Refine your search
Collections
Co-Authors
Journals
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
Pradeep, S.
- Biology, Feeding Potential, Standardization of Mass Production Techniques and Impact Study of Dipha Aphidivora Meyrick
Abstract Views :442 |
PDF Views:0
Authors
Affiliations
1 Raitha Samparka Kendra, Annigeri, Dharwad (karnataka), IN
2 University of Agricultural and Horticultural Sciences, Shimoga (karnataka), IN
3 Agri-business and Export Knowledge Centre, University of Agricultural Sciences, Dharwad (karnataka), IN
1 Raitha Samparka Kendra, Annigeri, Dharwad (karnataka), IN
2 University of Agricultural and Horticultural Sciences, Shimoga (karnataka), IN
3 Agri-business and Export Knowledge Centre, University of Agricultural Sciences, Dharwad (karnataka), IN
Source
International Journal of Plant Protection, Vol 7, No 2 (2014), Pagination: 302-311Abstract
Experiments were conducted on the biology, feeding potential and standardization of the mass production of Dipha aphidivora Meyrick predator on sugarcane woolly aphid, Ceratovacuna lanigera Zehntner during 2005-2007, at Agricultural Research Station (ARS), Honnavile, Shimoga (district), Karnataka, India. The duration of the first instar was 2.5 to 3.5 days (average 2.95+0.90 days). The average duration of second, third, fourth and fifth instar, pupal period, adult female and male moth lasted for 4.57+1.33, 8.30+1.11, 11.37+2.96 and 6.10+0.77 days, 8.50+2.15 days, 4.5+0.50 days, 1.5±0.30 days, respectively and the total larval period lasted for 24.5 to 39.5 days. The daily consumption rate by D. aphidivora was 30.8 aphids per day. D. aphidivora or aphid multiplied faster on 7-month-old crop than 5, 6 and 8 month old crop. At the rate of 50 number of D. aphidivora pupae release, highest populations of 4230 per shade net D. aphidivora were harvested. Highest populations of D. aphidivora were harvested when the shade nets were irrigated once in two days with the population of 4123 D. aphidivora per shade net than irrigated once in week with the population of 1490 D. aphidivora per shade net. During the experiment, average temperature was 280C and relative humidity was 78 per cent.Keywords
Ceratovacuna Laniger, Dipha Aphidivora, Biology, Mass Production, Feeding Potential- Implementation of Image Processing in Real-Time Road Traffic Control
Abstract Views :222 |
PDF Views:3
Authors
Affiliations
1 Bharathiyar College of Engineering & Technology, Karaikal, U.T. of Puducherry, IN
1 Bharathiyar College of Engineering & Technology, Karaikal, U.T. of Puducherry, IN
Source
Digital Image Processing, Vol 4, No 15 (2012), Pagination: 828-833Abstract
Existing traffic control system is using sensors. As the sensor which collects traffic flow information, mainly Ultrasonic Vehicle Detector has been used. This detector detects vehicle presence by the time difference of the reflection of ultrasonic wave fired from above the road surface to just under it. But especially queue and delay length are measured indirectly by the number of passed vehicles in a unit time. So a sensor which can collect more precise traffic flow information is needed. Also each Ultrasonic Vehicle Detector has to be installed above the road surface per a measurement lane and so there is a fear of spoiling the beauty of the city. On the other hand, the Digital Image processes an image received from the CCTV camera installed aside and above the approach lane at the traffic signal intersection. In this, queue length will be detected. To detect and measure queue parameters, two different algorithms have been used. The first algorithm is motion detection and the second is a vehicle detection operation. In this process, we detect moving vehicle grouping and delay vehicle grouping from the results of Image Preprocess, and calculate the delay range. Also, we make a stable output which is strong in the noise by smoothing the calculated delay range by referring to the output at the time of back and forth. After the queue length detection, depend upon the vehicles in four sides the preference will give to the vehicles. This saves the time and reduces the error in the existing system.Keywords
Ultrasonic Vehicle Detector, Motion Detection, Vehicle Detection, Queue Length Parameters.- Content Based Information Retrieval Using Relevance Techniques
Abstract Views :214 |
PDF Views:2
Authors
Affiliations
1 Department of Information Technology, Amrita School of Engineering, IN
1 Department of Information Technology, Amrita School of Engineering, IN
Source
Digital Image Processing, Vol 4, No 8 (2012), Pagination: 409-413Abstract
Recently, retrieving multimedia content has become an important research area. Content based retrieval in multimedia is a research problem since multimedia data needs detailed interpretation from pixel values. Information Retrieval (IR) can be defined as the activity of providing the user with his/her information need. Sometimes it is not possible to retrieve to the user the exact information they have in mind. The basic idea of relevance feedback is to shift the burden of finding the "Right query formulation" from the user to the system. To enable this feature, the user provides the system with Relevance Feedback. This user feedback typically takes the form of relevance judgments expressed over the resulting set. The "feedback loop" can then be iterated multiple times, until the user gets satisfied with the results. In this paper, techniques to improve relevance feedback in content based searches are discussed and our paper shows how relevance improves precision and recall.Keywords
Relevance Feedback, Precision, Recall, Dispersion, Threshold, F1 Score, Inverted Index, Jdbc-Odbc, Gython, Guess.- A New one Round Image Encryption Algorithm Based on Multiple Chaotic Systems
Abstract Views :387 |
PDF Views:0
Authors
Affiliations
1 Department of Electronics and Communication Engineering, P.A. College of Engineering and Technology, IN
