An Algorithm for Non-Linear Network Programming: Implementation, Results and Comparisons

1992 ◽  
Vol 43 (10) ◽  
pp. 979
Author(s):  
N. Boland ◽  
C. J. Goh ◽  
A. I. Mees
Keyword(s):  
Network Programming ◽  
Linear Network ◽  
Non Linear

An orientation-independent UHF rectenna array with a unified matching and decoupling RF network

2019 ◽  
Vol 11 (5-6) ◽  
pp. 490-500 ◽  
Author(s):  
M. Fantuzzi ◽  
G. Paolini ◽  
M. Shanawani ◽  
A. Costanzo ◽  
D. Masotti
Keyword(s):  
Radio Frequency Identification ◽  
Indoor Localization ◽  
Future Generation ◽  
Linear Network ◽  
Close Proximity ◽  
Non Linear ◽  
Frequency Identification ◽  
Identification Tags ◽  
Energy Autonomy

AbstractThis work describes the design of a rectenna array exploiting orthogonal, closely-spaced UHF monopoles for orientation-independent RF energy harvesting to energize a passive tag, designed for UWB localization, with wake-up radio (WUR) capabilities. To reach this goal, different RF networks are studied to simultaneously realize RF decoupling of the antenna elements and matching of the radiating elements to the non-linear network of rectifiers. The design is performed for a wide power range of the RF incoming signals that need to be exploited for both energizing the passive tag and for providing energy autonomy to a WUR sub-system, used to minimize the long-term power consumption during tag standby operations. Two meandered cross-polarized monopoles, located in close proximity, and thus highly coupled, are adopted for orientation-insensitive operations. The combining RF network is reactive and includes an unbalanced power divider to draw a fraction of the harvested energy to a secondary way for WUR operations. The performance of the harvester is first optimized by EM/non-linear co-design of the whole system over an interval of low RF power levels. The system has been realized and experimentally validated: the superior results obtained, in terms of both dc voltage and power, with respect to a standard single-monopole rectenna, justify the deployment of the presented tag for the energy autonomy of future generation radio-frequency identification tags for indoor localization.

A Proportional Hazards Neural Network for Performing Reliability Estimates and Risk Prognostics for Mobile Systems Subject to Stochastic Covariates

2005 ◽  
Author(s):  
George M. Lloyd ◽  
Timothy Hasselman ◽  
Thomas Paez
Keyword(s):  
Survival Data ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Mobile Systems ◽  
Linear Network ◽  
Model Free ◽  
Hazards Model ◽  
Reliability Estimates ◽  
Non Linear ◽  
Stochastic Covariates

We present a proportional hazards model (PHM) that establishes a framework suitable for performing reliability estimates and risk prognostics on complex multi-component systems which are transferred at arbitrary times among a discrete set of non-stationary stochastic environments. Such a scenario is not at all uncommon for portable and mobile systems. It is assumed that survival data, possibly interval censored, is available at several “typical” environments. This collection of empirical survival data forms the foundation upon which the basic effects of selected covariates are incorporated via the proportional hazards model. Proportional hazards models are well known in medical statistics, and can provide a variety of data-driven risk models which effectively capture the effects of the covariates. The paper describes three modifications we have found most suitable for this class of systems: development of suitable survival estimators that function well under realistic censoring scenarios, our modifications to the PHM which accommodate time-varying stochastic covariates, and implementation of said model in a non-linear network context which is itself model-free. Our baseline hazard is a parameterized reliability model developed from the empirical reliability estimates. Development of the risk score for arbitrary covariates arising from movement among different random environments is through interaction of the non-linear network and training data obtained from a Markov chain simulation based on stochastic environmental responses generated from Karhunen-Loe`ve models.

A cellular non-linear network for image fusion based on data regularization

2006 ◽  
Vol 34 (5) ◽  
pp. 533-546 ◽  
Author(s):  
Andrea Anzalone ◽  
Federico Bizzarri ◽  
Marco Storace ◽  
Mauro Parodi
Keyword(s):  
Image Fusion ◽  
Linear Network ◽  
Non Linear ◽  
Data Regularization

Experimental validation of non linear network models

1996 ◽  
pp. 141-198 ◽  
Author(s):  
Christian Carrot ◽  
Jacques Guillet ◽  
Pascale Revenu ◽  
Alain Arsac
Keyword(s):  
Experimental Validation ◽  
Network Models ◽  
Linear Network ◽  
Non Linear
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