Product quality level monitoring and control for logic chips and modules

1983 ◽  
Vol 23 (6) ◽  
pp. 1170
Keyword(s):  
Product Quality ◽  
Quality Level ◽  
Monitoring And Control ◽  
And Control ◽  
Level Monitoring

scholarly journals Remote Level Monitoring and Control Solution

2016 ◽  
Vol 49 (6) ◽  
pp. 194-197 ◽  
Author(s):  
Maria de Fátima Chouzal ◽  
Maria Teresa Restivo ◽  
Fernando Gomes Silva ◽  
Fernando Gomes Almeida ◽  
Tiago Faustino Andrade
Keyword(s):  
Monitoring And Control ◽  
Control Solution ◽  
And Control ◽  
Level Monitoring

scholarly journals Silicon dioxide based Nano sensor to measure glucose level in blood

2018 ◽  
Vol 7 (4) ◽  
pp. 2862-2866
Author(s):  
Kavitha Ganesan ◽  
K.Senthil Kumar
Keyword(s):  
Silicon Dioxide ◽  
Glucose Level ◽  
Mass Production ◽  
Response Probability ◽  
Monitoring And Control ◽  
Comparison Results ◽  
Diabetic Person ◽  
The Cost ◽  
And Control ◽  
Level Monitoring

Diabetes is a growing problem affecting many people in the world. Diabetes leads to various complications like lower limb amputations, blindness, cardiovascular disease etc. Diabetes has many complexity that is reduced by individual monitoring and control of glucose level. Glucose level monitoring occur with recent development in Nanotechnology. Nanotechnology develops nanosensors which measure glucose level. Nanosensors fabricated by silicon dioxide in mass production reduces the cost of the nanosensors. Nanosensors made of silicon dioxide read the blood samples of a person without diabetes and a diabetic person with the help of SIGVIEW and analyzed using MATLAB. Statistical analysis is made with comparison of spectrogram, magnitude response, probability distribution, time domain and the autocorrelation of normal blood signals and diabetic signals. Comparison results in separation of a normal and diabetic person with an excellent performance of nanosensors in mass production.

RKH Space Methods for Low Level Monitoring and Control of Nonlinear Systems II. A Vector-Case Example: The Lorenz System

1997 ◽  
Vol 07 (06) ◽  
pp. 823-845 ◽  
Author(s):  
Alan Cover ◽  
James Reneke ◽  
Suzanne Lenhart ◽  
Vladimir Protopopescu
Keyword(s):  
Nonlinear Systems ◽  
Reproducing Kernel ◽  
Lorenz System ◽  
Linearization Method ◽  
Monitoring And Control ◽  
Stochastic Linearization ◽  
Vector Case ◽  
The Lorenz System ◽  
And Control ◽  
Level Monitoring

By using techniques from the theory of reproducing kernel Hilbert (RKH) spaces, we continue the exploration of the stochastic linearization method for possibly unknown and/or noise corrupted nonlinear systems. The aim of this paper is twofold: (a) the stochastic linearization formalism is explicitly extended to the vector case; and (b) as an illustration, the performance of the stochastic linearization for monitoring and control is assessed in the case of the Lorenz system for which the dynamic behavior is known independently.

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