1 Department of Electronics and Communication Engineering, P.A. College of Engineering and Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 5, No 4 (2015), Pagination: 1017-1023Abstract
This paper presents a bit level pixel by pixel chaotic image encryption scheme with a permutation-diffusion structure. In this scheme, a Neural Network Like Structure (NNLS) is proposed to do the diffusion efficiently. The plain image, split into 16 blocks is permuted by the keys from the key generator. Each key will produce different permutation order (PO) generated using a chaotic map. After permutation, the pixels of the permuted image is converted into bits and given to the proposed NNLS, where the diffusion takes place. The bit by bit diffusion performed by NNLS on the pixels will give more randomness to the cipher image. The performance of the NNLS is contingent upon sensitivity of the keys from the key generator. The test results and analyses performed using several security standards shows that the proposed scheme is more secure, reliable and can be used for real time image encryption.Keywords
One Round Encryption, Permutation, Diffusion, NPCR, UACI, NIST.- Seasonal Incidence of Dipha aphidivora Meyrick (Pyralidae:Lepidoptera) in Bhadra Command Areas
Abstract Views :220 |
PDF Views:1
Authors
Affiliations
1 Raitha Samparka Kendra, Annigeri, Dharwad (Karnataka), IN
2 Agricultural Research Station, University of Agricultural Sciences, Dharwad (Karnataka), IN
3 University of Agricultural and Horticultural Sciences, Shimoga (Karnataka), IN
1 Raitha Samparka Kendra, Annigeri, Dharwad (Karnataka), IN
2 Agricultural Research Station, University of Agricultural Sciences, Dharwad (Karnataka), IN
3 University of Agricultural and Horticultural Sciences, Shimoga (Karnataka), IN
Source
International Journal of Plant Protection, Vol 9, No 1 (2016), Pagination: 52-57Abstract
Investigation on the seasonal incidences of Dipha aphidivora Meyrick (Pyralidae: Lepidoptera) and its natural enemies from June 2005 to May 2006 undertaken at Agricultural Research Station, Honnavile, Bhadravathi and Shimoga district revealed an incidence ranging from 1.6 to 7.2 larvae per plant recorded at Shimoga district (Location I, Shettihalli), 1.1 to 7.9 larvae per plant at Shimoga district (Location II, Honnavile), 0.7 to 6.9 larvae per plant at Bhadravathi taluk (Location III, Barandur) and 1.2 to 6.5 larvae per plant at Bhadravathi taluk (Location IV, Tadsa). The highest incidence of 7.2, 7.9, 6.5 larvae per plant was observed during October month at Shimoga district, (Location I, Shettihalli), (location II, Honnavile) and Bhadravathi taluka (Location IV, Tadsa) and 6.5 larvae per plant was observed during September month at Bhadravathi taluk (Location III, Barandur). The lowest population of 1.6 and 1.1 larvae per plant was recorded during July month at location I, II, 0.7, and 1.2 larvae per plant was recorded at location III and location IV. The D. aphidivora population had two peaks in a year one at October and another at January. The correlation between the D. aphidivora population and weather parameters revealed that the incidence of D. aphidivora was negatively correlated with minimum temperature, maximum temperature and rainfall and positively correlated with relative humidity in all the four locations. The correlation between D. aphidivora population and SWA population showed significant positive correlation at location I, location III and location IV and non-significant negative correlation at location II. The natural enemies recorded on D. aphidivora were the field lizards and an unidentified bird, which devoured D. aphidivora. No parasites were recorded on D. aphidivora.Keywords
Seasonal Incidences, Dipha aphidivora, SWA, Sugarcane, Weather.- Volume Estimation of Existing and Potential Glacier Lakes, Sikkim Himalaya, India
Abstract Views :433 |
PDF Views:143
Authors
Affiliations
1 Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, IN
2 Sikkim State Council of Science and Technology, Department of Science and Technology and Climate Change, Development Area, Gangtok 737 101, IN
1 Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, IN
2 Sikkim State Council of Science and Technology, Department of Science and Technology and Climate Change, Development Area, Gangtok 737 101, IN
Source
Current Science, Vol 116, No 4 (2019), Pagination: 620-627Abstract
Glacial lake outburst floods (GLOFs) are a hazard commonly reported in the glaciated terrain of the Himalaya. Glacier lakes can form if the glaciers retreat and the bottom topography overdeepens. We have adopted a technique to estimate the depth and volume of lakes using parameters such as glacier surface velocity, slope and laminar flow of ice. The technique has been automated using Python programming language. The method was applied in the Sikkim Himalaya to map potential lake sites and also to predict further expansion of existing glacier lakes. Studies were carried out for ten glaciers in the Tista river basin. The analysis suggests nine potential lake sites, including further expansion of four existing glacier lakes. To validate the results, the model lake boundary in 2001 was compared with the satellite-observed value of 2015 and field measurements made at the South Lhonak lake. The volume of the South Lhonak lake (in 2015) was estimated as 60 ± 10.8 million m3; with prolonged retreat of the glacier, the lake is likely to expand to a maximum volume of 90 ± 16.2 million m3. The above technique can provide new information to planners, leading to a more realistic approach in understanding the disaster potential of glacier lakes.Keywords
Glacier Lakes, Depth and Volume Estimation, Remote Sensing, Disaster Potential, Over Deepening.References
- Kulkarni, A. V. and Karyakarte, Y., Observed changes in Himalayan glaciers. Curr. Sci., 2014, 106, 237–244.
