C.D.,Liong, S.Y. and Karunasinghe, D. S.K., Derivation of ef fective and efficientdata set with subtractive clustering method and genetic algorithm, Journal of Hydroinformatics,Vol. 7, No.4, pp. 219-233, 2005.
- Bataineh, K.M., Najia, M. and Saqera, M., A Comparison Study between VariousFuzzy Clustering Algorithms’, Jordan Journal ofMechanical and Industrial Engineering, Vol. 5(4), pp. 335-343, 2011.
- Priyono, A.,Ridwan, M. and Alias, A. J., Generation of Fuzzy Rules with Subtractive Clustering, Journal Teknologi, Vol. 43, pp. 143-153, 2007.
- Goyal, S. and Goyal, G.K., Radial Basis (Exact Fit) Artif icialNeural Network Techniquefor Estimating Shelf Life of Burfi, Advances in Computer Science and its Applications, Vol. 1, No.2, pp.93-96, 2012.
- Orr, M.J.L., Introduction to radial basis function networks, Technical Report,Centre for Cognitive Science, University of Edinburgh, Scotland, 1996.
- Fiszelew, A., Britos, P. and Ochoa, A., Finding Optimal Neural Network ArchitectureUsing Genetic Algorithms’, Advances in Computer Science and Engineering Research in Computing Science,Vol. 27, pp. 15-24, 2007.
- Zhang, B.T. and Muhlenbein, H., Evolving Optimal Neural Networks Using GeneticAlgorithms with Occams’ Razor’, Complex Systems, Vol. 7, No.3, pp. 199220, 1993.
- Sug, H., Generating Better Radial Basis Function Network for Large Data Set of Census,International Journal of Software Engineering and Its Applications, Vol. 4 (2), pp. 15-22, 2010.
- Barman, B. and Mukhopadhyay, A., Construction of GA-optimized Radial Basis Neural Network from HIV-1 Vpr Mutant Microarray Gene Expression Data, Proceedings of International Conference on Computational Intelligence: Modeling, Techniques and Applications (CIMTA 2013),Kalyani, India, Procedia Technology, Vol. 10, pp. 450-456, 2013.
- Barman, B.,Biswas, P. and Mukhopadhyay, A., Comparison of gene regulatory networks usingadaptive neural network and selforganising map approach over Huh7 hepatoma cell microarray datamatrix, International Journal of Bio-Inspired Computation, Vol. 8, No.4, pp. 240-247, 2016.
- Detection of Edges of Moving Object from Video
Authors
1 Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani, Nadia, IN
Source
Indian Science Cruiser, Vol 31, No 5 (2017), Pagination: 52-57Abstract
In this paper, an effort has been made to detect a moving object with its edges for proper analysis. Appropriate detection and analysis of an image is a key factor for several image based applications. This has a high application in various security system and traffic system. Here, the object is falling from a height, ‘h’, with an angular rotation of ‘θ’ in its own axis. The aim this paper is to recognize moving object separately from other stationary things and background. The edges of moving object are also detected. Roberts edge detection method is used to detect edges of moving object. To work with a video and to implement algorithms in a continuous manner, simulation models using MATLAB r2014a software are used and outputs are obtained after compiling simulation models.References
- Y. Caspi and M. Irani, “ Alignment of nonoverlapping sequences”, Proc. Int. Conf. Computer Vision, 76-83, 2001.
- P. Torr, “Motion Segmentation and Outlier Detection”, PhD thesis, Department of Engineering Science, University of Oxford, 1995.
- D. Jacobs, “Linear fitting with missing data: Applications to structure-from-motion and its characterizing intensity images”. Proc.Conf. Computer Vision and Pattern Recognition, 206-212, 1997
- I. Rechardson, M. Bystrom, M. de Frutos and S. Kannangara, “Dynamic transform replacement in an H.264 CODEC”, IPIC2008, 2108-2111.
- W. H. A Wang and C. L. Tung , “Dynamic hand gesture recognition using hierarchical dynamic Bayesian networks through low-level image processing”, IEEE international conference on machine and cybernetics , 12-15July2008,Volume: 6,pages 3247-3253, 2008.
- M. Trentacoste , W. Heidrich, L. Whitehead, H. Seetzen and G. Ward , “Photometric image processing for high dynamic range displays”, Journal of Visual Communication and Image Representation, Vol. 18 (5), 439-451, 2007.
