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
Sivaram, M.
- A Habitat Suitability Index Model for Nilgiri Tahr (Hemitragus hylocrius Ogilby) in Eravikulam National Park, Kerala
Abstract Views :401 |
PDF Views:0
Authors
Source
Indian Forester, Vol 133, No 10 (2007), Pagination: 1289-1304Abstract
The Nilgiri tahr, Hemitragus hylocrius Ogilby, is an endangered mountain goat distributed in southern Western Ghats of Peninsular India. Monitoring tahr habitats and selection of sites for management interventions and reintroduction of the tahr in their natural environment require an understanding of habitat suitability and hence this study was undertaken. The estimated tahr population in Kerala was around 1,000 individuals on 11 sites. The Eravikulam National Park (ENP) was chosen for developing the Habitat Suitability Index (HSI) model since it has the sole viable population of about 700 individuals. Based on the habitat utilization, the ENP was divided into different blocks. The relational trends between block-wise tahr density and the critical habitat variables viz., altitude, extent of cliff and availability of principal food species were examined through correlation analysis for evolving habitat suitability criteria. This formed the basis for deriving suitability indices, ranging from 0 to 1, which were subsequently used for developing the HSI models. In order to find out how well the HSI models captured variation in tahr density, they were subjected to regression analysis. The analyses indicate that the HSI models were satisfactory considering the limited number of factors involved. However, the models should be evaluated in other habitat conditions especially in fragmented habitats considering their degradation factors such as human disturbance, grazing pressure.
- Impact of Indarbela quadrinotata on the Growth of Casuarina equisetifol1a
Abstract Views :378 |
PDF Views:2
Authors
Source
Indian Forester, Vol 136, No 2 (2010), Pagination: 182-186Abstract
Several insect pests are associated with Casuarina equisetifolia L. in India. Among them, the bark eating caterpillar, Indarbela quadrinotata Walker (Metarbelidae: Lepidoptera) has attained serious pest proportions in Tamil Nadu State, which is located in the southern part of the country. The impact of I. quadrinotata on the growth of C. equisetifolia was studied in selected plantations of this species, located in three agro-climatic zones of the State. The data generated suggest that I. quadrinotata has the potential to reduce the growth of the trees significantly.Keywords
Casuarina equisetifolia, Caterpillar, Indarbela quadrinotata, Insect Pests, Tamil Nadu- Detection of Accurate Facial Detection using Hybrid Deep Convolutional Recurrent Neural Network
Abstract Views :262 |
PDF Views:0
Authors
Affiliations
1 Department of Information Technology, Lebanese French University, IQ
1 Department of Information Technology, Lebanese French University, IQ
Source
ICTACT Journal on Soft Computing, Vol 9, No SP 2 (2019), Pagination: 1844-1850Abstract
Facial Landmark discovery is an imperative issue in numerous PC vision applications about appearances. It is extremely testing as human faces in wild conditions regularly present expansive varieties fit as a fiddle because of various stances, impediments or demeanors. Profound neural systems have been connected to take in the guide from face pictures to confront shapes. To the best of our insight, Recurrent Neural Network (RNN) has not been utilized in this issue yet. In this paper, we propose a technique which uses RNN and Deep Neural Network (DNN) to take in the face shape. To start with, we design a system utilizing Convolutional Neural Network (CNN) to get the underlying Landmark estimation of appearances. At that point, we utilize feed-forward neural systems for neighborhood look where a segment based seeking technique is investigated. By utilizing LSTM- CNN-RNN, the underlying estimation is more dependable which makes the accompanying segment based pursuit doable and exact. Tests demonstrate that the worldwide system utilizing CNN-LSTM-RNN shows signs of improvement results than past systems in the two recordings and single picture. Our technique beats the cutting edge calculations particularly regarding fine estimation of Landmark spots.Keywords
Facial landmark, Deep Neural Network, Recurrent Neural Network, Convolutional Neural Network.References
- T. Weise, S. Bouaziz, H. Li and M. Pauly, “Realtime Performance-based Facial Animation”, ACM Transactions on Graphics, Vol.30, No. 4, pp. 71-77, 2011.
- Q. Cai, D. Gallup, C. Zhang and Z. Zhang, “3D Deformable Face Tracking with a Commodity Depth Camera”, Proceedings of 11th International Conference on European Conference on Computer Vision, pp. 229-242, 2010.
- G. Fanelli, M. Dantone, and L.V. Gool, “Real Time 3D Face Alignment with Random Forests-based Active Appearance Models”, Proceedings of 10th International Conference and Workshops on Automatic Face and Gesture Recognition, pp. 1-8, 2013.
- Z.Z. Zhang, W. Zhang, J.Z. Liu and X.O. Tang, “Multiview Facial Landmark Localization in RGB-D Images via Hierarchical Regression With Binary Patterns”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 24, No. 9, pp. 1475-1485, 2014.
- T. Cootes, C.J. Taylor, D.H. Cooper and J. Graham, “Active Shape Models-Their Training and Application”, Computer Vision and Image Understanding, Vol. 61, No. 1, pp. 38-59, 1995.
- T. Cootes, G.J. Edwards and C.J. Taylor, “Active Appearance Models”, Available at: https://www.cs.cmu.edu/~efros/courses/AP06/Papers/cootes-eccv-98.pdf.