- Kulkarni, A. V., Bahuguna, I. M., Rathore, B. P., Singh, S. K., Randhawa, S. S., Sood, R. K. and Dhar, S., Glacial retreat in Himalaya using Indian Remote Sensing satellite data. Curr. Sci., 2007, 92, 69–74.
- Bolch, T. et al., The state and fate of Himalayan glaciers. Science, 2012, 336, 310–314.
- Maanya, U. S., Kulkarni, A. V., Tiwari, A., Bhar, E. D. and Srinivasan, J., Identification of potential glacial lake sites and mapping maximum extent of existing glacier lakes in Drang Drung and Samudra Tapu glaciers, Indian Himalaya. Curr. Sci., 2016, 111, 553–560.
- Yao, X., Liu, S., Sun, M., Wei, J. and Guo, W., Volume calculation and analysis of the changes in moraine-dammed lakes in the north Himalaya: a case study of Longbasaba lake. J. Glaciol., 2012, 58(210), 753–760.
- Mason, K., Indus floods and Shyok glaciers. Himalayan J., 1929, 1, 10–29.
- Sangewar, C. V., Srivastava, D. and Singh, R. K., Reservoir within the Shaune Garang glacier, district Kinnaur, Himachal Pradesh. In Abstract Proceedings of the Symposium on Snow, Ice and Glaciers: A Himalayan Perspective, Geological Survey of India, 2001, 39–40.
- Dobhal, D. P., Gupta, A. K., Mehta, M. and Khandelwal, D. D., Kedarnath disaster: facts and plausible causes. Curr. Sci., 2013, 105, 171–174.
- Richardson, S. D. and Reynolds, J. M., An overview of glacial hazards in the Himalayas. Quaternary Int., 2000, 65, 31–47.
- Worni, R., Huggel, C. and Stoffel, M., Glacial lakes in the Indian Himalayas from an area-wide glacial lake inventory to on-site and modeling-based risk assessment of critical glacial lakes. Sci. Total Environ., 2013, 468, S71–S84.
- Kulkarni, A. V., Philip, G., Thakur, V. C., Sood, R. K., Randhawa, S. S. and Chandra, R., Glacier inventory of the Satluj Basin using remote sensing technique. Himalayan Geol., 1999, 20, 45–52.
- Randhawa, S. S., Sood, R. K., Rathore, B. P. and Kulkarni, A. V., Moraine-dammed lakes study in the Chenab and the Satluj river basins using IRS data. J. Indian Soc. Remote Sensing, 2005, 33, 285–290.
- Bhambri, R., Mehta, M., Dobhal, D. P. and Gupta, A. K., Glacier Lake Inventory of Uttarakhand, Wadia Institute of Himalayan Geology, Dehradun, 2015, 1st edn.
- Raj, K. B. G., Remya, S. N. and Kumar, K. V., Remote sensingbased hazard assessment of glacial lakes in Sikkim Himalaya. Curr. Sci., 2013, 104(3), 359–364.
- Raj, K. B. G. and Kumar, K. V., Inventory of glacial lakes and its evolution in Uttarakhand Himalaya using time series satellite data. J. Indian Soc. Remote Sensing, 2016, 44, 959–976.
- Nie, Y., Liu, Q. and Liu, S., Glacial lake expansion in the Central Himalayas by Landsat images, 1990–2010. PLoS ONE, 2013, 8, p.e 83973.
- Bolch, T., Peters, J., Yegorov, A., Pradhan, B., Buchroithner, M. and Blagoveshchensky, V., Identification of potentially dangerous glacial lakes in the northern Tien Shan. Nat. Hazards, 2011, 59, 1691–1714.