- N. Nain, V. Laxmi, A. K. Jain and R. Agarwal, “Morphological Edges Detection and Corner Detection Algorithm using Chain encoding” , Malaviya National Institute of Technology , Jaipur, IPCV’ 06.
- J. Canny, “A Computational Approach to Edge Detection” , PAMI 8(6) : 679-698, 1986.
- H. Freeman and L. S. Davis, “A Corner Finding Algorithm for Chain Coded Curves” , IEEE Trans. Computers , Vol-26, pp. 297- 303 , 1977.
- SUSAN, “A New Approach to Low Level Image Processing” , Technical Report TR95SMS 1 c.
- X. C. He and N. H. C. Yung, “Curvature Scale Space Corner Detector with Adaptive Threshold and Dynamic Region of Support”, ICPR, 17th International Conference on Pattern Recognition, ICPR’04, Volumn-2, pp 791-794 2004.
- B.BARMAN , T.K. DEY “An Algorithm to find the positional effects of a 3-D element under motion(ALGO-0157)” ,In the 2009 IEEE International Advance Computing Conference in Patiala, India, 2009
- Identification of Effective Genes from HIV-1 VPR Microarray Datasets after Constructing GA-Optimized Genetic Network
Authors
1 Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani-741235, Nadia, West Bengal, IN
Source
Journal of the Association of Engineers, India, Vol 87, No 3-4 (2017), Pagination: 37-45Abstract
Identification of effective genes from microarray dataset is important for obtaining relationship between that gene and a particular disease. For this, Gene Regulatory Network (GRN) construction is important. In this paper, GA-optimized GRNs are constructed using radial basis neural network (RBN) approach within cluster center matrix of human HIV-1 Vpr mutant microarray time series gene expression data. After this, effective genes from each microarray dataset are found by calculating minimum Euclidean distance between optimized output value and corresponding dataset.Keywords
HIV-1, Microarray Data, Subtractive Clustering, Radial Basis Neural Network, Optimization, Euclidian Distance.References
- Kogan, M. and Rappaport, J., HIV-1 accessory protein Vpr: relevance in the pathogenesis of HIV and potential for therapeutic intervention, Retrovirology, Vol.8(25), pp.25-44, 2011.
- Bannwarth, S. and Gatignol, A., HIV-1 TAR RNA: the target of molecular interactions between the virus and its host, Current HIV Research, Vol.3(1), pp.61-71, 2005.
- Barman, B., Biswas, P. and Mukhopadhyay, A., Comparison of gene regulatory networks using adaptive neural network and selforganising map approach over Huh7 hepatoma cell microarray datamatrix, International Journal of Bio-Inspired Computation, Vol.8, No.4, pp. 240-247, 2016.
- Deshmane, S. L., Amini, S., and Sen, S., Regulation of the HIV-1 promoter by HIF-1a and Vpr proteins, Virology Journal, Vol.8(477), 2011.
- Doan, C. D. , Liong, S. Y., and Karunasinghe, D. S. K., Derivation of effective and efficient data set with subtractive clustering method and genetic algorithm, Journal of Hydroinformatics, Vol.7(4), pp.219-233, 2005.
- Priyono, A., Ridwan, M. and Alias, A. J., Generation of fuzzy rules with subtractive clustering, Journal Teknologi, Vol.43, pp.143-153, 2007.
- Sug, H., Generating better radial basis function network for large data set of census, International Journal of Software Engineering and Its Applications, Vol.4(2), pp.15-22, 2010.
- Fiszelew, A., Britos, P. and Ochoa, A., Finding optimal neural network architecture using genetic algorithms, Advances in Computer Science and Engineering Research in Computing Science, Vol.27, pp.15-24, 2007.
- Barman, B. and Mukhopadhyay, A., Construction of GA-optimized Radial Basis Neural Network from HIV-1 Vpr Mutant Microarray Gene Expression Data, Proceedings of the International Conference on Computational Intelligence: Modeling, Techniques and Applications (CIMTA2013), Kalyani, India, Procedia Technology, Vol.10, pp.450-456, 2013.