- I. Matthews and S. Baker, “Active Appearance Models Revisited”, International Journal of Computer Vision, Vol. 6, No. 2, pp. 135-164, 2004.
- T. Cootes, G.J. Edwards and C.J. Taylor. “Active Appearance Models”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 6, pp. 681-685, 2001.
- X. Liu, “Generic Face Alignment using Boosted Appearance Model”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2007.
- D. Cristinacce and T. Cootes, “Automatic Feature Localization with Constrained Local Models”, Pattern Recognition, Vol. 41, No. 10, pp. 3054-3067, 2008.
- D. Cristinacce and T. Cootes, “Feature Detection and Tracking with Constrained Local Models”, Proceedings of International Conference on British Machine Vision Conference, pp. 929-938, 2006.
- J. Saragih, S. Lucey and J. Cohn, “Deformable Model Fitting by Regularized Landmark Mean-Shift”, International Journal of Computer Vision, Vol. 91, No. 2, pp. 200-215, 2011.
- Y. Wang, S. Lucey and J. Cohn, “Enforcing Convexity for Improved Alignment with Constrained Local Model”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008.
- L. Gu, and T. Kanade, “A Generative Shape Regularization Model for Robust Face Alignment”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 413-426, 2008.
- P. Dollar, P. Welinder and P. Perona, “Cascaded Pose Regression”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 1078-1085, 2010.
- T.C. Patrick Sauer and C. Taylor, “Accurate Regression Procedures for Active Appearance Models”, Proceedings of International Conference on European Conference on Computer Vision, 2011, pp. 1-30, 2011.
- J. Saragih and R. Goecke, “A Nonlinear Discriminative Approach to AAM Fitting”, Proceedings of 7th IEEE International Conference on Computer Vision, pp. 1-8, 2007.
- X.D. Cao, Y.C. Wei, F. Wen and J. Sun, “Face Alignment by Explicit Shape Regression”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2887-2894, 2012.
- F. Visin et al., “ReNet: A Recurrent Neural Network based Alternative to Convolutional Networks”, Available at: https://arxiv.org/pdf/1505.00393.pdf.
- Y. Sun, Q. Liu, H. Lu, “Low Rank Driven Robust Facial Landmark Regression”, Neurocomputing, Vol. 151, pp. 196-206, 2015.
- M. Ozuysal, M. Calonder, V. Lepetit and P. Fua, “Fast Key-Point Recognition using Random Ferns”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 3, pp. 448-461, 2009.
- N. Duffy and D.P. Helmbold, “Boosting methods for Regression”, Machine Learning, Vol. 47, No. 2, pp. 153-200, 2002.
- J.H. Friedman, “Greedy Function Approximation: A Gradient Boosting Machine”, Available at: https://statweb.stanford.edu/~jhf/ftp/trebst.pdf.
- D.G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision, Vol. 60, No. 2, pp. 91-110, 2004.
- M. Calonder, V. Lepetit, C. Strecha and P. Fua, “Brief: Binary Robust Independent Elementary Features”, Proceedings of 10th IEEE International Conference on Computer Vision, pp. 778-792, 2010.
- V. Lepetit, P. Lagger and P. Fua, “Randomized Trees for Real-Time Keypoint Recognition”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 775-781, 2005.
- E. Rublee, V. Rabaud, K. Konolige and G. Bradski, “ORB: An Efficient Alternative to SIFT or SURF”, Proceedings of International Conference on Computer Vision, pp. 2564-2571, 2011.
- S. Leutenegger, M. Chli and R. Siegwart, “Brisk: Binary Robust Invariant Scalable Keypoints”, Proceedings of International Conference on Computer Vision, pp. 2548-2555, 2011.
- A. Alahi, R. Ortiz and P. Vandergheynst, “FREAK: Fast Retina Keypoint”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 510-517, 2012.
- X.P. Burgos-Artizzu, P. Perona and P. Dollar. “Robust Face Landmark Estimation Under Occlusion”, Proceedings of International Conference on Computer Vision, pp. 1513-1520, 2013.
- Y. Sun, X.G. Wang and X.O. Tang, “Deep Convolutional Network Cascade for Facial Point Detection”, Proceedings of International Conference on Computer Vision, pp. 3476-3483, 2013.
- J. Zhang, S.G. Shan, M.N. Kan and X.L. Chen, “Coarse-to-Fine Auto-Encoder Networks (CFAN) for Real-Time Face Alignment, Proceedings of International Conference on European Conference on Computer Vision, pp. 1-16, 2014.
- Y. Chen, W. Luo and J. Yang. “Facial Landmark Detection via Pose-Induced Auto-Encoder Networks”, Proceedings of International Conference on Image Processing, pp. 27-30, 2015.
- Z.P. Zhang, P. Luo, C.C Loy and X.O. Tang, “Facial Landmark Detection by Deep Multi-Task Learning”, Proceedings of International Conference on European Conference on Computer Vision, pp. 94-108, 2014.
- S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory”, Neural Computing, Vol. 9, No. 8, pp. 1735-1780, 1997.
- A. Graves et al., “A Novel Connectionist System for Unconstrained Handwriting Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, No. 5, pp. 855-868, 2009.