- Liu, J. J., Tang, C. and Cheng, Z. L., The two main mechanisms of glacier lake outburst flood in Tibet, China. J. Mt. Sci., 2013, 10, 239–248.
- O’Connor, J. E., Hardison, J. H. and Costa, J. E., Debris flows from failures of neoglacial-age moraine dams in the Three Sisters and Mount Jefferson Wilderness Areas, Oregon. US Geol. Surv.Prof. Pap., 2001, 1606, 1–93.
- Huggel, C., Kääb, A., Haeberli, W., Teysseire, P. and Paul, F., Remote sensing-based assessment of hazards from glacier lake outbursts: a case study in the Swiss Alps. Can. Geotech. J., 2002, 39, 316–330.
- Sakai, A., Glacial lakes in the Himalayas: a review on formation and expansion processes. Global Environ. Res., 2012, 16, 23–30.
- Patel, L. K., Sharma, P., Laluraj, C. M., Thamban, M., Singh, A. and Ravindra, R., A geospatial analysis of Samudra Tapu and Gepang Gath glacial lakes in the Chandra Basin, Western Himalaya. Nat. Hazards, 2017, 86, 1275–1290.
- Sharma, R. K., Pradhan, P., Sharma, N. P. and Shrestha, D. G., Remote sensing and in situ-based assessment of rapidly growing South Lhonak glacial lake in eastern Himalaya, India. Nat. Hazards, 2018, 93(1), 1–17.
- Gantayat, P., Kulkarni, A. V. and Srinivasan, J., Estimation of ice thickness using surface velocities and slope: case study at Gangotri Glacier, India. J. Glaciol., 2014, 60, 277–282.
- Farinotti, D. et al., How accurate are estimates of glacier ice thickness? Results from ITMIX, the Ice Thickness Models Intercomparison eXperiment. Cryosphere, 2017, 11, 949–970.
- Ganju, A., Kulkarni, A. V., Dhobhal, D. P., Kumar, B., Shukla, S. P. and Shrestha, D. G., South Lhonak glacial lake outburst flood, North Sikkim, 2015 (pers. commun.).
- Leprince, S., Barbot, S., Ayoub, F. and Avouac, J. P., Automatic and precise orthorectification, coregistration, and subpixel correlation of satellite images, application to ground deformation measurements. IEEE Trans. Geosci. Remote Sensing, 2007, 45, 1529–1558.
- Linsbauer, A., Paul, F. and Haeberli, W., Modeling glacier thickness distribution and bed topography over entire mountain ranges with GlabTop: application of a fast and robust approach. J. Geophys. Res. Earth Surf., 2012, 117, F03007.
- Cuffey, K. M. and Paterson, W. S. B., The Physics of Glaciers, Butterworth-Heinemann, Oxford, UK, 2010, 4th edn.
- Swaroop, S., Raina, V. K. and Sangeswar, C. V., Ice flow of Gangotri glacier. In Proceedings of the Workshop on Gangotri Glacier (eds Srivastava, D., Gupta, K. R. and Mukerji, S.), Geological Survey of India (Spec. Publ. 80), 26–28 March 2003.
- Farinotti, D., Huss, M., Bauder, A., Funk, M. and Truffer, M., A method to estimate the ice volume and ice-thickness distribution of alpine glaciers. J. Glaciol., 2009, 55, 422–430.
- Kamb, B. and Echelmeyer, K. A., Stress-gradient coupling in glacier flow: I. Longitudinal averaging of the influence of ice thickness and surface slope. J. Glaciol., 1986, 32, 267–284.
- Hutchinson, M. F., ANUDEM Version 5.3, Fenner School of Environment and Society, Australian National University, Canberra, 2011.
- ESRI, A., ArcGIS 10.1. Environmental Systems Research Institute, Redlands, CA, USA, 2012.
- Hollister, J. and Milstead, W. B., Using GIS to estimate lake volume from limited data. Lake Reservoir. Manage., 2010, 26, 194–199.
- Saraswat, P., Syed, T. H., Famiglietti, J. S., Fielding, E. J., Crippen, R. and Gupta, N., Recent changes in the snout position and surface velocity of Gangotri glacier observed from space. Int. J. Remote Sensing, 2013, 34, 8653–8668.
- Lee, D. S., Storey, J. C., Choate, M. J. and Hayes, R. W., Four years of Landsat-7 on-orbit geometric calibration and performance. IEEE Trans. Geosci. Remote Sensing, 2004, 42, 2786– 2795.
- Pfeffer, W. T. et al., The Randolph glacier inventory: a globally complete inventory of glaciers. J. Glaciol., 2014, 60, 537–552.
- Haeberli, W. and Hölzle, M., Application of inventory data for estimating characteristics of and regional climate-change effects on mountain glaciers: a pilot study with the European Alps. Ann. Glaciol., 1995, 21, 206–212.