- Report of 5th Nabanita-Satish Chandra Biswas and 2nd Dr. Murali Mohan Biswas Memorial Lectures-2018
Authors
1 Kalyani Govt. Engineering College, IN
Source
Indian Science Cruiser, Vol 32, No 4 (2018), Pagination: 62-63Abstract
On 7th July, 2018, Institute of Science, Education and Culture (ISEC) in collaboration with Birla Industrial and Technological Museum (BITM) has organized “5th Nabanita-Satish Chandra Biswas Memorial Lecture-2018” and “2nd Dr. Murali Mohan Biswas Memorial Lecture-2018” at Conference Hall of BITM. Photographs of the founder of ISEC, late Prof (Dr.) Murali Mohan Biswas and his parents late Satish Chandra Biswas and Nabanita Biswas were garlanded with Rajaniganddha flower.- Report on Centenary Seminar on "challenges and Prospects of Engineering Professionals in India" Held on November 17, 2018
Authors
1 Department of Electronics and Communication Engineering, Kalyani Govt. Engineering College, Kalyani, IN
Source
Journal of the Association of Engineers, India, Vol 88, No 3-4 (2018), Pagination: 62-68Abstract
On the occasion of celebrating Centenary year (1919-2018) of The Association of Engineers, India, a Seminar was organized on "Challenges and Prospects of Engineering Professionals in India" in association with the Government College of Engineering and Ceramic Technology (GCECT), Kolkata on November 17, 2018. The Registration for this seminar was started from 10.30 am at GCECT. The Principal of GCECT, Prof. Dr. Krishnendu Chakrabarty was taking care of all guests present there. The well equipped and decorated auditorium of GCECT was full with all the respective dignitaries, AEI Members and registered students from Government colleges and other institutes.- Report of the Seminar on “Iconic Scientific Personalities: Prof. S N Bose, Prof. M N Saha and Prof. P C Mahalanobis”
Authors
Source
Indian Science Cruiser, Vol 32, No 6 (2018), Pagination: 61-63Abstract
On 29th September, 2018, Institute of Science, Education and Culture (ISEC), in collaboration with Birla Industrial and Technological Museum (BITM), organized a seminar on “Iconic Scientific Personalities: Prof. S N Bose, Prof. M N Saha and Prof. P C Mahalanobis” at the Seminar Hall, BITM as a tribute to pay homage to the three great scientists on the occasion of their 125th birth anniversary. That great occasion started with an Inaugural Speech by Prof. Anil Kumar Ghosh, the President of ISEC.- Report on “Professor Asima Chatterjee Centenary Seminar” (PACCS-2018) Held on 23rd September, 2018
Authors
1 KGEC & ISEC
Source
Indian Science Cruiser, Vol 33, No 1 (2019), Pagination: 66-67Abstract
Being a member of ISEC family, it was a great opportunity for me to attend “Professor Asima Chatterjee Centenary Seminar” (PACCS-2018) on Recent Inventions in Chemical and Allied Sciences organized by Professor Asima Chatterjee Foundation, Kolkata (PACFK) on 23rd September, 2018 at M. N. Saha Auditorium, University of Calcutta (Rajabazar Campus). It was started with an inaugural ceremony by offering flowers to the portrait of Professor Asima Chatterjee.- A Brief Report of the Seminar on Celebration of Centenary Year (1919 - 2018) of the Association of Engineers, India Held on November 17, 2018
Authors
1 KGEC & ISEC
Source
Indian Science Cruiser, Vol 33, No 1 (2019), Pagination: 67-68Abstract
The Association of Engineers, India (AEI) celebrated the glorious occasion of its Centenary Year (1919 - 2018) by organizing a seminar on “Challenges and Prospects of Engineering Professionals in India” on 17.11.2018 at College of Engineering and Ceramic Technology (GCECT), Kolkata. Eminent Professors and Engineers were present in this auspicious event. Prof. Dr. Santanu Das, the Secretary of ISEC, was also the Convener of Centenary Committee of The AEI. Dr. Anil Kumar Ghosh, The President of ISEC, Dr. Swapna Mukherjee, The Convener of Seminar Committee of ISEC, Mr. Subhendu Datta Treasurer of ISEC and other members of ISEC along with Mr. R.K. Pratihar, the Executive Editor of Indian Science Cruiser published by ISEC, attended the Seminar.- An Approach to Identify Gene Markers Relating to Viral Carcinogenesis using Data Mining Tools
Authors
1 Department of Computer Science and Engineering, University of Kalyani, Kalyani, Nadia, West Bengal, IN
2 Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani, Nadia, West Bengal, IN
Source
Indian Science Cruiser, Vol 33, No 2 (2019), Pagination: 24-32Abstract
Genes are the smallest hereditary unit in any living organism. Several genes work together and regulate each other to accomplish different cellular processes. In order to perform specific cellular task genetic information has been used for protein synthesis. According to recent studies, these entire procedures react through the change in the expression level of genes. Therefore, monitoring the changes in expression patterns over time provides a distinct possibility of unraveling the mechanistic drivers characterizing cellular responses. Nowadays, with the help of microarray technology, it is possible to measure the mRNA expression level of thousands of genes simultaneously. Therefore, Hepatitis C, virus (HCV) infected microarray data set has been collected to carry out the proposed experiments. In this paper, gene expression changes due to Hepatitis C virus (HCV) infection have been studied. Then the genes having almost similar expression pattern in all the time points were grouped together. Next, biological significance analysis of those groups of genes is performed to identify the gene markers not only related to viral infection processes but also actively taking part in viral carcinogenesis process.