- C. Plahl, M. Kozielski, R. Schluter and H. Ney, “Feature Combination and Stacking of Recurrent and Non-Recurrent Neural Networks for LVCSR”, Proceedings of International Conference on Acoustics, Speech and Signal Processing, pp. 6714-6718, 2013.
- M. Wollmer et al., “Online Driver Distraction Detection using Long Short-Term Memory”, IEEE Transactions on Intelligent Transportation Systems, Vol. 12, No. 2, pp. 574-582, 2011.
- S. Hochreiter, Y. Bengio, P. Frasconi and J. Schmidhuber, “Gradient Flow in Recurrent Nets: The Difficulty of Learning Long-Term Dependencies”, IEEE Press, 2001.
- M. Schuster and K.K. Paliwal, “Bidirectional Recurrent Neural Networks”, IEEE Transactions on Signal Processing, Vol. 45, No. 11, pp. 2673-2681, 1997.
- A. Graves and J. Schmidhuber, “Framewise Phoneme Classification with Bidirectional LSTM and other Neural Network Architectures”, Neural Networks, Vol. 18, No. 5-6, pp. 602-610, 2005.
- A. Maas et al., “Recurrent Neural Networks for Noise Reduction in Robust ASR”, Available at: http://www1.icsi.berkeley.edu/~vinyals/Files/rnn_denoise_2012.pdf.
- C. Cao, Q. Hou and K. Zhou, “Displaced Dynamic Expression regression for Real-Time Facial Tracking and Animation”, Proceedings of ACM Conference on Special Interest Group on Computer Graphics, Vol. 33, No. 4, pp. 142-147, 2014.
- C. Sagonas, G. Tzimiropoulos, S. Zafeiriou and M. Pantic, “The First Facial Landmark Localization Challenge”, Proceedings of IEEE International Conference on Computer Vision, pp. 41-52, 2013.
- A. Asthana, S. Zafeiriou, S. Cheng and M. Pantic, “Robust Discriminative Response Map Fitting with Constrained Local Models”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3444-3451, 2013.
- X. Yu, J. Huang, S. Zhang, W. Yan, D. N. Metaxas, “Pose-Free Facial Landmark Fitting via Optimized Part Mixtures and Cascaded Deformable Shape Model”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 1944-1951, 2013.
- X. Zhu and D. Ramanan, “Face Detection, Pose Estimation, and Landmark Localization in the Wild”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 2879-2886, 2012.
- F. Weninger, J. Bergmann and B. Schuller, “Introducing Currennt-The Munich Open-Source CUDA Recurrent Neural Network Toolkit”, Journal of Machine Learning Research, Vol. 16, pp. 547-551, 2015.
- P.N. Belhumeur, D.W. Jacobs, D.J. Kriegman and N. Kumar, “Localizing Parts of Faces using a Consensus of Exemplars”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 545-552, 2013.
- V. Le, J. Brandt, Z. Lin, L. Bourdev and T.S. Huang, “Interactive Facial Feature Localization”, Proceedings of International Conference on European Conference on Computer Vision, pp. 679-692, 2012.
- J. Yang, J.K. Deng, K.H. Zhang and Q.S. Liu, “Facial Shape Tracking Via Spatio-Temporal Cascade Shape Regression”, Proceedings of the IEEE International Conference on Computer Vision Workshop, pp. 41-49, 2015.
- G.S. Chrysos, E. Antonakos, S. Zafeiriou and P. Snape. “Offline Deformable Face Tracking in Arbitrary Videos, Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 32-35, 2015.
- J. Shen, S. Zafeiriou, G.S. Chrysos, J. Kossaifi, G. Tzimiropoulos and M. Pantic, “The First Facial Landmark Tracking in-the-Wild Challenge: Benchmark and Results”, Proceedings of IEEE International Conference and Workshop on Computer Vision, pp. 11-17, 2015.
- G. Tzimiropoulos, “Project-Out Cascaded Regression with an Application to Face Alignment”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3659-3667, 2015.
- C. Sagonas, E. Antonakos, G, Tzimiropoulos, S. Zafeiriou and M. Pantic, “300 faces In-the-wild challenge: Database and Results”, Image and Vision Computing, Vol. 47, pp. 3-18, 2016.
- C. Sagonas, G. Tzimiropoulos, S. Zafeiriou and M. Pantic, “A Semi-Automatic Methodology for Facial Landmark Annotation”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2111-2117, 2013.
- S. Bell, K. Bala, L. Zitnick and R. Girshick, “Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2874-2883, 2016.
- V. Kazemi and J. Sullivan, “One Millisecond Face Alignment with an Ensemble of Regression Trees”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1867-1874, 2014.
- A. Graves and J. Schmidhuber, “Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks”, Proceedings of the 21st International Conference on Neutral Information Processing Systems, pp. 545-552, 2008.
- W. Byeon, T.M. Breuel, F. Raue and M. Liwicki, “Scene Labeling with LSTM Recurrent Neural Networks”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 111-117, 2015.