- Fujita, K., Suzuki, R., Nuimura, T. and Sakai, A., Performance of ASTER and SRTM DEMs, and their potential for assessing glacial lakes in the Lunana region, Bhutan Himalaya. J. Glaciol., 2008, 54, 220–228.
- Large Losses in Glacier Area and Water Availability by the End of Twenty-First Century under High Emission Scenario, Satluj Basin, Himalaya
Abstract Views :363 |
PDF Views:138
Authors
Veena Prasad
1,
Anil V. Kulkarni
1,
S. Pradeep
1,
S. Pratibha
1,
Sayli A. Tawde
2,
Tejal Shirsat
1,
A. R. Arya
1,
Andrew Orr
3,
Daniel Bannister
3
Affiliations
1 Divecha Centre for Climate Change, IN
2 Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bengaluru 560 012, IN
3 British Antarctic Survey, Cambridge, CB3 0ET, GB
1 Divecha Centre for Climate Change, IN
2 Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bengaluru 560 012, IN
3 British Antarctic Survey, Cambridge, CB3 0ET, GB
Source
Current Science, Vol 116, No 10 (2019), Pagination: 1721-1730Abstract
Glaciers in the Satluj river basin are likely to lose 53% and 81% of area by the end of the century, if climate change followed RCP 8.5 scenario of CNRMCM5 and GFDL-CM3 models respectively. The large variability in area loss can be due to difference in temperature and precipitation projections. Presently, Satluj basin has approximately 2000 glaciers, 1426 sq. km glacier area and 62.3 Gt glacier stored water. The current mean specific mass balance is –0.40 m.w.e. a–1. This will change to –0.42 and – 1.1 m.w.e. a–1 by 2090, if climate data of CNRM-CM5 and GFDL-CM3 are used respectively. We have used an extreme scenario of GFDL-CM3 model to assess the changes in the contribution of glacier melt to the Bhakra reservoir. Mass balance model suggests that glaciers are contributing 2 km3 a–1 out of 14 km3 of water. This will increase to 2.2 km3 a–1 by 2050, and then reduce to 1.5 km3 a–1 by the end of the century. In addition, loss in glacier area by the end of century, will also increase the vulnerability of mountain communities, suggesting need for better adaptation and water management practices.Keywords
Climate Change, Glacier, Glacier Melt Runoff, Himalaya, Mass Balance, Satluj Basin, Water Availability.References
- Food and Agricultural Organization, Irrigation in Southern and Eastern Asia in figures AQUASTAT Survey-2011, 2012. FAO Water Report no. 37, 2012, pp. 129–138.
- Immerzeel, W. W., van Beek, L. P. H., Marc, F. P. and Bierkens, M. F. P., Climate change will affect the Asian water towers. Science, 2010, 328, 1382–1385.
- Bhakra Beas Management Board (BBMB) Report, 2016; http://bbmb.gov.in/writereaddata/Portal/Reports/15_1_BBMB-ANNUAL-REPORT-2015-16-E.pdf.
- Dharmadhikary, S., Unravelling Bhakra. Econ. Polit. Wkly., 2005, 41(3), 1–10.
- Pal, I., Lall, U., Robertson, A. W., Cane, M. A. and Bansal, R., Predictability of Western Himalayan river flow: melt seasonal inflow into Bhakra Reservoir in northern India. Hydrol. Earth Syst. Sci., 2013, 17(6), 2131–2146.
- SANDRP, 2013; https://sandrp.files.wordpress.com/2018/03/hep_performance_in_sutlej_river_basin_june2013.pdf (accessed on 29 May 2018).
- Singh, D., Gupta, R. D. and Jain, S. K., Study of long-term trend in river discharge of Sutlej river (N–W Himalayan region 2014). Geogr. Environ. Sustain., 2014, 7(3), 87–96.
- Singh, P. and Jain, S. K., Snow and glacier melt in the Satluj River at Bhakra Dam in the Western Himalayan region. Hydrol. Sci. J., 2002, 47(1), 93–106.
- Singh, P. and Jain, S. K., Modelling of streamflow and its components for a large Himalayan basin with predominant snowmelt yield. Hydrol. Sci. J., 2003, 48(2), 257–276.
- Oerlemans, J., Quantifying global warming from the retreat of glaciers. Science, 1994, 264(5156), 243–245.
- Kulkarni, A. V., Rathore, B. P., Mahajan, S. and Mathur, P., Alarming retreat of Parbati glacier, Beas basin, Himachal Pradesh. Curr. Sci., 2005, 88(11), 1844–1850.
- Chaturvedi, R. K., Kulkarni, A. V., Karyakarte, Y. and Bala, G., Glacial mass balance changes in the Karakoram and Himalaya based on CMIP5 multi-model climate projections. Climatic Change, 2014, 123(2), 315–328.
