V.Sivasankarareddy & K.Ch.Mallareddy – April 2018 Page No.: 01-03
This paper is proposed to distinguish the classical codecs MPEG4 and H.264. The task is to compress video for surveillance system with stationary cameras by decreasing of temporal redundancy. The layers mentioned to process are Alpha channel, background estimation, image with intensities of objects and background estimation, correcting image. Some layers are compressed with lossy version of WebP and others are compressed with lossless version of WebP.
- Page(s): 01-03
- Date of Publication: 21 April 2018
- V.Sivasankarareddy
Assistant Professor, Electronics and Communications Engineering
Krishnachaitanya Institute of Technology & Science, Markapur, Andhra Pradesh, India - K.Ch.Mallareddy
Assistant Professor, Electronics and Communications Engineering
Krishnachaitanya Institute of Technology & Science, Markapur, Andhra Pradesh, India
References
[1]. https://searchunifiedcommunications.techtarget.com/definition/codec
[2]. https://en.wikipedia.org/wiki/H.264/MPEG-4_AVC
[3]. https://asp-eurasipjournals.springeropen.com/track/pdf/10.1155/ASP/2006/27579?site=asp-eurasipjournals.springeropen.com.
[4]. S. A. Kuzmin, “Compression in a panoramic video surveillance system,” Proceedings of the IEEE Russia. North West Section, Vol. 6, 2014. Pp. 31-33
V.Sivasankarareddy & K.Ch.Mallareddy “Implementation of Codec in Video Processing”, International Journal of Research and Innovation in Applied Science -IJRIAS vol.3 issue 4 April 2018, pp.01-03 URL: https://www.rsisinternational.org/journals/ijrias/DigitalLibrary/Vol.3&Issue4/01-03.pdf
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Usha Thakur, Sonal Rai – April 2018 Page No.: 04-07
Digital images in their uncompressed form require an enormous amount of storage capacity. Such uncompressed data needs large transmission bandwidth for the transmission over the network. Image compression is minimizing the size in bytes of a graphics file without degrading the quality of image. In this paper we study on various image compression techniques for number of applications.
- Page(s): 04-07
- Date of Publication: 21 April 2018
- Usha Thakur
M. Tech. Scholar, CSE Dept., LNCTE, Bhopal (M.P.), India - Sonal Rai
Assistant Professor, CSE Dept., LNCTE, Bhopal (M.P.), India
References
[1]. Shruthi K N, Shashank B M, Y.SaiKrishna Saketh, Dr. Prasantha .H.S and Dr. S.Sandya “Comparison Analysis Of A Biomedical Image For Compression Using Various Transform Coding Techniques”, IEEE, 2016, Pp 297-303.
[2]. V. V. Sunil Kumar and M. Indra Sena Reddy “Image Compression Techniques by using Wavelet Transform”, Journal of Information Engineering and Applications, 2012, Pp 35-40.
[3]. Maneesha Gupta and Dr.Amit Kumar Garg “Analysis Of Image Compression Algorithm Using DCT”, IJERA, 2012, Pp 515-521.
[4]. Kamrul Hasan Talukder and Koichi Harada “Haar Wavelet Based Approach for Image Compression and Quality Assessment of Compressed Image”, AJAM, 2010, Pp 1-8.
[5]. Kiran Bindu, Anita Ganpati and Aman Kumar Sharma “A COMPARATIVE STUDY OF IMAGE COMPRESSIONALGORITHMS”, International Journal of Research in Computer Science, 2012, Pp 37-42.
[6]. Miguel Hernandez-Cabronero, Victor Sanchez, Michael W. Marcellin, Joan Serra-Sagrista “A distortion metric for the lossy compression of DNA microarray images” 2013 Data Compression Conference.
[7]. Seyun Kim, Nam Ik Cho “Hierarchical Prediction and Context Adaptive Coding for Lossless Color Image Compression” I EEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 23, NO. 1, JANUARY 2014. Pp 445-449.
[8]. Seyun Kim, Nam Ik Cho “Lossless Compression of Color Filter Array Images by Hierarchical Prediction and Context Modeling” IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 24, NO. 6, JUNE 2014. Pp 1040-1046.