- Privacy Preserving Data Mining using Threshold Based Fuzzy C-Means Clustering
Abstract Views :500 |
PDF Views:1
Authors
Affiliations
1 Department of Information Technology, Lebanese French University, Erbil, IQ
1 Department of Information Technology, Lebanese French University, Erbil, IQ
Source
ICTACT Journal on Soft Computing, Vol 9, No 1 (2018), Pagination: 1813-1816Abstract
Privacy preserving is critical in the field of where data mining is transformed into cooperative task among individuals. In data mining, clustering algorithms are most skilled and frequently used frameworks. In this paper, we propose a privacy-preserving threshold clustering that uses code based technique with threshold estimation for sharing of secret data in privacy-preserving mechanism. The process includes code based methodology which enables the information to be partitioned into numerous shares and handled independently at various servers. The proposed method takes less number of iterations in comparison with existing methods that does not require any trust among the clients or servers. The paper additionally provides experimental results on security and computational efficiency of proposed method.Keywords
Privacy Preserving, Data Mining, Threshold Cryptography, Fuzzy C-Means Clustering, Vandermonde Matrix, Secure Multiparty Computation.References
- R. Agrawal and R. Srikant, “Privacy Preserving Data Mining. ACM SIGMOD”, Proceedings of International Conference on Management of Data, pp. 439-450, 2000.
- Y. Lindell and Pinkas, “Privacy Preserving Data Mining”, Journal of Cryptology, Vol. 15, No. 3, pp. 177-183, 2002.
- A. Shamir, “How to Share a Secret”, Communications of the ACM, 1979.
- M. Mignotte. “How to Share a Secret”, Proceedings of Workshop on Cryptography, pp. 371-375, 1983.
- Josef Pieprzyk and Xian-Mo Zhang, “Ideal Threshold Schemes from MDS Codes”, Proceedings of International Conference on Information Security and Cryptology, pp. 253-263, 2003.
- B. Pinkas, “Cryptographic Techniques for Privacy-Preserving Data Mining”, Available at: http://www.pinkas.net/PAPERS/sigkdd.pdf.
- S. Verykios et al., “State of the-Art in Privacy Preserving Data Mining”, ACM SIGMOD Record, Vol. 33, No. 1, pp. 50-57, 2004.
- V Baby and Subhash N Chandra, “Privacy-Preserving Distributed Data Mining Techniques: A Survey”, International Journal of Computer Applications, Vol. 143, No. 10, pp. 37-41, 2016.
- J. Brickell and V. Shmatikov, “Privacy-Preserving Classifier Learning”, Proceedings of 13th International Conference on Financial Cryptography and Data Security, pp. 1-6, 2009.
- G. Jagannathan and R.N. Wright, “Privacy-Preserving Distributed k-Means Clustering over Arbitrarily Partitioned Data”, Proceedings of 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 593-599, 2005.
- P. Bunn and R. Ostrovsky, “Secure Two-Party K-Means Clustering”, Proceedings of ACM International Conference on Computer and Communications Security, pp. 486-497, 2007.
- M. Upmanyu, A.M. Namboodiri, K. Srinathan and C.V. Jawahar, “Efficient Privacy Preserving K-Means Clustering”, Proceedings of Pacific-Asia Workshop on Intelligence and Security Informatics, pp. 154-166, 2010.
- E. Bertino, I.N. Fovino and L.P. Provenza. “A Framework for Evaluating Privacy Preserving Data Mining Algorithms”, Data Mining and Knowledge Discovery, Vol. 11, No. 2, pp. 121-154, 2005.
- Autonomous Greedy Routing in Wireless Sensor Networks
Abstract Views :260 |
PDF Views:0
Authors
Affiliations
1 Department of Computing Science and Engineering, Galgotias University, IN
2 Department of Information Technology, Lebanese French University, IQ
1 Department of Computing Science and Engineering, Galgotias University, IN
2 Department of Information Technology, Lebanese French University, IQ
Source
ICTACT Journal on Communication Technology, Vol 10, No 1 (2019), Pagination: 1947-1952Abstract
Routing is challenging issue in WSN: Cryptography and key management schemes seem good, but they are too expensive in WSN. Prevention-based and detection based are the two approaches that are used in MANET. In prevention-based approaches a centralized key management is required, These applications require a good Quality of Service (QoS) from sensor networks, such as, minimum percentage of sensor coverage in the required area, continuous service during required time slot with minimum (or limited) resources (like sensor energy and channel bandwidth) and minimum outside intervention. The whole network may be affected if the infrastructure is destroyed. So this approach is used to prevent misbehavior, but not detect malicious based routes Detection based approaches are used to detect selfish node along with route that helps to identify malicious misbehavior route. Detection based approaches are based on trust in MANETs. Hence this approach is used to calculate the trust value in trust management schemes. The proposed scheme differentiates, routes, data packets and control packets, and also excludes the other causes that results in dropping packets, such as unreliable wireless connections and buffer overflows. The proposed scheme in a MANET routing protocol, evaluation of the AODV (Adhoc on demand on distance vector) and Low Energy Adaptive Clustering Hierarchy (LEACH) protocol with the NS2 simulator.Keywords
MANET, WSN, Routing, Quality of Service, AODV.References
- M.A. Rahman, M.S. Islam and A. Talevski, “Performance Measurement of Various Routing Protocols in Ad-Hoc Network”, Proceedings of International Multi Conference of Engineers and Computer Scientists, pp. 18-20, 2009.
- R.P. Gupta, D.V.K. Sharma and V.M. Shrimal, “Investigation of Different Parameters of Dynamic Source Routing with varied Terrain Areas and Pause Time for Wireless Sensor Network”, International Journal of Modern Engineering Research, Vol. 1, No. 2, pp. 626-631, 2011.