- Tawde, S. A., Kulkarni, A. V. and Bala, G., An estimate of glacier mass balance for the Chandra basin, western Himalaya, for the period 1984–2012. Ann. Glaciol., 2017, 55(75), 99–109.
- Dimri, A. P. and Dash, S. K., Wintertime climatic trends in the western Himalaya. Climatic Change, 2012, 111(3), 775–800.
- Allen et al., IPCC, 2014: Annex II: Glossary (eds Mach, K. J., Planton, S. and von Stechow, C.). In Climate Change 2014: Synthesis Report, Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Pachauri, R. K. and Meyer, L. A.,), IPCC, Geneva, Switzerland, 2014, pp. 117–130.
- Negi, H. S., Kanda, N., Shekha, M. S. and Ganju, A., Recent wintertime climatic variability over the North West Himalayan cryosphere. Curr. Sci., 2018, 114(4), 760–770.
- Bolch, T. et al., The state and fate of Himalayan glaciers. Science, 2012, 336(6079), 310–314.
- Kulkarni, A. V. and Karyakarte, Y., Observed changes in Himalaya glaciers. Curr. Sci., 2014, 106(2), 237–244.
- Kääb, A., Chiarle, M., Raup, B. and Schneider, C., Climate change impacts on mountain glaciers and permafrost. Global Planet. Change, 2007, 56(1), 7–9.
- Cogley, J. C., The future of Asia’s glaciers. Nature, 2017, 549, 166–167.
- Gardelle, J., Berthier, E., Arnaud, Y. and Kaab, A., Region-wide glacier mass balances over the Pamir Karakoram-Himalaya during 1999–2011. Cryosphere, 2013, 7(6), 1885–1886.
- Azam, M. F., Wagnon, P., Vincent, C., Ramanathan, A. L., Linda, A. and Singh, V. B., Reconstruction of the annual mass balance of Chhota Shigri Glacier (Western Himalaya, India) since 1969. Ann. Glaciol., 2014, 55, 69–80.
- Azam, M. F., Wagnon, P., Vincent, C., Ramanathan, A. L., Favier, V., Mandal, A. and Pottakka, G., Processes governing the mass balance of Chhota Shigri Glacier (western Himalaya, India) assessed by point-scale surface energy balance measurements. Cryosphere, 2014, 8, 2195–2217.
- Singh, M., Mishra, V. D., Thakur, N. K., Kulkarni, A. V. and Singh, M., Impact of climatic parameters on statistical stream flow sensitivity analysis for hydro power. J. Indian Soc. Remote Sens., 2009, 37(4), 601–614.
- Kaser, G., Großhauser, M. and Marzeion, B., Contribution potential of glaciers to water availability in different climate regimes. Proc. Natl. Acad. Sci., 2010, 107(47), 20223–20227.
- Moors, et al., Adaptation to changing water resources in the Ganges basin, northern India. Environ. Sci. Policy, 2011, 14(7), 758–769.
- Gantayat, P., Kulkarni, A. V. and Srinivasan, J., Estimation of ice thickness using surface velocities and slope: case study at Gangotri Glacier, India. J. Glaciol., 2014, 60(220), 277–282.
- Bahr, D. B., Meier, M. F. and Peckham, S. D., The physical basis of glacier volume–area scaling. J. Geophys. Res., 1997, 102, 20355–20362.
- Tawde, S. A., Kulkarni, A. V. and Bala, G., Estimation of glacier mass balance on a basin scale: an approach based on satellitederived snowlines and a temperature index model. Curr. Sci., 2016, 111(12), 1977–1989.
- Marzeion, B., Jarosch, A. H. and Hofer, M., Past and future sealevel change from the surface mass balance of glaciers. Cryosphere, 2012, 6, 1295–1322.
- Cuffey, K. and Paterson, W. S. B., The flow of ice masses. In. The Physics of Glaciers, Elsevier, Oxford, UK, 2010, pp. 285–398.
- Leprince, S., Barbot, S., Ayoub, F. and Avouac, J.-P., Automatic and precise orthorectification, coregistration, and subpixel correlation of satellite images, application to ground deformation measurements. IEEE Trans. Geosci. Remote Sens., 2007, 45(6), 1529–1558.
- Titarov, P. S., Evaluation of CARTOSAT 1 geometric potential. In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, Vol. XXXVII, Part B1, Beijing, 2008.
- Nuth, C. and Kääb, A., Co-registration and bias corrections of satellite elevation data sets for quantifying glacier thickness change. Cryosphere, 2011, 5(1), 271–290.
- Pieczonka, T., Bolch, T., Junfeng, W. and Shiyin, L., Heterogeneous mass loss of glaciers in the Aksu-Tarim Catchment (Central Tien Shan) revealed by 1976 KH-9 Hexagon and 2009 SPOT-5 stereo imagery. Remote Sens. Environ., 2013, 130, 233–244.