[9]. Mai Xu, Shengxi Li, Jianhua Lu, Wenwu Zhu “Compressibility Constrained Sparse Representation With Learnt Dictionary for Low Bit-Rate Image Compression” IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 24, NO. 10, OCTOBER 2014. Pp 1743-1757.
[10]. Vikrant Singh Thakur, Kavita Thakur “DESIGN AND IMPLEMENTATION OF A HIGHLY EFFICIENT GRAY IMAGE COMPRESSION CODEC USING FUZZY BASED SOFT HYBRID JPEG STANDARD” 2014 International Conference on Electronic Systems, Signal Processing and Computing Technologies. Pp 484-489.
[11]. Chandan Singh Rawat and Sukadev Meher “A Hybrid Image Compression Scheme using DCT and Fractal Image Compression”, International Arab Journal of Information Technology, 2013, Pp 553-562.
[12]. Navpreet Saroya and Prabhpreet Kaur “Analysis of IMAGE COMPRESSION Algorithm Using DCT and DWT Transforms”, International Journal of Advanced Research in Computer Science and Software Engineering, 2014, Pp 897-900.
[13]. S.M.Ramesh and Dr.A.Shanmugam “Medical Image Compression using WaveletDecomposition for Prediction Method”, IJCSIS, 2010, Pp 262-265.
[14]. Fouzi Douak, Redha Benzid and Nabil Benoudjit “Color image compression algorithm based on the DCT transform combined to an adaptive block scanning”, Elsevier, 2011, Pp 16-26.
[15]. Azam Karami, MehranYazdiand Grégoire Mercier “Compression of Hyperspectral Images Using Discerete Wavelet Transform and Tucker Decomposition”, IEEE, 2012, Pp 444-450.
[16]. MFerni Ukrit, G.R.Suresh “Effective Lossless Compressionjor Medical Image Sequences Using Composite Algorithm” 2013 International Conference on Circuits, Power and Computing Technologies. Pp 1122-1126.
[17]. Krishan Gupta, Dr Mukesh Sharma, Neha Baweja “THREE DIFFERENT KG VERSION FOR IMAGE COMPRESSION” 2014. Pp 831-837.
[18]. Saif Alzahir, Arber Borici “An Innovative Lossless Compression Method for Discrete-Color Images” IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 24, NO. 1, JANUARY 2015. Pp 44-55.
Usha Thakur, Sonal Rai “A Study Image Compression Techniques for the Number of Applications” International Journal of Research and Innovation in Applied Science -IJRIAS vol.3 issue 4 April 2018, pp.04-07 URL: https://www.rsisinternational.org/journals/ijrias/DigitalLibrary/Vol.3&Issue4/04-07.pdf
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Navita Kumari and Rahul Verma – April 2018 Page No.: 08-11
Review of different image fusion methods for merging of complementary diagnostic content has been carried out in this paper. Image fusion methods enhance the quality of images quantitatively and qualitatively. The availability of a large number of techniques in feature processing, feature extraction and decision fusion makes the field of image fusion appealing to be Mused in many applications. The algorithms used for image fusion studies have resulted in the improved imaging quality and also have proved to be useful for many applications. The well-known approaches include PCA, wavelets transforms, neural networks, fuzzy logic, and classifiers such as support vector machines. This will benefit for researchers to carry out further work in this thrust area of research.
- Page(s): 08-11
- Date of Publication: 24 April 2018
- Navita Kumari
Department of Electronics and Telecommunication Engineering
Central College of Engineering & Management, Kabir Nagar, Raipur, Chhattisgarh, India - Rahul Verma
Department of Electronics and Telecommunication Engineering
Central College of Engineering & Management, Kabir Nagar, Raipur, Chhattisgarh, India
References
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[3]. Yoonsuk Choi*, ErshadSharifahmadian, Shahram Latifi, “Quality assessment of image fusion Methods in transform domain”, International Journal on Information Theory (IJIT), Vol.3, No.1, January 2014.
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[5]. Ibrahim Elshafiey, Ayed Algarni and Majeed A. Alkanhal, Image Fusion Based Enhancement of Nondestructive Evaluation Systems, Image Fusion, Osamu Ukimura (Ed.), InTech, 2011.
[6]. G. Pajares and M. de la Cruz, “A wavelet-based image fusion tutorial,” Pattern Recognition, Vol. 37, No. 9, pp. 1855–1872, 2004.