- J.N. Al-Karaki and A.E. Kamal, “Routing Techniques in Wireless Sensor Networks: A Survey”, IEEE Wireless Communications, Vol. 11, No. 6, pp. 6-28, 2004.
- T. Van Dam and K. Langendoen, “An Adaptive Energy-Efficient MAC Protocol for Wireless Sensor Networks”, Proceedings of 1st International Conference on Embedded Networked Sensor Systems, pp. 171-180, 2003.
- Vijay Mohan Shrimal, Ravindra Prakash Gupta and Virendra Kumar Sharma, “Investigation of Adhoc Topology AODV for Wireless Sensor Networks for Varying Terrain Areas for Different Speed (Node Speed)”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 2, No. 1, pp. 12-18, 2012.
- L. Breslau, D. Estrin, K. Fall, S. Floyd, J. Heidemann, A. Helmy and H. Yu, “Advances in network simulation”, Computer, Vol. 33, No. 5, pp. 59-67, 2000.
- K. Fall and K. Varadhan, “The ns Manual (formerly ns Notes and Documentation)”, Available at: https://www.isi.edu/nsnam/ns/doc/ns_doc.pdf.
- Imad Aad, Mohammad Hossein Manshaei and Jean Pierre Hubaux, “ns2 for the Impatient”, Available at: http://www.manshaei.org/files/HoE-ns2-Mobnet09.pdf.
- A. Aziz, S. Rahayu, N.A. Endut, S. Abdullah, M. Daud and M. Norazman, “Performance Evaluation of AODV, DSR and DYMO Routing Protocol in MANET”, Scientific Research Journal, Vol. 5, No. 2, pp. 49-65, 2008.
- I.F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, “Wireless Sensor Networks: A Survey”, Computer Networks, Vol. 38, No. 4, pp. 393-422, 2002.
- Tree Allometric Equations in South Asia
Abstract Views :298 |
PDF Views:0
Authors
Affiliations
1 Soil Science Department, Kerala Forest Research Institute, Peechi, Thrissur, Kerala, IN
2 Statistics Department, National Diary Research Institute, Banglore, IN
3 Forest Department, Food and Agriculture Organization of the United Nations, Vialedelle Terme di Caracalla, Rome, IT
4 Statistics Department, Kerala Forest Research Institute, Peechi, IN
1 Soil Science Department, Kerala Forest Research Institute, Peechi, Thrissur, Kerala, IN
2 Statistics Department, National Diary Research Institute, Banglore, IN
3 Forest Department, Food and Agriculture Organization of the United Nations, Vialedelle Terme di Caracalla, Rome, IT
4 Statistics Department, Kerala Forest Research Institute, Peechi, IN
Source
Indian Forester, Vol 142, No 1 (2016), Pagination: 1-7Abstract
Estimation of volume, biomass and carbon stocks support several applications from the commercial exploitation of timber to global carbon cycle. Especially in the latter context the estimation of tree biomass with sufficient accuracy is essential to determine annual changes of carbon stored in particular ecosystems. Under the aegis of UN - REDD programme an extensive database on tree allometry in South Asia (Bangladesh, Bhutan, India, Nepal, Maldives, Pakistan and Sri Lanka) was prepared by extensive and exhaustive literature collected from the region by institutional visits, bibliographic databases and FAO reports. An evaluation of this data on tree allometry in South Asia shows that there exists a total of 4456 equations on volume, biomass, BEF, carbon and other growth variables for 375 species belonging to 96 families and 275 genera. Proportionate allocation of allometric models for different species in the collected documents is not homogenous with commercially important ones capturing more percentage share of equations. Vague description of tree components and output terms reduces the quality of allometric equations developed in the region. Also the geographical distribution of these allometric equations is highly skewed and conscious efforts should be taken to unearth documents on allometry in the neglected life zones.Keywords
Tree Allometric Equation, Estimation of Volume, Biomass, Carbon Stock, South Asia.- Error Propagation in Forest Biomass Assessment
Abstract Views :247 |
PDF Views:0
Authors
Affiliations
1 Southern Regional Station, National Dairy Research Institute, Bangalore (Karnataka), IN
2 Kerala Forest Research Institute, Peechi, IN
3 Food and Agricultural Organization, Rome, IT
1 Southern Regional Station, National Dairy Research Institute, Bangalore (Karnataka), IN
2 Kerala Forest Research Institute, Peechi, IN
3 Food and Agricultural Organization, Rome, IT
Source
Indian Forester, Vol 142, No 1 (2016), Pagination: 62-67Abstract
Forest biomass is the basis for the estimation of carbon storage and emission due to forestry sector. Though the total forest biomass includes aboveground and belowground biomass, this paper deals with the issues related to aboveground biomass. The total aboveground biomass is estimated through a number of variables measured across various components of trees using non-destructive methods. The techniques employed range from simple measuring tape to regression models to satellite imageries. The total error in biomass estimates is the sum of errors in the variables propagated in a hierarchical fashion. The knowledge of prediction errors helps to know the quality of biomass and subsequently bio-energy and carbon estimates. In this paper, various sources of error in biomass estimation, error quantification and error propagation are discussed. The sources of error include tree measurements, sampling strategy, choice of an allometric model and satellite imageries.In South Asia, the standard errors of co-efficient of biomass equations and R2 are often depicted as indicators for the quality of volumeand biomass equations. The Studies on error propagation in biomass estimates are scarce. Monte Carlo analysis, Pseudo-meta-analysis and Bayesian model averaging have been investigated to address the issues of error propagation in biomass estimation. Among these Bayesian model averaging appears to be a promising technique.Keywords