- Kääb, A., Berthier, E., Nuth, C., Gardelle, J. and Arnaud, Y., Contrasting patterns of early twenty-first-century glacier mass change in the Himalayas. Nature, 2012, 488, 495–498.
- Pratibha, S. and Kulkarni, A. V., Decadal change in supraglacial debris cover in Baspa basin, Western Himalaya. Curr. Sci., 2018, 114(4), 792–799.
- Huss, M., Density assumptions for converting geodetic glacier volume change to mass change. Cryosphere, 2013, 7, 877– 887.
- Pieczonka, T. and Bolch, T., Region-wide glacier mass budgets and area changes for the Central Tien Shan between ~1975 and 1999 using 861 Hexagon KH-9 imagery. Global Planet. Change, 2013, 128, 862.
- Taylor, K. E., Stouffer, R. J. and Meehl, G. A., An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc., 2012, 93, 485–498; doi:10.1175/BAMS-D-11-00094.1.
- Palazzi, E., von Hardenberg, J. and Terzago, S., Precipitation in the Karakoram-Himalaya: a CMIP5 view. Clim. Dynam., 2015, 45, 21–45.
- Anandhi, A., Frei, A., Pierson, D. C., Schneiderman, E. M., Zion, M. S., Lounsbury, D. and Matonse, A. H., Examination of change factor methodologies for climate change impact assessment. Water Resour. Res., 2013, 47(3), 1–10.
- Johannesson, T., Raymond, C. and Waddington, E., Time-scale for adjustment of glaciers to changes in mass balance. J. Glaciol., 1989, 35, 355–369.
- Huss, M. and Hock, R., Global-scale hydrological response to future glacier mass loss. Nat. Clim. Change, 2018, 8(2), 135.
- Maanya, U. S., Kulkarni, A. V., Tiwari, A., Bhar, E. D. and Srinivasan, J., Identification of potential glacial lake sites and mapping maximum extent of existing glacier lakes in Drang Drung and Samudra Tapu glaciers, Indian Himalaya. Curr. Sci., 2016, 111(3), 553–560.
- Pfeffer, W. T. et al., The Randolph glacier inventory: a globally complete inventory of glaciers. J. Glaciol., 2014, 60(221), 537– 552.
- Taylor, J. R., An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements, University Science Books, California, USA, 1997, 2nd edn.
- Gardner, A. S. et al., Reconciled estimate of glacier contributions to sea level rise: 2003 to 2009. Science, 2013, 340(6134), 852– 857.
- Zhao, L., Ding, R. and Moore, J. C., The high Mountain Asia glacier contribution to sea-level rise from 2000 to 2050. Ann. Glaciol., 2016, 57(71), 223–231.
- Lutz, A. F., Immerzeel, W. W., Shrestha, A. B. and Bierkens, M. F. P., Consistent increase in High Asia’s runoff due to increasing glacier melt and precipitation. Nat. Clim. Change, 2014, 4, 587– 592.
- Laser Technology in Prosthodontics: A Review
Abstract Views :181 |
PDF Views:0
Authors
Affiliations
1 Additional Professor, Dept. of Prosthodontics, Manipal College of Dental Sciences, Manipal - 576104, IN
1 Additional Professor, Dept. of Prosthodontics, Manipal College of Dental Sciences, Manipal - 576104, IN
Source
Indian Journal of Public Health Research & Development, Vol 10, No 11 (2019), Pagination: 2522-2527Abstract
Laser is an acronym, which stands for light amplification by stimulated emission of radiation. Several decades ago, the laser was a death ray, the ultimate weapon of destruction, something you would only find in a science fiction story. Then lasers were developed and actually used, among other places, in light shows. The beam sparkled, it showed pure, vibrant and intense colors. Today the laser is used in the scanners at the grocery store, in compact disc players, and as a pointer for lecturer and above all in medical and dental field. The image of the laser has changed significantly over the past several years. With dentistry in the high tech era, we are fortunate to have many technological innovations to enhance treatment, including intraoral video cameras, CAD-CAM units, RVGs and air-abrasive units. However, no instrument is more representative of the term high-tech than, the laser. Dental procedures performed today with the laser are so effective that they should set a new standard of care. The scientific method and artistic details prescribed for reconstructive dentistry are well has been documented. The current use of lasers in reconstructive dentistry encompasses a wide variety of soft tissue procedures. The future may hold promise for hard tissue laser use in crown preparation, bone recontouring, and implant placement.Keywords
Laser Beam, High Tech Era, Prosthodontics, Technology.- Flexible Dentures: An Review
Abstract Views :171 |
PDF Views:0
Authors
Affiliations
1 Additional Professor, Dept. of Prosthodontics, Manipal College of Dental Sciences, IN
1 Additional Professor, Dept. of Prosthodontics, Manipal College of Dental Sciences, IN
Source
Indian Journal of Public Health Research & Development, Vol 10, No 11 (2019), Pagination: 2558-2560Abstract
