Statistics making the optimum estimate linear

1972 ◽  
Vol 60 (4) ◽  
pp. 449-449
Author(s):  
L.P. Bolgiano
Keyword(s):  
Optimum Estimate

Optimum probabilistic processing in colour perception. II. Colour vision as template matching

1979 ◽  
Vol 205 (1159) ◽  
pp. 249-266 ◽  
Keyword(s):  
Template Matching ◽  
Colour Vision ◽  
Statistical Approach ◽  
Line Element ◽  
Colour Space ◽  
Spectral Parameters ◽  
Colour Perception ◽  
Wavelength Filter ◽  
Basic Constraint ◽  
Optimum Estimate

A statistical approach to account for psychophysical phenomena in human colour vision is presented. The central visual processor is viewed as an optimum recognizer of stochastic patterns supplied by the periphery. The processor makes an optimum estimate of the spectral parameters of the stimulus, given the wavelength filter characteristics of the periphery, the stochastic nature of the information and an internal template to which the external stimulus is matched. The estimate is constrained in ways inferred from empirical phenomena. Subjective brightness of mono­chromatic stimuli and related constant brightness manifolds in the colour space constitute the constraint for brightness estimation. Results analogous and in accord with those of earlier line element theories are obtained. The Bezold-Brücke hue shift constitutes the basic constraint for hue estimation. The hue estimate involves interrelation between the fields in the experiment. Similarities and differences both in basic conceptions and results introduced by the template matching notions are discussed.

scholarly journals Optimal Measuring and Energy Planning of Smart Grid Considering the Battery Lifetime by Using FFA

2021 ◽  
Vol 12 (6) ◽  
pp. 2324-2337
Author(s):  
Shaik Karimulla, K. Ravi
Keyword(s):  
Smart Grid ◽  
Power Supply ◽  
Economic Planning ◽  
Energy Planning ◽  
Operational Cost ◽  
Battery Lifetime ◽  
Procedure Cost ◽  
Energy Assets ◽  
Optimum Estimate ◽  
The Ideal

The ceaselessly developing request for power in this world privileges a productive and dependable framework of energy sources. Disseminated energy assets such as wind-based energy and solar-based energy can remain shared inside a smart grid to source energy to the utilizers in a feasible way. In arranging to safeguard the extra dependable and conservative energy source, the battery-operated capacity system is coordinated inside the smart grid. In this objective, the operational cost of smart grid is decreased by financial planning in view of the ideal estimate of the battery-operated system and the period of battery procedure. Hence, the actual period battery procedure cost is modelled in view of the perceptiveness of release individually in the time interim. In addition, the anticipated economic planning with battery-operated measuring is improved by using the Fire-Fly algorithm (FFA). The adequacy of FFA is associated through other metaheuristic strategies in relations of execution estimation files, which remain price of power also misfortune of power supply possibility. The proposed method results show that this method diminishes the price of the grid and also achieves the optimum estimate of the battery-operating to the smart grid.

SENSOR‐ARRAY PROCESSING WITH CHANNEL‐RECURSIVE BAYES TECHNIQUES

Geophysics ◽  
1971 ◽  
Vol 36 (5) ◽  
pp. 822-834 ◽  
Author(s):  
Edward J. Farrell
Keyword(s):  
Covariance Matrix ◽  
Sensor Array ◽  
Bayes Estimation ◽  
Transient Signal ◽  
Unknown Parameters ◽  
Sensor Array Processing ◽  
Recursive Evaluation ◽  
Frequency Domain Approach ◽  
Class Of Loss Functions ◽  
Optimum Estimate

Arrays of seismometers, hydrophones, and electromagnetic receivers have several signal processing problems in common. This paper is concerned primarily with source location and secondarily with signal extraction. The basic problem can be described as follows: A transient signal from an event is detected in the outputs of the sensor array. We determine the location of the source from the temporal positions of the signal in the array outputs. Further, if the signal is unknown, we estimate it. The approach taken here differs from previous investigations in three ways: (i) a Bayes estimation approach is used, (ii) the estimates are evaluated recursively with respect to channels, and (iii) a time‐domain approach is used, as opposed to a frequency‐domain approach. The proposed estimation technique is optimum with respect to a large class of loss functions, since it is based on the expectation of the posterior distribution. Recursive evaluation of the posterior expectation has several advantages. At each step we have the optimum estimate of the unknown parameters and the corresponding covariance matrix. A channel selection rule and stopping rule are defined in terms of the covariance matrix. Further, having an optimum estimate at each step permits simplification of the processing; e.g., the search interval may be limited to the most probable region of the parameter space. Such techniques significantly decrease the processing time and increase the rate of convergence. Equations are developed for the known‐signal case with planar and spherical wavefronts, and results are presented from a computer simulation. Subsequently, equations for the unknown‐signal case are presented with simulation results.

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