Index Terms-Microarray data, fuzzy c-means clustering, Euclidean distance, hepatitis C, cluster evaluation, neural network, viral carcinogenesis, HBV, HCV, HPV, HTLV-I, EBV, and KSHV. An Approach to Identify Gene Markers Relating to Viral Carcinogenesis Using Data Mining Tools Paramita Biswas1 and Bandana Barman2 S
Keywords
Microarray Data, Fuzzy C-Means Clustering, Euclidean Distance, Hepatitis C, Cluster Evaluation, Neural Network, Viral Carcinogenesis, HBV, HCV, HPV, HTLV-I, EBV, KSHV.References
- C. M. O’Connor and J. U. Adams, Essentials of Cell Biology. Cambridge, MA: NPG Education, 2010.
- Z. Herceg and P. Hainaut, “Genetic and epigenetic alterations as biomarkers for cancer detection, diagnosis and prognosis,” Molecular oncology, vol. 1, no. 1, pp. 26–41, 2007.
- Y. Chen, V. Williams, M. Filippova, V. Filippov, and P. Duerksen-Hughes, “Viral carcinogenesis: factors inducing dna damage and virus integration,” Cancers, vol. 6, no. 4, pp. 2155–2186, 2014.
- A. J. Levine, “The common mechanisms of transformation by the small dna tumor viruses: The inactivation of tumor suppressor gene products: p53,” Virology, vol. 384, no. 2, pp. 285–293, 2009.
- I. Androulakis, E. Yang, and R. Almon, “Analysis of timeseries gene expression data: methods, challenges, and opportunities,” Annu. Rev. Biomed. Eng., vol. 9, pp. 205– 228, 2007.
- C. M. Bishop, Neural networks for pattern recognition. 1995.
- P. T. Pearson, “Visualizing clusters in artificial neural networks using morse theory,” Advances in Artificial Neural Systems, vol. 2013, p. 8, 2013.
- C.-C. Chang and S.-H. Chen, “A comparative analysis on artificial neural network-based two-stage clustering,” Cogent Engineering, vol. 2, no. 1, p. 995785, 2015.
- T. S. Madhulatha, “An overview on clustering methods,” IOSR Journal of Engineering, vol. 2, no. 4, pp. 719–725, 2012.
- T. Calin´ski and J. Harabasz, “A dendrite method for cluster analysis,” Communications in Statistics-theory and Methods, vol. 3, no. 1, pp. 1–27, 1974.