Biomass, Error Propagation, Allometric Equation, Monte Carlo Analysis, Pseudo-Meta-Analysis and Bayesian Model.- Status of Forest Biomass and Carbon Stock Assessment in South Asia
Abstract Views :235 |
PDF Views:0
Authors
Affiliations
1 Southern Regional Station, National Dairy Research Institute, Bangalore, IN
2 Kerala Forest Research Institute, Peechi, Kerala, IN
3 Food and Agricultural Organization, Rome, IT
1 Southern Regional Station, National Dairy Research Institute, Bangalore, IN
2 Kerala Forest Research Institute, Peechi, Kerala, IN
3 Food and Agricultural Organization, Rome, IT
Source
Indian Forester, Vol 142, No 1 (2016), Pagination: 81-85Abstract
In this paper, the status of forest biomass and carbon stock assessment in South Asia has been reviewed based on the reports of Forest Resource Assessment (FRA) -2010 published by FAO. The quality of the data reported to FAO is medium to high. The countries heavily depend on growing stock volume from National Forest Inventory reports for biomass and carbon estimation. While the National Forest Inventory is being carried out in India at regular intervals, other countries are attempting or in the process of undertaking periodic forest inventory. Besides updating the existing database of volume and biomass allometric equations, the regional/country specific biomass and carbon conversion factors are required to improve the present biomass and carbon estimates.Keywords
Forest Biomass, Carbon Stock, South Asia.- TMIS:A Decision Support System for Monitoring and Forecasting Prices of Timber Logs
Abstract Views :265 |
PDF Views:0
Authors
Affiliations
1 Southern Regional Station,ICAR-National Dairy Research Institute, Bangalore, IN
2 Kerala Forest Research Institute, Peechi, IN
1 Southern Regional Station,ICAR-National Dairy Research Institute, Bangalore, IN
2 Kerala Forest Research Institute, Peechi, IN
Source
Indian Forester, Vol 142, No 4 (2016), Pagination: 346-354Abstract
The timber market in developing countries is unorganized and there is no proper system available to track the timber market trends. The aim of the study was to develop a computer based decision support system to monitor the timber prices. This system is called as Timber Market Intelligence System (TMIS). In order to monitor the prices of various timber species, timber price indices were constructed using three approaches viz., Laspeyre's, Pasche's and Fisher's Price Index and implemented in TMIS. The price forecast models were also incorporated. The TMIS was demonstrated using the timber price data of Timber Sales Depots of Kerala Forests and Wildlife Department for the period 2005 to 2009. The results indicated that one of the three price index approaches is sufficient to track the teak prices. A simple aggregate price index was found useful in the case of other species Artocarpus hirsutus, Grewia tiliifolia, Xylia xlocarpa, Terminelia paniculata, Terminelia bellirica, Terminelia tomentosa and Largerstroemia microcarpa. The maximum growth in teak prices was observed during the period 2006 to 2007 and only a marginal increase was observed from the year 2008 onwards. The annual growth rate in the prices of teak was lower as compared to other timber species.Keywords
Timber Market Intelligence, Timber Price Index, Timber Depots.- Evaluation of Line Transect Sampling Technique in Estimating Elephant Abundance in Forests Using Dung Survey
Abstract Views :728 |
PDF Views:0
Authors
Affiliations
1 Southern Regional Station, National Dairy Research Institute, Bangalore, IN
2 Kerala Forest Research Institute, Peechi, Thrissur District 680 653 Kerala, IN
1 Southern Regional Station, National Dairy Research Institute, Bangalore, IN
2 Kerala Forest Research Institute, Peechi, Thrissur District 680 653 Kerala, IN
Source
Indian Forester, Vol 142, No 10 (2016), Pagination: 959-964Abstract
Line transect sampling technique is widely applied for estimating the biological population in forests. Recently, this technique has been in use for the estimation of elephant abundance using dung survey. The method of transforming dung count into elephant density requires dung density, which is corrected by defecation and decay rate. In this paper, the performance of the line transect sampling technique (LTS) in dung surveys with particular reference to variation in the number of detections of dung piles caused by annual rainfall variability was evaluated. The data set for this purpose was from the estimation of elephant population in the State of Kerala during the years 2005, 2007 and 2010, covering about 9000 km2. The study showed that the presence of dung piles and its detection probability were dependent on the level of rainfall in the two months preceding the date of dung survey. However, the LTS could provide comparable dung density estimates under the highly varying number of dung piles present in the area due to differences in the annual rainfall pattern.Keywords
Distance Sampling, Detection Probability, Elephant Density, Rainfall Variability.References
- Anderson D.R. and Southwell C. (1995). Estimates of macropod density from line transect surveys relative to analyst expertise. J. Wild Manage, 59(4): 852-857.
- Barnes R.F.W., Asamoah-Boateng B., Majam J.N. and Agyei-Ohemeng J. (1997). Rainfall and the population dynamics of elephant dung-piles in the forests of southern Ghana. Afr. J. Eco., 35: 39-52.
