Ramesh Singh resumes & CV records
Resumes
Ramesh Singh - Uttar Pradesh, US
Education:
B-Tech - Uttar Pradesh, IN 2005 to 2008
Class XII
Class XII
Ramesh P Singh, 7113425 S Hearst Ct, Tustin, CA 92782
Ramesh Singh Phones & Addresses
13425 Hearst Ct, Tustin, CA 92782 714-3899477
Irvine, CA
9335 Lee St, Fairfax, VA 22031 703-6911776
9299 Tower Side Dr, Fairfax, VA 22031 703-8654902
Vienna, VA
Orange, CA
Education
School / High School:
B-Tech- Uttar Pradesh, IN 2005
Specialities:
Class XII
Mentions for Ramesh P Singh
Publications & IP owners
Us Patents
Crop Yield Prediction Using Piecewise Linear Regression With A Break Point And Weather And Agricultural Parameters
US Patent:
7702597, Apr 20, 2010
Filed:
Apr 19, 2005
Appl. No.:
11/108674
Inventors:
Ramesh P. Singh - Fairfax VA, US
Anup Krishna Prasad - Kanpur, IN
Vinod Tare - Kanpur, IN
Menas Kafatos - Fairfax Station VA, US
Anup Krishna Prasad - Kanpur, IN
Vinod Tare - Kanpur, IN
Menas Kafatos - Fairfax Station VA, US
Assignee:
George Mason Intellectual Properties, Inc. - Fairfax VA
International Classification:
G06F 15/18
G06E 1/00
G06G 7/00
G06E 1/00
G06G 7/00
US Classification:
706 21
Abstract:
Crop yield may be assessed and predicted using a piecewise linear regression method with break point and various weather and agricultural parameters, such as NDVI, surface parameters (soil moisture and surface temperature) and rainfall data. These parameters may help aid in estimating and predicting crop conditions. The overall crop production environment can include inherent sources of heterogeneity and their nonlinear behavior. A non-linear multivariate optimization method may be used to derive an empirical crop yield prediction equation. Quasi-Newton method may be used in optimization for minimizing inconsistencies and errors in yield prediction. Minimization of least square loss function through iterative convergence of pre-defined empirical equation can be based on piecewise linear regression method with break point. This non-linear method can achieve acceptable lower residual values with predicted values very close to the observed values. The present invention can be modified and tailored for different crops worldwide.
Wavelet Maxima Curves Of Surface Latent Heat Flux
US Patent:
7890266, Feb 15, 2011
Filed:
Oct 6, 2009
Appl. No.:
12/574044
Inventors:
Guido Cervone - Arlington VA, US
Menas Kafatos - Fairfax Station VA, US
Domenico Napoletani - Fairfax VA, US
Ramesh P. Singh - Fairfax VA, US
Menas Kafatos - Fairfax Station VA, US
Domenico Napoletani - Fairfax VA, US
Ramesh P. Singh - Fairfax VA, US
Assignee:
George Mason Intellectual Properties, Inc. - Fairfax VA
International Classification:
G01V 1/28
US Classification:
702 15, 324300
Abstract:
The present invention introduces an innovative data mining technique to identify precursory signals associated with earthquakes. It involves a multistrategy approach that employs one-dimensional wavelet transformations to identify singularities in data, and analyzes the continuity of wavelet maxima in time and space to determine the singularities that could be precursory signals. Surface Latent Heat Flux (SLHF) data may be used. A single prominent SLHF anomaly may be found to be associated some days prior to a main earthquake event.
Wavelet Maxima Curves Of Surface Latent Heat Flux
US Patent:
2005022, Oct 20, 2005
Filed:
Apr 18, 2005
Appl. No.:
11/108115
Inventors:
Guido Cervone - Arlington VA, US
Menas Kafatos - Fairfax Station VA, US
Domenico Napoletani - Fairfax VA, US
Ramesh Singh - Fairfax VA, US
Menas Kafatos - Fairfax Station VA, US
Domenico Napoletani - Fairfax VA, US
Ramesh Singh - Fairfax VA, US
International Classification:
E04B001/98
E04H009/02
E04H009/02
US Classification:
052167100
Abstract:
The present invention introduces an innovative data mining technique to identify precursory signals associated with earthquakes. It involves a multistrategy approach that employs one-dimensional wavelet transformations to identify singularities in data, and analyzes the continuity of wavelet maxima in time and space to determine the singularities that could be precursory signals. Surface Latent Heat Flux (SLHF) data may be used. A single prominent SLHF anomaly may be found to be associated some days prior to a main earthquake event.
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