- Report of National Seminar on “Preservation and Sustenance of Biodiversity” held on 9 March 2019
Authors
1 KGE College & ISEC, IN
Source
Indian Science Cruiser, Vol 33, No 3 (2019), Pagination: 66-67Abstract
Institute of Science, Education and Culture (ISEC) and Birla Industrial and Technological Museum (BITM), Kolkata collaboratively organized a National Seminar on "Preservation and Sustenance of Biodiversity" on 9 March 2019 at the Seminar Hall, BITM, Kolkata. Prof. Anil Kumar Ghosh, the President of ISEC, inaugurated the seminar with an inspirational speech based on topic of the said seminar. He also welcomed all in the seminar.- Detection of Moving Object Using Morphological Filters
Authors
1 Department of Electronics & Communication Engineering, Bengal College of Engineering. & Technology, Durgapur, IN
2 Department of Electronics & Communication Engineering, Kalyani Government Engineering College, Kalyani, Nadia,, IN
Source
Reason-A Technical Journal (Formerly Reason-A Technical Magazine), Vol 16 (2017), Pagination: 36-45Abstract
In this paper, novel morphological filters are developed under the scope of traffic system in India is proposed. The algorithms of three filters are developed and implemented with their proper coding using Matlab (R2017a) software to detect the moving objects from CCTV video signal. For this aim, three filters are designed with gaining concepts of linear filters and also non-linear operators i.e., morphological operators. Noise reducing is also important to identify or detect a moving object. As the most of traffic videos contain background images and also different noise signals, it is necessary to minimize or to eliminate noise by subtracting background images from the images of traffic video. After detecting moving object using three morphological filters developed, PSNR and SNR values are also calculated for identified object to get the best filter designed. It is seen from the result, that moving object i.e., only white car detected after removing noise and applying median filter followed by morphological filter on background subtracted image, gives highest PSNR and SNR values.Keywords
Morphological Filters, Binary Erosion and Dilation, Median Filter, Mean Filter, MATLAB Simulation.References
- Corso, J., Linear Filters and Image Processing, EECS 598-08 Lecture Notes, Foundations of Computer Vision, College of Engineering, Electrical Engineering & Computer Science, University of Michigan, Fall 2014.
- Maragos, P., Morphological Signal and Image Processing, ©1999 by CRC Press LLC, Georgia Institute of Technology, 1996.
- Gochoo, M. and Bayanduuren, D. et al., Design and Application of Novel Morphological Filter Used in Vehicle Detection, Proc 2016IEEE/ACIS 15th International Conference on Computer and Information (ICIS) (2016), Okayama, Japan, June 26-29, 2016, ISBN: 978-1-5090-0807-0, pp: 1-5, DOI:http://doi.ieeecomputersociety.org/10.1109/ICIS.2016.7550798, 2016.
- Lee, J.S.J., and Haralick, R.M. et al., Morphologic Edge Detection, IEEE Trans. Rob. Autom., Vol. RA-3, pp. 142-156, Apr. 1987.
- Loce, R .P. and Dougherty, E.R., Facilitation of Optimal Binary Morphological Filter Design via Structuring Element Libraries and Design Constraints, Optical Engineering, Vol. 31,pp. 1008-1025, May 1992.
- Mara go s, P. an d Sch afe r, R.W., Morphological Filters. Part I: Their Set
- Maragos , P. and Schafer, R . W. , Morphological Filters--Part II: Their Relations to Median, Order-Statistic, and Stack Filters, IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 35 , Issue. 8, pp. 1170 - 1184, ISSN: 0096-3518 DOI: 10.1109/TASSP.1987.1165254, Aug 1987. Corrections, Vol.37, no.4, p.597, Apr. 1989.
- Marag os, P. an d Sch afer, R.W., Morphological Systemsfor Multidimensional Signal Processing, Proc. IEEE, Vol. 78, pp. 690 –710, April 1990.
- Nascimento, J. and Marques Jorge, Performance Evaluation of Object Detection Algorithms for Video Surveillence, IEEE Transactions on Multimedia, Volume: 8 , Issue: 4, DOI: 10.1109/TMM.2006.876287, pp. 761 - 774, Aug. 2006.
- Chowdhury, F. A., and Shanchary, I. J. et al., Abandoned Object Detection with Video Surveillance, Thesis Paper, School of Computer Science and Engineering, BARK University, Fall 2014.
- Herrero, E. and Orrite, C. et. al., VideoSensor for Detection and Tracking of Moving Objects. In: Perales F.J., Campilho A.J.C., de la Blanca N.P., Sanfeliu A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg, 2003.
- Vipul, M.,Detecting moving Linear ShiftInvariant Filters., IEEEobjects in Video Frames, Thesis for Trans. Acoust. Speech, Signal Master of Technology, Department ofProcess., vol. 35, Issue 8, ISSN:0096Computer Science and Engineering 3518, p p . 11 5 3 - 11 6 9 , National Institute of Technology DOI:10.1109/TASSP. 1987.1165259, Rourkela, 2014.Aug. 1987
- Kumar, P. and Singhal, A. et al., RealTime Moving Object Detection Algorithm on High-Resolution Videos Using GPUs, J RealTime Image Proc,DOI 10.1007/s11554-0120309-y, Springer-Verlag Berlin Heidelberg 2013.