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[14]. Toet, J.J. Van Ruyven, J.M. Valeton, Merging thermal and visual images by a contrast pyramid, Optical Engineering, Vol.: 28, No.: 7 pp: 789– 792, (1989).
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[17]. S. Thirunavukkarasu, B. P. C. Rao, A. K. Soni, S. Shuaib Ahmed, and T. Jayakumar, “Comparative performance of image fusion methodologies in eddy current testing,” Research Journal of Applied Sciences, Engineering and Technology, Vol. 4, No. 24, pp. 5548-5551, 2012.
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[19]. Huaxun Zhang and Xu Cao, ‘A Way of Image Fusion Based on Wavelet Transform’, IEEE 9th International Conference on Mobile Ad-hoc and Sensor Networks, 2013.
[20]. Zaid Omar, Saif S. Ahmed and Musa Mokji, Marsyita Hanafi & Vikrant Bhateja, ‘Wavelet-based Medical Image Fusion via a Non-linear Operator’, IEEE Region 10 Conference (TENCON), 2016.
[21]. Mirajkar Pradnya P. and Ruikar Sachin D, ‘Wavelet based Image Fusion Techniques’, International Conference on Intelligent Systems and Signal Processing (ISSP), 2013.
[22]. Yao-Hong Tsai, Yen-Han Lee, ‘Wavelet-based Image Fusion by Adaptive Decomposition’, Eighth International Conference on Intelligent Systems Design and Applications, 2008.
[23]. Syed Sohaib Ali, Muhammad Mohsin Riaz and Abdul Ghafoor, ‘Hybrid component substitution and wavelet based image fusion’, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2013.
[24]. Abhinav Krishn, Vikrant Bhateja, Himanshi & Akanksha Sahu, ‘Medical Image Fusion Using Combination of PCA and Wavelet Analysis’, International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2014.
[25]. Hamid Reza Shahdoosti, Hassan Ghassemian, ‘Spatial PCA as A New Method For Image Fusion’, IEEE 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP), 2012.
[26]. Abhinav Krishn, Vikrant Bhateja, Himanshi, and Akanksha Sahu, ‘Medical Image Fusion Using Combination of PCA and Wavelet Analysis’, IEEE International Conference on Advances in Computing, Communications and Informatics (ICACCI), New Delhi, 2014.
[27]. Hongyuan Jing and Tanya Vladimirova, ‘Novel PCA Based Pixel-Level Multi-Focus Image Fusion Algorithm’, IEEE NASA/ESA Conference on Adaptive Hardware and Systems (AHS), 2014.
[28]. Mohamed R. Metwalli, Ayman H. Nasr, Osama S. Farag Allah, and S. El-Rabaie, ‘Image Fusion Based on Principal Component Analysis and High-Pass Filter’, IEEE International Conference on Computer Engineering & Systems (ICCES), 2009.
[29]. Changtao He, Quanxi Liu, Hongliang Li, Haixu Wang, ‘Multimodal medical image fusion based on IHS and PCA’, Procedia Engineering, Vol.: 7, pp: 280-285, 2010.
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[31]. A.P. James, B. V. Dasarathy, Medical Image Fusion: A survey of the state of the art, Information Fusion, 2014.
[32]. H. Szu, I. Kopriva, P. Hoekstra, N. Diakides, M. Diakides, J. Buss, J. Lupo, ‘Early tumor detection by multiple infrared unsupervised neural nets fusion’, Engineering in Medicine and Biology Society, Vol. 2, pp. 1133–1136, 2003.
[33]. W. Li, X.-f. Zhu, ‘A new algorithm of multi-modality medical image fusion based on pulse-coupled neural networks’, Advances in Natural Computation, Springer, pp. 995–1001, 2005.