Patients looking for a comfortable alternative to traditional dentures could be interested in Flexible dentures, using advanced technology to fit around the shape of teeth and gums upon insertion, the flexible denture can be used when there is not enough bone for fitting dental implants to replace missing teeth. The flexible denture works by flexing into position, not needing the use of metal clasping mechanisms to hold the dentures in place. Individuals with irregularities in the shape of their mouth could benefit greatly from the treatment and experience a growth to their self-confidence.Keywords
Removable Partial Denture, Flexible Denture, Undercuts.- Deep Learning Based Ultrasound Image Classification for Improved and Better Medical Diagnosis
Abstract Views :367 |
PDF Views:0
Authors
Affiliations
1 Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, IN
1 Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, IN
Source
International Journal of Emerging Trends in Science & Technology, Vol 6, No 1 (2020), Pagination: 10-14Abstract
Ultrasound is the best imaging techniques for detection of abnormalities in the human body. Ultrasound is a medical imaging technique in which a transducer transmits and receives the ultrasound waves to and from the organs of the human body. Ultrasound waves are high-frequency wave ranges from 20 KHz to Giga Hertz. Ultrasound methods are non-invasive, pain-free and patient-friendly techniques. Detection of abnormalities using Ultrasound helps doctors to treat the patient. Abnormalities in liver, Breast, Kidney, Uterus, Heart, Liver, Nerves, Prostate found out using ultrasound techniques has a list of unidentifiable problems in Medical Images in traditional methods. Deep learning plays a vital role in the modern era for much problem identification in medical imaging and other domain.Keywords
Classification, Convolutional Neural Network, Linear Regression, Medical Imaging, Random Forest.References
- Y. Hiramatsu, C. Muramatsu, H. Kobayashi, T. Hara, and H. Fujita, “Automated detection of masses on whole breast volume ultrasound scanner: False positive reduction using deep convolutional neural network,” In: Proceedings of the SPIE Medical Imaging, Orlando, FL, USA, Bellingham: SPIE, February 11-16, 2017.
- C. Bian, R. Lee, Y. Chou, and J. Cheng, “Boundary regularized convolutional neural network for layer parsing of breast anatomy in automated whole breast ultrasound,” In: M. Descoteaux, L. Maier-Hein, A. Franz, P. Jannin, D. Collins, S. Duchesne, (ed.), Medical Image Computing and Computer-Assisted Intervention (MICCAI), Berlin: Springer, pp. 259-266, 2017.
- M. Manikandan, N. V. Andrews, and V. Kavitha, “Investigation on micro classification of breast cancer from mammogram image sequence,” International Journal of Pure and Applied Mathematics, vol. 118, no. 20, pp. 645-649, 2018.
- T. Doan, and J. Kalita, “Selecting machine learning algorithms using regression models,” 2015 IEEE International Conference on Data Mining Workshop (ICDMW), 2015.
- I. Fasel, and J. Berry, “Deep belief networks for realtime extraction of tongue contours from ultrasound during speech,” In: Proceedings of 2010 20th International Conference on Pattern Recognition, Istanbul, Turkey, pp. 1493-1496, August 23-26, 2010.
- S. Keerthi, and S. Dhivya, “Comparison of RVM and SVM classifier performance in analysing the tuberculosis in chest X Ray,” International Journal of Control Theory and Applications, vol. no. 10, no. 36, pp. 269276, 2017.
- A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Advances in Neural Information Processing Systems, vol. 25, no. 2, pp. 1097-1105, 2012.
- S. Mohanapriya, and M. Vadivel, “Automatic retrival of MRI brain image using multiqueries system,” 2013 International Conference on Information Communication and Embedded Systems (ICICES), INSPEC Accession Number: 13485254, doi: 10.1109/ ICICES.2013.6508214, pp. 1099-1103, 2013.
- C. Dong, C. L. Chen, and X. Tang, “Accelerating the super-resolution convolutional neural network,” European Conference Computer Vision, pp. 391-407, 2016.
- K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep convolutional neural network for inverse problems in imaging,” IEEE Transactions on Image Processing, vol. 26, no. 9, pp. 4509-4522, September 2017.
- K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 770-778, 2016.
- M. Annakamatchi, and V. Keralshalini, “Design of spiral shaped patch antenna for bio-medical applications,” International Journal of Pure and Applied Mathematics, vol. 118, no. 11, pp. 131-135, 2018.
- J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?,” In: Advances in Neural Information Processing Systems 27 (NIPS 2014), pp. 3320-3328, 2014.
- S. Azizi, F. Imani, B. Zhuang, A. Tahmasebi, J. T. Kwak, ......, and P. Abolmaesumi, “Ultrasound-based detection of prostate cancer using automatic feature selection with deep belief networks,” In: N. Navab, J. Hornegger, W. Wells, A. Frangi, (ed.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, Berlin: Springer, pp. 70-77, 2015.