- Barnes R.F.W. and Dunn A. (2002). Estimating forest elephant density in Sapo National Park (Liberia) with a rainfall model. Afr. J. Eco., 40: 159163.
- Borchers D., Margues T., Gunnlaugsson and Jupp P. (2010). Estimating distance sampling detection functions when distances are measured with errors. J. Agricultural, Biological and Environmental Statistics, 15: 346-361.
- Buckland S.T., Anderson D.R., Burnham K.P., Laake J.L., Borchers D.L. and Thomas L. (2001). Introduction to distance sampling: Estimating abundance of biological populations,Oxford University Press Inc, New York.
- Buckland S.T., Anderson D.R., Burnham K.P., Laake J.L., Borchers D.L. and Thomas L. (2004). Advanced Distance Sampling, Oxford University Press Inc, New York.
- Buckland S.T., Plumptre A.J., Thomas L. and Rexstad E.A. (2010a). Line Transect Sampling of primates: Can animal-to-observer distance methods work? Int. J. Primatol., 31: 485-499.
- Buckland S.T., Plumptre A.J., Thomas L. and Rexstad E.A. (2010b). Design and analysis of line transect surveys for Primates. Int. J. Primatol., 31: 833-847.
- Burnham K.P., Anderson D.R. and Laake J.L. (1980). Estimation of density from line transects sampling of biological populations. Wildlife Monographs, 72: 202.
- Forcard S., Isotti R. and Tinell A. (2002). Line transect estimates of ungulate populations in a Mediterranean Forest. J. Wild Manage, 66(1): 48-58.
- Guthery F.S. (1988). Line transect sampling of probable density of Rangeland: Evaluation and recommendations. Wildlife Society Bulletin, 16: 193-203.
- The Hindu (2012) The Hindu archives. http://www.thehindu.com/navigation/?type=static&page=archive, accessed on 30 April 2012.
- Mercey K.A. and Jayaraman K. (1999). Predicting the variation in detection function in line transect sampling through random parameter model. Environmental and Ecological Statistics, 6(4): 341-350.
- Mercey K.A. and Jayaraman K. (2002) Effect of errors in visual assessment of distance in line transect sampling. In: Proceedings of the 14th Kerala Science Congress 29-31 January 2002 (M.R.Das eds), Science and Technology Department, Government of Kerala, Thriuvananthapuram, 2002, pp. 455-458.
- Mercey K.A., and Jayaraman K. (2004). Detection function models for indirect evidence of elephant and gaur. Indian Forester, 130(10): 12011203.
- Sivaram M., Ramachandran K.K., Nair P.V. and Jayson E.A., Population estimation of Wild Elephants in the Elephant Reserves of Kerala State2005, Kerala Forest Research Institute, Peechi, KFRI Extension Report, 2005, No.18, p.46.
- Sivaram M., Ramachandran K.K., Nair P.V. and Jayson E.A. (2007). Population estimation of Wild Elephants in the Elephant Reserves of Kerala State-2007, Kerala Forest Research Institute, Peechi, KFRI Extension Report, No.27, p.40.
- Sivaram M., Ramachandran K.K., Jayson E.A. and Nair P.V. (2010). Wild Elephant Census of Kerala State-2010, Kerala Forest Research Institute, Peechi, KFRI Extension Report, 2011, No.52, p.40.
- Thomas L., Laake J.L., Strinberg S., Marques F.F.C., Buckland S.T., Borchers D.L., Anderson D.R., Burnham K.P., Hedley S.L., Pollard J.H., Bishop J.R. and Marques T. A. (2006). Distance 5.0, Release 2, Research Unit for Wildlife population Assessment, University of St. Andrews, U.K.
- Turnock B.J. and Quinn II T.J. (1991). The effect of responsive movement on abundance estimation using line transect sampling. Biometrics, 47: 701-715.
- Varman K.S., Ramakrishnan U. and Sukumar R. (1995). Direct and indirect methods of counting elephants: A comparison of results from Mudumalai Sanctuary. In: A week with Elephants: Proceedings of the International Seminar on Asian Elephants (J.C. Daniel, J. C. and S.H. Datye eds),Oxford University Press, New Delhi.
- White G.C., Bartmann R.M., Carpenter L.M. and Garrot R.A. (1989). Evaluation of aerial line transects for estimating deer densities. J. Wild Manage, 53(3): 625-635.
- Optimized Cooperative QOS Enhanced Distributed Multipath Routing Protocol
Abstract Views :209 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Engineering, Lebanese French University, IQ
2 Department of Information Technology, Emirates College for Management and Information Technology, AE
3 Department of Master of Computer Application, Jain University, IN
1 Department of Computer Engineering, Lebanese French University, IQ
2 Department of Information Technology, Emirates College for Management and Information Technology, AE
3 Department of Master of Computer Application, Jain University, IN
Source
ICTACT Journal on Communication Technology, Vol 10, No 3 (2019), Pagination: 2061-2065Abstract
The link failure is considered in this paper in order to guarantee reliable and continuous transmission of data. And to ensure that cooperative routing is done faster response and effective packet transmission. Cooperative communications are the most recent fields of research; they combine wireless channels’ link quality and broadcasting nature. So communication in ad hoc mobile networks only works properly if the participating nodes work together in routing and transmission. A flow is divided into batches of data packets. When they leave the source node every packet in the same batch has the same forwarder list. The underlying routing protocol used in this work is Proactive Source Routing (PSR), which provides each node with all other nodes on the network. The forwarder list therefore contains the identity of the path nodes from the source node to the location. Once packets progress through the network, forwarding nodes can amend the forwarder list when any changes in the network topology have been observed. In addition, some other nodes not listed as transmitting data may also be transmitted, which is called as small-scale retransmission.Keywords
Link-Quality, QOS, Proactive Source Routing, Multipath Routing Protocol.References
- Y. Chai, W. Shi, T. Shi and X. Yang, “An Efficient Cooperative Hybrid Routing Protocol for Hybrid Wireless Mesh Networks”, Wireless Networks, Vol. 23, No. 5, pp. 1387-1399, 2017.