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Navita Kumari and Rahul Verma “Image Fusion Methodologies: A Survey of the State of the Art” International Journal of Research and Innovation in Applied Science -IJRIAS vol.3 issue 4 April 2018, pp.08-11 URL: https://www.rsisinternational.org/journals/ijrias/DigitalLibrary/Vol.3&Issue4/08-11.pdf
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Dr H.M. Sudharshana – April 2018 Page No.: 12-17
Adolescence is a time of many transitions both for tens and their families. To ensure that terms and adults navigate these transitions successfully, adolescents develop stronger reasoning skills, logical and moral thinking, and become more capable of abstract thinking and making rational judgments. it is important for both to understand what is happening to the teen. Adolescence can be a time of both disorientation and discovery. This transitional period can bring up issues of independence and self-identity. Biological maturity precedes psychosocial maturity. How these transitions effects teens, physically, socially and otherwise, the changes that take place during adolescence suggest what adults can do, and what support resources are available.
- Page(s): 12-17
- Date of Publication: 24 April 2018
- Dr H.M. Sudharshana
Faculty, Abdul Nazir Sab State Institute of Rural Development and Panchayath Raj, Mysuru. Karnataka, India
References
[1]. Adolescent Disruptive Behavior Disorders. By James Alexander. Found in Clinical Updates for Therapists: Volume 1, AAMFT (1999).
[2]. Browner (1999) the biological embedding of early experience and its effects on health in adulthood. Ann N Y Accad Sci. 896: 85–95
[3]. Commission on Social Determinants of Health. Closing the gap in a generation: action on the social determinants of health. World Health Organization, (2008)
[4]. Live &Kaplan (1999). Psychosocial development. In Comprehensive Adolescent Health Care.7:32-34
[5]. luicidi 1994. Operations Research Proceedings papers (1994) Vol. 44 No.01. Selected issues.
[6]. Hayward,Kelvin Wilson & hammer (1997). Neighborhood and family influences on educational attainment: results from the Indian child health study follow-up 2001. Child Dev. 2007; 78: 168–189
[7]. Stenberg &Bhatia(Ed.), Adolescence perspective. Handbook of adolescent psychology (pp. 3–46.(1984).
[8]. Thieneman &stiner(1992).The relations of pubertal status to intrapersonal changes in young adolescents. Journal of Early Adolescence 8:405–419.
[9]. walsh and devin (1998) . Coming of age too early: Pubertal influences on girls’ vulnerability to psychological distress. Child Development 67:3386–3400.
[10]. Walsh and Devin.(1998).Comparing an individuals test score against norms derived from small samples.clinicalpsychologist,12,482:486
[11]. “The Invention of Adolescence”. Psychology Today. June 9, 2016. Retrieved February 19, 2017.
[12]. W.Marsh (1989). “Age and sex differences in multiple dimensions of self-concept: Preadolescence to early adulthood”. Journal of Educational Psychology.
Dr H.M. Sudharshana “Adolescence among High School Girls –A Case Study of Mysore City” International Journal of Research and Innovation in Applied Science -IJRIAS vol.3 issue 4 April 2018, pp.12-17 URL: https://www.rsisinternational.org/journals/ijrias/DigitalLibrary/Vol.3&Issue4/12-17.pdf
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Anupam Asthana, Akshit Gupta, Akshay Kumar Singh, Ekant Bajaj, Nishu Bansal – April 2018 Page No.: 18-21
Dynamic memory network is the state-of-the-art model for question answering tasks. With synergy of Dynamic memory network and Facebook bAbI dataset [5], we get high performance result for question answering tasks. In Question Answering task, the Dynamic Memory Network takes input in form of story or paragraph through input module, processes the input and question asked and with episodic memory try to give the answer of the particular question. Our aim for this project is to replicate results on the existing network and to experiment on single and double context tasks using Convolutional Long Short-Term Memory, Bidirectional Long Short-Term Memory and Time Distributed Dense Layer [9].
- Page(s): 18-21
- Date of Publication: 24 April 2018
- Anupam Asthana
Department of Computer Science,
Ajay Kumar Garg Engineering College, Ghaziabad, U.P., India - Akshit Gupta
Department of Computer Science,
Ajay Kumar Garg Engineering College, Ghaziabad, U.P., India - Akshay Kumar Singh
Department of Computer Science,
Ajay Kumar Garg Engineering College, Ghaziabad, U.P., India - Ekant Bajaj
Department of Computer Science,
Ajay Kumar Garg Engineering College, Ghaziabad, U.P., India - Nishu Bansal
Asst. Professor, Department of Computer Science,
Ajay Kumar Garg Engineering College, Ghaziabad, U.P., India
References
[1]. Kumar, Ankit et al. (2015) . Ask me anything: Dynamic memory networks for natural language processing. arXiv preprint arXiv:1506.07285.
