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San Diego, CA, United States

Singh R.P.,Indian Veterinary Research Institute | Bandyopadhyay S.K.,Kab Laboratories Inc.
VirusDisease | Year: 2015

Peste des petits ruminants, a viral disease of small ruminants, the control of which is important for poverty alleviation and to ensure livelihood security in Asia, Middle East and Africa. In recognition of these issues, we developed and applied vaccine and diagnostics to demonstrate effective control of PPR during preceding 6 years in a sub-population of small ruminants in India. Two south Indian states, namely Andhra Pradesh and Karnataka, strongly indicated possibility of PPR control with more than 90 % reduction in number of reported outbreaks of PPR, mostly through mass vaccination. Similarly, the situation at the national level also demonstrated a decline of more than 75 % in the number of reported outbreaks. Sharing these experiences may motivate other countries for similar initiatives leading to progressive control of PPR, which is in line with the initiatives of the organizations like FAO/OIE and the recent platforms on global PPR research alliance. © 2015, Indian Virological Society. Source


Mandal D.,Central Soil and Water Conservation Research and Training Institute | Sharda V.N.,Central Soil and Water Conservation Research and Training Institute | Sharda V.N.,Kab Laboratories Inc.
Journal of the Indian Society of Soil Science | Year: 2011

An integrated bio-physical approach, taking into account a productivity index (PI) and erosion risk index (ERI), was deployed for the quantitative evaluation of land in Doon valley of India. The results indicated that the productivity index varied between 0.19 and 0.77 i.e. medium to very high inherent productivity. The productivity index (PI) was mainly affected by changes in available water content followed by soil depth, bulk density and pH. The erosion risk (ERI) was strongly affected by slope gradient and rainfall aggressiveness. In general, high and very high erosion risk was found (ERI varied between 0.39 to 0.85). A comprehensive erosion hazard management strategy was suggested for efficient management of present and future erosion debacle in the area. Source


Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase II | Award Amount: 750.00K | Year: 2003

The Automatic Feature Evaluator (AFE) Phase II program will develop a demonstration capability to show how data with different, missing, and corrupted attributes can be assembled into a decision-making process. The unit will address three areas ofconcern: clustering, the formation of some initial groupings (clusters) of measurements, each representing an object; classification, the subsequent evolution of the set of clusters as new reports come in and are assigned to clusters; and maintenance, theroutine and non-routine analysis of the cluster space to detect and correct problems. Although the algorithms are in general statistical in nature, they do not assume any particular distribution of the elements reported. The algorithms are able to dealwith measurements that are non-ideal in other ways also. They can handle elements that are discrete and even non-numeric. They can deal with reports that contain missing data, outliers, or gross errors. They can also handle multi-modal distributions andare able to track changes in the underlying distributions over time. Some of these issues are addressed on the basis of knowledge of the reports and their content, but most of the issues are addressed in general terms. This technology solves the problem ofusing very different data inputs to derive grouping and classification solutions


Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase I | Award Amount: 0.00 | Year: 2003

The Automatic Feature Evaluator (AFE) Phase II program will develop a demonstration capability to show how data with different, missing, and corrupted attributes can be assembled into a decision-making process. The unit will address three areas ofconcern: clustering, the formation of some initial groupings (clusters) of measurements, each representing an object; classification, the subsequent evolution of the set of clusters as new reports come in and are assigned to clusters; and maintenance, theroutine and non-routine analysis of the cluster space to detect and correct problems. Although the algorithms are in general statistical in nature, they do not assume any particular distribution of the elements reported. The algorithms are able to dealwith measurements that are non-ideal in other ways also. They can handle elements that are discrete and even non-numeric. They can deal with reports that contain missing data, outliers, or gross errors. They can also handle multi-modal distributions andare able to track changes in the underlying distributions over time. Some of these issues are addressed on the basis of knowledge of the reports and their content, but most of the issues are addressed in general terms. This technology solves the problem ofusing very different data inputs to derive grouping and classification solutions


Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase II | Award Amount: 494.67K | Year: 2011

Under SBIR Topic N101-100 (Multi-Source Imagery and Geopositional Exploitation [MSIGE]), three Phase I performers developed capability concepts to address different aspects of the MSIGE problem set. In Phase 2, we propose to develop a prototype DCGS-N capability for Multi-INT ISR and Targeting Services (MITS) by developing three subsystems and integrating them under separate Phase II contracts. This approach will increase value to the DCGS-N PoR by providing a low-risk, rapidly transitionable, end-to-end capability. Three proposed subsystems: - STRIKE LINE (Ticom Geomatics) -- Sensor Cueing, Data Publish and Subscribe, Wide Area Network (WAN) Distributor -- MITS System Engineering and Integration Lead - VISION (KAB Labs) -- Presentation Layer, Local Area Network (LAN) Distributor, Video Processing Framework, Video/Multi-INT Indexing/Search - AFOS (Mosaic ATM) -- Geolocalization, FMV Metadata Decoder, Metadata Accuracy Enhancement, Feature Projection into Full Motion Video (FMV) MITS will provide the following high level capabilities for DCGS-N: - Cue imagery sensors with geopositional data to collect FMV on targets of interest - Combine FMV with other target data, provide an integrated display - Improve geopositional accuracy of objects in analyst-selected FMV - Index video repositories for rapid searching, near real-time, and post mission analysis - Distribute enhanced multi-INT data products

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