Design and Analysis of Air Quality Monitoring System
| Autor: | J. Suganthi, V. Vinitha, S. Sabeenamarry, P. Sathya, R. Guruprasath |
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| Rok vydání: | 2021 |
| Předmět: |
Air quality monitoring
Computer Science::Sound Computer science 0502 economics and business 05 social sciences 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 02 engineering and technology Hardware_ARITHMETICANDLOGICSTRUCTURES 050203 business & management Automotive engineering |
| Zdroj: | International Journal of Advanced Research in Science, Communication and Technology. :100-105 |
| ISSN: | 2581-9429 |
| DOI: | 10.48175/ijarsct-1216 |
| Popis: | In the adaptive noise cancellation (ANC) challenge, a novel least-mean-square (LMS) algorithm for filtering speech sounds has been created. It is focused on minimising the difference weight vector's squared Euclidean norm under a stability restriction specified over the a posteriori estimation error. The Lagrangian methodology was employed for this reason in order to propose a nonlinear adaptation rule described in terms of the product of differential inputs and errors, which is a generalisation of the normalised (N)LMS algorithm. The proposed approach improves monitoring ability in this sense, as shown by studies using the AURORA 2 and 3 speech databases. They include a thorough output assessment as well as a thorough comparison to regular LMS algorithms with nearly the same computational load, such as the NLMS and other recently published LMS algorithms including the updated (M)-NLMS, the error nonlinearity (EN)-LMS, or the normalised data nonlinearity (NDN)-LMS adaptation. |
| Databáze: | OpenAIRE |
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