[2]. Hermann, Karl Moritz et al. Teaching Machines to Read and Comprehend. https://arxiv.org/abs/1506.03340
[3]. Xiong, Caiming et al. (2016) Dynamic Memory Networks for Visual and Textual Question Answering. https://arxiv.org/abs/1603.01417
[4]. Weston, Jason et al. (2015) Memory Networks. ICLR 2015.https://arxiv.org/abs/1410.3916
[5]. Jason Weston, Antoine Bordes, Sumit Chopra, and Tomas Mikolov. Towards ai-complete question answering: A set of prerequisite toy tasks. arXiv preprint arXiv:1502.05698, 2015.
[6]. Hochreiter & Schmidhuber (1997) Long Short-Term Memory https://www.bioinf.jku.at/publications/older/2604.pdf
[7]. Raghuvanshi,Chase- Dynamic Memory Networks for Question Answering https://cs224d.stanford.edu/reports/RaghuvanshiChase.pdf
[8]. https://colah.github.io/posts/2015-08-Understanding-LSTMs/
[9]. https://deeplearning4j.org/lstm.html
Anupam Asthana, Akshit Gupta, Akshay Kumar Singh, Ekant Bajaj, Nishu Bansal “Dynamic Memory Network Approach to Question Answering” International Journal of Research and Innovation in Applied Science -IJRIAS vol.3 issue 4 April 2018, pp.18-21 URL: https://www.rsisinternational.org/journals/ijrias/DigitalLibrary/Vol.3&Issue4/18-21.pdf
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N. Venkata Niranjan Kumar, Gowhami G – April 2018 Page No.: 22-27
Technologically advanced industries have been demanding high strength temperature resistant alloys. So, there is a huge demand for materials with the properties like high material strength, hardness, toughness and other diverse properties. The AWJM process is used in manufacture of electronic devices, permanent making on rubber stencils etc. This paper investigates the experimental study and optimization of Abrasive water jet machine (AWJM) process variables for super alloys (Incoloy825). The objective is to identify the values of process variables where the surface roughness (Ra) and kerf width (K) is minimum. In this study, Incoloy825 super alloy of 8 mm thickness is used as a work piece. Three parameters were chosen as process variables; they are nozzle traverse speed, abrasive flow rate and standoff distance. By using Taguchi’s L27 orthogonal array (OA) experiments are conducted, for each experimental run Ra was measured by using Talysurf equipment, kerf width was measured by using tool maker’s microscope. The main objective of present work is to study the optimization of AWJM process variables to get minimum Ra and K .Optimal levels of process parameters were identified by using Grey- relational analysis.
- Page(s): 22-27
- Date of Publication: 24 April 2018
- N. Venkata Niranjan Kumar
Assistant. Professor, Mechanical Engineering Department,
Srikalahasteeswara Institute of Technology, Srikalahasthi- 517640, A.P, India - Gowhami G
2Assistant. Professor Mechanical Engineering Department,
Bangalore Technological Institute, Bangalore-560035, Karnataka, India
References
[1]. M. Saleem, L. Toubal, R. Zitoune, H. Bougherara; Investigating the effect of machining processes on the mechanical behavior of composite plates with circular holes Available online 8 September 2013.
[2]. Neelesh K. Jain, V.K. Jain, Kalyanmoy Deb Optimization of process parameters of mechanical type advanced machining processes using genetic algorithms International Journal of Machine Tools & Manufacture 47, 2007.
[3]. A.A. Khan, M.M. Haque, Performance of different abrasive materials during abrasive water jet machining of glass Journal of Materials Processing Technology 191, 2007.
[4]. UshastaAich, Simul Banerjee, AsishBandyopadhyaya, Probal Kumar Das Abrasive Water Jet Cutting of Borosilicate Glass 3rd International Conference on Materials Processing and Characterisation (ICMPC 2014) Procedia Materials Science 6, 2014.
[5]. Vijaykumar pal, S.K. Choudury; Surface characterization and machining of blind pockets on Ti6Al4V by abrasive water jet machining, 2014.