- Olugboja, A. and Wang, Z., Detection of Moving Objects Using Foreground Detector and Improved Morphological Filter, 3rd International Conference on Information Science and Control Engineering, 978-15090-2534-3 /16 © 2016 IEEE, IEEE Computer Society, pp. 329-333, 2016. DOI: 10.1109/ICISCE.2016.80.
- Mahalingam, T. and Subramoniam, M., A Robust Single and Multiple Moving Object Detection, Tracking and classification, Applied Computing and Informatics (Article in Press),2018. https://doi.org/10.1016/ j.aci.2018.01.00.
- Ray, K. S. and Chakraborty, S., An Efficient Approach for Object Detection and Tracking of Objects in a Video with Variable Background, arXiv:1706.02672v1 [cs.CV] 11 May 2017.
- Risha K P and Chempak K. A." Novel Method of Detecting Moving Object in Video", International Conference on Emerging Trends in Engineering, Science and Technology (ICETEST - 2015), Procedia Technology 24, pp. 1055 – 1060, 2016.
- Oreifej, O. and L,i X. et al., Simultaneous Video Stabilization and Moving Object Detection in Turbulence, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35 , Issue. 2 , pp. 450 - 462, ISSN: 13187927, DOI: 10.1109/TPAMI. 2012.97, IEEE Computer Society, Feb. 2013.
- Yazdi, M. and Bouwmans, T., New Trends on Moving Object Detection in Video Images Captured by a moving Camera: A Survey, Computer Science Review, DOI: 10.1016/j.cosrev.2018.03.001, 2018
- Yang, M., A Moving Objects Detection Algorithm in Video Sequence, ISBN: 978-14799-3903-9/14/$31.00 ©2014 IEEE, ICALIP2014, pp. 410-413, 2014.
- Yang , Y. and Zhang, Q. et. al., Research Article Moving Object Detection for Dynamic Background Scenes Based on Spatiotemporal Model, Advances in Multimedia Vo l u m e 2 0 1 7 , Article ID 5179013 , https://doi.org/10.1155/2017/5179013, pp. 1-9, 2017.
- Kalirajan, K. and Sudha, M., Research Article: Moving Object Detection for Video Surveillance. The Scientific World Journal, Vol. 2015, Article ID 907469, pp. 1-9, http://dx.doi.org/10.1155/2015/907469, 2015.
- Report on the Concluding Session of Centenary Year of the Association of Engineers, India Held on April 20 2019
Authors
1 Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani and Life Fellow, The Association of Engineers, India, IN
Source
Journal of the Association of Engineers, India, Vol 89, No 1-2 (2019), Pagination: 96-97Abstract
During celebrating the centenary year (1919-2018) of The Association of Engineers, India, the association has successfully organized a seminar at Government College of Engineering and Ceramic Technology (GCECT), Kolkata on November 17, 2018 and two days National Conference at Kalyani Government Engineering College, Kalyani during 15-16 February, 2019. In continuation of this glorious occasion, the concluding session was organized on 20th April, 2019 attheTraining Centre ofTheAEI at I A/11, Salt Lake City, Kolkata.- Report of 6thNabanita–Satish Chandra Biswas Memorial Lecture and 3rd Dr. Murali Mohan Biswas Memorial Lecture, 2019 held on July 06 2019
Authors
1 KGEC & ISEC, IN
Source
Indian Science Cruiser, Vol 34, No 3 (2020), Pagination: 63-65Abstract
No Abstract.- Compression of Machine Dependent Colour Image by Colour Transformation Techniques
Authors
1 Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani- 741235, Nadia, West Bengal, IN
Source
Indian Science Cruiser, Vol 34, No 3 (2020), Pagination: 32-37Abstract
Aim of this paper is development of enhance-image compression techniqueformachine dependent imageusing colour transformation techniques. CMYK colour model image with .tif extension uses large amount of machine dependent data. YCbCr colour space image model is a machine independent image. Here, image conversion technique is used to convert CMYK image into RGB colour space image. Then transformation process followed by mathematical processing is used to obtain RGB to Luma and Chrominance, Intensity based image model, YCbCr..This also results image compression.Keywords
Image colour space models, Luminance, Chrominance, Intensity, Image Compression, Fast Fourier Transform, Log Transform, MATLAB R2017a.References
- Digital image processing, https://www.scribd.com/ doc/48795959/Project-report-for-digital-imageprocessing, Project report uploaded by Ravi Shukla on February 14, 2011 retrieved on February 10, 2020.
