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19 results on '"Surekha, Sala"'

2. Adaptive Exon Prediction Using Maximum Error Normalized Algorithms

Author

Zıa Ur Rahman, Md., Vardhan, Baligodugula Vishnu, Jenith, Lakkakula, Rakesh Reddy, Veeramreddy, Surekha, Sala, Srinivasareddy, Putluri, Bansal, Jagdish Chand, Series Editor, Deep, Kusum, Series Editor, Nagar, Atulya K., Series Editor, Mathur, Garima, editor, Bundele, Mahesh, editor, Lalwani, Mahendra, editor, and Paprzycki, Marcin, editor

Published
2022
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3. Cloud Based Exon Prediction Using Maximum Error Normalized Logarithmic Algorithms

Author

Rahman, Md. Zıa Ur, Haneesh, Annabathuni Chandra, Reddy, Bhimireddy Shanmukha Sai, Surekha, Sala, Srinivasareddy, Putluri, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Singh, Mayank, editor, Tyagi, Vipin, editor, Gupta, P. K., editor, Flusser, Jan, editor, Ören, Tuncer, editor, and Sonawane, V. R., editor

Published
2021
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8. Adaptive beamforming for cognitive radio based wireless communication systems.

Author

Surekha, Sala, Mantravadi, Nagesh, Mirza, Shafi Shahsavar, Lakshmi, M. L. M., and Rahman, Md. Zta Ur

Subjects
*COGNITIVE radio, *WIRELESS communications, *MEAN square algorithms, *BEAMFORMING, *SIGNAL-to-noise ratio, *SIGNAL processing, *COMPUTATIONAL complexity
Abstract

In wireless communications, cognitive radios are mostly used technique, in this estimation of number of occupied channels is a problem in any signal processing, so direction of arrival is considered. In adaptive beamforming, least mean square algorithm is mostly used, but performance of algorithm decreases significantly due to fixed step size, so that it doesn't suit for signal to noise ratio variations. Variable step size based adaptive algorithm is proposed to improve performance. But this variable step size algorithm is computationally complex due to this reason its not preferred in practical applications. Therefore, in this paper robust variable step size adaptive algorithm is proposed for beamforming. By using sigmoid function, variable step size is obtained, using instantaneous mean error, sigmoid function is calculated then normalization done by squared sum of estimated signal power and instantaneous error. By this proposed algorithm, without tuning any parameter step size is updated adaptively, it performs well with low computational complexity, simulation results show better performance with proposed algorithm [ABSTRACT FROM AUTHOR]

Published
2024
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9. Artifact removal in thoracic electrical bioimpedance signals using robust diffusion least power algorithm.

Author

Mirza, Shafi Shahsavar, Mantravadi, Nagesh, Lakshmi, M. L. M., Surekha, Sala, and Rahman, Md. Zia Ur

Subjects
SIGNAL-to-noise ratio, ALGORITHMS, RESPIRATORY muscles, STROKE, ERROR rates
Abstract

By using thoracic electrical bioimpedance (TEB) analysis stroke volume calculated for sudden cardiac arrests. In this paper proposed robust normalized diffusion least power algorithm (RNDLPA) is proposed to get high resolution components. In clinical scenarios, TEB components are contaminated with various artifact components, which interrupts the identification of several features in stroke intensity. The number of multiplications is also one of the key elements for monitoring in health care applications. Hence by proposed RNDLPA algorithm it reduces computational complexity, stability is improved and also steady state error rate is reduced. To eliminate noise components various adaptive cancelers are presented and they are compared with proposed algorithm. Further to show performance improvement signal to noise ratio (SNR) improvement is taken into consideration it gets better values 14.3594 dB and 12.3551 dB for respiratory noise and muscle noise respectively when compared to existed techniques. [ABSTRACT FROM AUTHOR]

Published
2024
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19. Adaptive Learning Based Beamforming for Spectrum Sensing in Wireless Communications.

Author

Mirza, Shafi Shahsavar, Mantravadi, Nagesh, Surekha, Sala, and Ur Rahman, Md Zia

Subjects
BEAMFORMING, WIRELESS communications, RADIO technology, COGNITIVE radio, LEARNING ability, INFORMATION technology security
Abstract

In wireless communications, cognitive radio systems are widely used. But in real time applications several interferences occur due to multiple input multiple output systems. The main reason for interference is imperfect spectrum sensing for channel allocation to secondary users. Secondary users try to access primary channels and switch to another frequency when primary user needs access. During this phenomenon interference come in to the picture between the two users. In such a scenario, beamforming provides information security and avoids interference. In this work, a new variable step size based adaptive learning is considered for beamforming. Variable step size scheme is used for distributed estimation in adaptive networks using diffusion concept. This learning scheme iteratively update the step size and reduces the power of error. The performance of proposed diffusion variable step size LMS (DVSLMS) algorithm is studied in multipath environment. The convergence analysis is carried to prove the ability of adaptive learning methods. The experimental results are presented in terms of convergence rate and number of active taps at threshold points 0.1, 0.5 and 1.0. [ABSTRACT FROM AUTHOR]

Published
2021

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