Efficient adaptive noise cancellation techniques in an IOT  Enabled Telecardiology System

Sulthana, A.K. Thasleem; Rahman, Zia Ur

doi:10.14419/ijet.v7i2.17.11562

Cited by 13 publications

(13 citation statements)

References 4 publications

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“…LMS-based AEP is implemented for comparison analysis. From NCBI database [19], Ten genomic datasets are considered for performance comparisons. The performance of the implemented models is measured through by taking the parameters such as Precision (Pr), Sensitivity (Sn), also Specificity (Sp).…”

Section: Resultsmentioning

confidence: 99%

Cloud Based Exon Prediction Methodology using Logarithmic Adaptive Algorithms for Genomic Signal Analysis

2020

IJETER

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Cloud Computing provides healthcare companies with the major convenience in the research and economic aspects. Cloud services ensure that large quantities of such sensitive data will be stored and managed securely. The gene sequence labs send inferred data to various sequence libraries under traditional flow of gene information through the Internet. Cloud service use will reduce DNA sequencing storage costs to a minimum. In this work, we developed a novel genomic bioinformatics-based system, using Amazon Cloud Services, that stores and processes genomic sequence information. An important task in bio-informatics that is used for the recognition and implementation of disease drug, is the exact recognition of exon regions in deoxyribonucleic acid (DNA) sequence. All exon identification techniques are based on three basic periodicity (TBP) properties of exons. With the comparison of various existing methods, adaptive signal processing techniques have been promising. This paper uses the maximum normalized logarithmic mean least square (MNLMLS) algorithm also its signed variants to develop multiple adaptive exon predictor (AEPs) with less computational complexity. Eventually, a performance evaluation is performed for different AEPs using various standard gene data sequences derived from National Biotechnology Information Centre (NBI) genomic sequence database, such as Sensitivity (Sn), Specificity (Sp) and Precision (Pr) measurements.

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Section: Resultsmentioning

confidence: 99%

Cloud Based Exon Prediction Methodology using Logarithmic Adaptive Algorithms for Genomic Signal Analysis

2020

IJETER

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“…Then by solving unbiasedness criterion from [31], then vector is obtained as (10) By substituting equation (10) in equation ( 7), we get the Bias Compensated NLMS algorithm as (11) For noisy inputs off unknown system, Bias Compensated NLMS algorithm (BC NLMS) provides unbiased estimates. Input variance is estimated if we want to use in BC NLMS algorithm, because it is not practically available.…”

Section: Normalized Lms Algorithm and Bias Compensated Nlms Algorithmmentioning

confidence: 99%

Artifact Elimination in Thoracic electrical bioimpedance signals using new Normalized Adaptive Filters

2020

IJETER

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Thoracic electrical bioimpedance (TEB) used for facilitating stroke volume from sudden cardiac arrest signals. It is a non -invasive method used monitoring cardiac outputs, measuring stroke volumes and to observe changes in hemodynamic parameters of volume of blood. While measuring volume of blood, TEB signal is contaminated with physiological and non-physiological signal artifacts. For avoiding these artifacts in this paper proposed an adaptive filter method for enhancing TEB Signals. Least Mean Square (LMS) algorithm is a basic adaptive method, but it is non stationary in nature and it has low convergence rate problems. Hence, Bias compensated Normalized Least Mean Square (BC NLMS) algorithm is proposed, then it check initially for stability in terms of mean deviation analysis and mean square deviation analysis. Depending in this analysis, noisy input variance estimation and variable step size are taken into consideration for better performance in terms of reduced steady error rate, good stability and convergence improvement. In this paper we present various adaptive noise cancellers (ANCs) for the elimination of artifacts from TEB signals. Also, in simulation results, artifacts are eliminated from noisy input signal and it performs well when compared to exiting methods.

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“…Unsupervised speech enhancement low power spectral densities are considered in Ming et al [20]. Various adaptive learning algorithms are presented in [21][22][23][24][25]. Adaptive low rank matrix decomposition is often used for signal enhancement and enhanced efficiency in terms of speech quality.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Reference Free Adaptive Learning Process for Speech Enhancement Applications

Jyoshna¹,

Rahman²,

KoteswaraRao³

2022

Computers, Materials &Amp; Continua

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In issues like hearing impairment, speech therapy and hearing aids play a major role in reducing the impairment. Removal of noise signals from speech signals is a key task in hearing aids as well as in speech therapy. During the transmission of speech signals, several noise components contaminate the actual speech components. This paper addresses a new adaptive speech enhancement (ASE) method based on a modified version of singular spectrum analysis (MSSA). The MSSA generates a reference signal for ASE and makes the ASE is free from feeding reference component. The MSSA adopts three key steps for generating the reference from the contaminated speech only. These are decomposition, grouping and reconstruction. The generated reference is taken as a reference for variable size adaptive learning algorithms. In this work two categories of adaptive learning algorithms are used. They are step variable adaptive learning (SVAL) algorithm and time variable step size adaptive learning (TVAL). Further, sign regressor function is applied to adaptive learning algorithms to reduce the computational complexity of the proposed adaptive learning algorithms. The performance measures of the proposed schemes are calculated in terms of signal to noise ratio improvement (SNRI), excess mean square error (EMSE) and misadjustment (MSD). For cockpit noise these measures are found to be 29.2850, -27.6060 and 0.0758 dB respectively during the experiments using SVAL algorithm. By considering the reduced number of multiplications the sign regressor version of SVAL based ASE method is found to better then the counter parts.

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Efficient adaptive noise cancellation techniques in an IOT Enabled Telecardiology System

Cited by 13 publications

References 4 publications

Cloud Based Exon Prediction Methodology using Logarithmic Adaptive Algorithms for Genomic Signal Analysis

Cloud Based Exon Prediction Methodology using Logarithmic Adaptive Algorithms for Genomic Signal Analysis

Artifact Elimination in Thoracic electrical bioimpedance signals using new Normalized Adaptive Filters

An Efficient Reference Free Adaptive Learning Process for Speech Enhancement Applications

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