N. Venkata Niranjan Kumar, Gowhami G “Optimization of AWJM Parameters on Machining Super Alloys Using Grey-Taguchi Method” International Journal of Research and Innovation in Applied Science -IJRIAS vol.3 issue 4 April 2018, pp.22-27 URL: https://www.rsisinternational.org/journals/ijrias/DigitalLibrary/Vol.3&Issue4/22-27.pdf
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Anupam Asthana, Akshit Gupta, Akshay Kumar Singh, Ekant Bajaj, Nishu Bansal – April 2018 Page No.: 28-30
Dynamic memory network has emerged as a next step towards the success of question answering. With synergy of Dynamic memory network [1] and Facebook bAbI dataset[7] , we aim at high performance result for the question answering task. The main focus of this project is to implement our own Dynamic Memory Network to achieve prominent results in question answering task. In Question Answering task, the Dynamic Memory Network takes input in form of story or paragraph through input module , processes the input and question asked and with episodic memory try to give the answer of the particular question.
- Page(s): 28-30
- Date of Publication: 24 April 2018
- Anupam Asthana
Department of Computer Science,
Ajay Kumar Garg Engineering College, Ghaziabad, U.P., India - Akshit Gupta
Department of Computer Science,
Ajay Kumar Garg Engineering College, Ghaziabad, U.P., India - Akshay Kumar Singh
Department of Computer Science,
Ajay Kumar Garg Engineering College, Ghaziabad, U.P., India - Ekant Bajaj
Department of Computer Science,
Ajay Kumar Garg Engineering College, Ghaziabad, U.P., India - Nishu Bansal
Asst. Professor, Department of Computer Science,
Ajay Kumar Garg Engineering College, Ghaziabad, U.P., India
References
[1]. Kumar, Ankit et al. (2015) . Ask me anything: Dynamic memory networks for natural language processing. arXiv preprint arXiv:1506.07285.
[2]. Hermann, Karl Moritz et al. Teaching Machines to Read and Comprehend. https://arxiv.org/abs/1506.03340
[3]. Xiong, Caiming et al. (2016) Dynamic Memory Networks for Visual and Textual Question Answering. https://arxiv.org/abs/1603.01417
[4]. Wetson, Jason et al. (2015) Memory Networks. ICLR 2015.https://arxiv.org/abs/1410.3916
[5]. Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. On ¨ the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259, 2014.
[6]. Raghuvanshi,Chase- Dynamic Memory Networks for Question Answering https://cs224d.stanford.edu/reports/RaghuvanshiChase.pdf
[7]. Jason Weston, Antoine Bordes, Sumit Chopra, and Tomas Mikolov. Towards ai-complete question answering: A set of prerequisite toy tasks. arXiv preprint arXiv:1502.05698, 2015
[8]. https://colah.github.io/posts/2015-08-Understanding-LSTMs/
Anupam Asthana, Akshit Gupta, Akshay Kumar Singh, Ekant Bajaj, Nishu Bansal “Question Answering using Dynamic Memory Network” International Journal of Research and Innovation in Applied Science -IJRIAS vol.3 issue 4 April 2018, pp.28-30 URL: https://www.rsisinternational.org/journals/ijrias/DigitalLibrary/Vol.3&Issue4/28-30.pdf
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Sunil R Meena, Bharat Kumar, Devesh Meena – April 2018 Page No.: 31-33
Present study aimed to assess and review the success criteria followed at the work place. Various parameters such as money, machinery, man-power, materials & management are studied. The main aim of the study is to determine the CSF that influences the project cycle by determining the relation between the 5M’s. Various techniques of project management are studied for determining the correlation between success factors.
- Page(s): 31-33
- Date of Publication: 04 May 2018
- Sunil R Meena
Faculty, School of Civil & Environmental Engineering,
Anand International of Engineering, Jaipur, Rajasthan, India - Bharat Kumar
Student, School of Civil & Environmental Engineering,
Anand International of Engineering, Jaipur, Rajasthan, India - Devesh Meena
Student, School of Civil & Environmental Engineering,
Anand International of Engineering, Jaipur, Rajasthan, India
References
[1]. Ali asghar Bavafa, Samieneh Motamed, Abdul Kadir marsona, “Significant Factors Affecting Safety Program Performance of Construction Firm in Iran” Volume 4 Issue (JETT), Page 71-77, (2016).