- Colour, http://www.maths.adelaide.edu.au matthew .roughan/notes/AMP1/files/output.pdf, retrieved on February 11, 2020.
- R L de Queiroz, Color transformation for the compression of CMYK images, Proc. SPIE 3963 on Color Imaging: Device-Independent Color, Color Hardcopy, and Graphic Arts V, December, 21 1999, https://doi.org/10.1117/12.373400.
- S Kolkur, D Kalbande, P Shimpi, C Bapat and J Jatakia, Human Skin Detection Using RGB, HSV and YCbCr Colour Models, B Iyer, S Nalbalwar and R Pawade (Eds.), Proc. ICCASP/ICMMD-2016 on Advances in Intelligent Systems Research, Vol 137, page 324-332, 2016.
- P D Hiscocks, Syscomp Computer Controlled Instruments LUMA: Luminance Analysis Software Manual, Syscomp Electronic Design Limited, http: www.syscompdesign.com.
- Getting to Know the CMYK Colour Model– CMYK Colour Model Image, retrieved from https://stodioo.com/articles/cmyk-color-model/ on June 13, 2019.
- Color in Image and Video- LAB Colour Model Image, http://www-i6.informatik.rwthaachen.de/web/ Misc/Coding/365/li/material/notes/Chap3/Chap3.3/ Chap3.3.html, retrieved on February 10, 2020.
- V S Rathore, M S Kumar and A Verm, Colour Based Image Segmentation Using L*A*B* Colour Space Based on Genetic Algorithm, International Journal of Emerging Technology and Advanced Engineering, Vol 2, No 6, 2012.
- Color, Lecture 11: CSE Vision, retrieved on www.CartoonStock.Com.
- Profile Based Color Space Conversions, https:// in.mathworks.com /help/images/profile-based-color-space-conversions.html, retrieved on 10.2.2020.
- B Barman, Detection of Edges of Moving Object from Video, Indian Science Cruiser, Vol 31, No 5, page 52-57, 2017. DOI:10.24906/isc/2017/v31/i5/161486.
- N Campos, RGB to YCbCr Conversion Playing with Bits and Pixels, posted on August 21, 2016, https:// sistenix.com/rgb2ycbcr.html, retrieved on October 3 2019.
- Input Image, Channel Digital Image CMYK color.jpg, https://en.wikipedia.org/wiki/File: Channel_digital_image_CMYK_color.jpg, retrieved on May 12, 2019.
- Report of Santi De and Gadadhar De Memorial Lecture 2019 and the National Seminar on 150th Year Celebration of Periodic Table
Authors
1 KGEC & ISEC, IN
Source
Indian Science Cruiser, Vol 34, No 4 (2020), Pagination: 60-62Abstract
No Abstract.- Report of One-Day National Seminar on “Water: Crisis and Conservation” held on November 23, 2019
Authors
1 KGEC & ISEC, IN
Source
Indian Science Cruiser, Vol 34, No 4 (2020), Pagination: 62-64Abstract
No Abstract.- Report of Celebration of National Science Day with the Theme, “National Science Day – New Frontiers in Science & Technology” held on 28th February 2022
Authors
1 Assistant Secretary, ISEC Kolkata, IN
Source
Indian Science Cruiser, Vol 36, No 2 (2022), Pagination: 60-61Abstract
No abstract.Keywords
No keywords.References
- No references.
- Report of “9th Nabanita-Satish Chandra Biswas Memorial Lecture and 6th Dr. Murali Mohan Biswas Memorial Lecture” Organised by ISEC, Kolkata on July 09 2022
Authors
1 9th Nabanita-Satish Chandra Biswas and 6th Dr. Murali Mohan Biswas Memorial Lecture ., IN
Source
Indian Science Cruiser, Vol 36, No 4 (2022), Pagination: 64 - 65Abstract
no abstractKeywords
no keywordsReferences
- no references
- Report of the Webinar on “Women Scientists in Biology & Medicine” held on 4th June 2022 , Report of the Webinar on “Impact of COVID-19 Pandemic on the Education System” held on 20 August 2022
Authors
1 Assistant Secretary, ISEC, IN
Source
Indian Science Cruiser, Vol 36, No 5 (2022), Pagination: 62 - 64Abstract
no abstractKeywords
no keywordsReferences
- no references