- P. Vivekanandan and A. Sunitha Nadhini, “A Survey on Efficient Routing Protocol using Mobile Networks”, International Journal of Advances in Engineering and Technology, Vol. 6, No. 1, pp. 370-382, 2013.
- S. Paruchuri and S. Awate, “Organizational Knowledge Networks and Local Search: The Role of IntraOrganizational Inventor Networks”, Strategic Management Journal, Vol. 38, No. 3, pp. 657-675, 2017.
- V. Balasubramanian and A. Karmouch, “Managing the Mobile Ad-Hoc Cloud Ecosystem using software Defined Networking Principles”, Proceedings of International Symposium on Networks, Computers and Communications, pp. 1-6, 2017.
- T.X. Tran, A. Hajisami, P. Pandey and D. Pompili, “Collaborative Mobile Edge Computing in 5G Networks: New Paradigms, Scenarios, and Challenges”, IEEE Communications Magazine, Vol. 55, No. 4, pp. 54-61, 2017.
- B. Blanco, J.O. Fajardo, I. Giannoulakis, E. Kafetzakis, S. Peng, J. Perez Romero and E. Sfakianakis, “Technology Pillars in the Architecture of Future 5G Mobile Networks: NFV, MEC and SDN”, Computer Standards and Interfaces, Vol. 54, pp. 216-228, 2017.
- M.L. Duckers, A.B. Witteveen, J.I. Bisson and M. Olff, “The Association between Disaster Vulnerability and PostDisaster Psychosocial Service Delivery across Europe”, Administration and Policy in Mental Health and Mental Health Services Research, Vol. 44, No. 4, pp. 470-479, 2017.
- C. Arrighi, L. Rossi, E. Trasforini, R. Rudari, L. Ferraris, M. Brugioni and F. Castelli, “Quantification of Flood Risk Mitigation Benefits: A Building-Scale Damage Assessment through the RASOR Platform”, Journal of Environmental Management, Vol. 207, pp. 92-104, 2018.
- C. Zhou and L.Y. Ding, “Safety Barrier Warning System for Underground Construction Sites using Internet-of-Things Technologies”, Automation in Construction, Vol. 83, pp. 372-389, 2017.
- N. Kumar and R.A. Khan, “Emergency Information System Architecture for Disaster Management: Metro City Perspective”, International Journal of Advanced Research in Computer Science, Vol. 8, No. 5, pp. 560-564, 2017.
- E. Burke, M. Pinedo, K.G. Zografos, M.A. Madas and K.N. Androutsopoulos, “Increasing Airport Capacity Utilisation through Optimum Slot Scheduling: Review of Current Developments and Identification of Future Needs”, Journal of Scheduling, Vol. 20, No. 1, pp. 103-113, 2017.
- F. Al-Turjman, “Cognitive Routing Protocol for DisasterInspired Internet of Things”, Future Generation Computer Systems, Vol. 92, pp. 1103-1115, 2017.
- C. Funai, C. Tapparello and W. Heinzelman, “Enabling Multi-Hop Ad Hoc Networks through WiFi Direct MultiGroup Networking”, Proceedings of International Conference on Computing, Networking and Communications, pp. 491-497, 2017.
- D. Aggarwal, “A Study on the Role of Mobile Ad Hoc Networks (MANETS) In Disaster Management”, International Journal of Advanced Research in Computer Science, Vol. 8, No. 5, pp. 1963-1966, 2017.
- X. Xu, A. Chen and C. Yang, “An Optimization Approach for Deriving Upper and Lower Bounds of Transportation Network Vulnerability under Simultaneous Disruptions of Multiple Links”, Transportation Research Procedia, Vol. 23, No. 7, pp. 645-663, 2017.
- S. Rani, J. Malhotra and R. Talwar, “Energy Efficient Chain based Cooperative Routing Protocol for WSN”, Applied Soft Computing, Vol. 35, pp. 386-397, 2015.
- M. Maalej, S. Cherif and H. Besbes, “QoS and Energy Aware Cooperative Routing Protocol for Wildfire Monitoring Wireless Sensor Networks”, The Scientific World Journal, Vol. 2013, pp. 1-11, 2013.
- M. Chen, T. Kwon, S. Mao, Y. Yuan and V.C. Leung, “Reliable and Energy-Efficient Routing Protocol in Dense Wireless Sensor Networks”, International Journal of Sensor Networks, Vol. 4, No. 1-2, pp. 104-112, 2008.