[2]. Jing Yang, Geoffrey Qiping Shen, Manfong Ho, “Exploring critical success factors for stakeholders management in construction project” Volume 4, Issue (IJCEM) Page 337-448, (2009).
[3]. Samart Homthong, Wutthipong Moungnoi, “critical success factors influencing project performance objectives, operation and Maintenances phases” (ISERD) April 2016.
[4]. Sumesh Sudheer Babu and Dr. Sudhakar, “Critical Success Factors Influencing Performance of Construction Projects” Volume 4, Issue 5, May 2015
[5]. B.C. Punmia and K.K. Khandelwale,”Project planning & Control with CPM & PERT”. Fourth edition, 2016.
Sunil R Meena, Bharat Kumar, Devesh Meena “Assessment of Critical Success Factors at Work Place” International Journal of Research and Innovation in Applied Science -IJRIAS vol.3 issue 4 April 2018, pp.31-33 URL: https://www.rsisinternational.org/journals/ijrias/DigitalLibrary/Vol.3&Issue4/31-33.pdf
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Dr. Arun Sharma, Dr. Vepa Kameswara Rao and Prof. (Dr.) Parshuram Singh – April 2018 Page No.: 34-40
Ultrasensitive sandwich type electrochemical immunosensors for the detection of various pathogens / toxins is described in this report using ferrocene functionalized antibodies as label. This scientific report is based on the electrochemical detection of various pathogens / toxins. In order to study the detection performance, various nanomaterials were used to modify the electrode surface. Further, this biofilm modified electrode surface was subjected to pathogens / toxins which are detected in an electrochemical sandwich immunoassay format using ferrocene tagged antibodies as labels. The limit of detection is reported here in the various immunosensing formats. In this report, we have discussed about the sensitive and stable sandwiched electrochemical immunosensing strategies using various nanomaterials as the immobilization platform to enhance the voltammetric signal and ferrocene derivatives as labels. The signal amplification strategy of ferrocene derivatives is quite promising which can be used for the electrochemical detection of various pathogens / toxins and is suitable for field use.
- Page(s): 34-40
- Date of Publication: 11 May 2018
- Dr. Arun Sharma
Head, Department of Chemistry,
Mahaveer Institute of Technology & Science (MITS), Jadan Pali, (Raj.), India - Dr. Vepa Kameswara Rao
Head of Biosensor Development Division & Associate Director,
Defence Research and Development Establishment, Gwalior (M.P.), India - Prof. (Dr.) Parshuram Singh
Head, Department of Physics,
Mahaveer Institute of Technology & Science (MITS), Jadan Pali, (Raj.), India
References
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Dr. Arun Sharma, Dr. Vepa Kameswara Rao and Prof. (Dr.) Parshuram Singh “Research Report on Recent Applications of Ferrocene Derivatives Based Electrochemical Immunosensors Using Nanomaterials” International Journal of Research and Innovation in Applied Science -IJRIAS vol.3 issue 4 April 2018, pp.34-40 URL: https://www.rsisinternational.org/journals/ijrias/DigitalLibrary/Vol.3&Issue4/34-40.pdf
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Vladimir Gurevich – April 2018 Page No.: 41-45
Automatic fire suppression systems, employing microprocessor-based equipment and microelectronic components connected via long cables with numerous sensors branched on a large area, are very sensitive to High Altitude Electromagnetic Pulse of nuclear explosion (HEMP). Unpredictable actuatin of these systems, with prior automatic disconnection of electric equipment as a result of HEMP impact, can lead to serious accidents in power systems. The article discusses the ways to improve resilience of automatic fire suppression systems of critical electric equipment.
- Page(s): 41-45
- Date of Publication: 11 May 2018
- Vladimir Gurevich
Central Electric Laboratory, Israel Electric Corp., POB 10, Haifa 31000, Israel
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Vladimir Gurevich “Improvement of HEMP Resilience of Automatic Fire Suppression System” International Journal of Research and Innovation in Applied Science -IJRIAS vol.3 issue 4 April 2018, pp.41-45 URL: https://www.rsisinternational.org/journals/ijrias/DigitalLibrary/Vol.3&Issue4/41-45.pdf
For Full Text Click here