This paper proposes different approaches of wavelet based image denoising methods. The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. Wavelet algorithms are useful tool for signal processing such as image compression and denoising. Multi wavelets can be considered as an extension of scalar wavelets. The main aim is to modify the wavelet coefficients in the new basis, the noise can be removed from the data. In this paper, we extend the existing technique and providing a comprehensive evaluation of the proposed method. Results based on different noise, such as Gaussian, Poisson's, Salt and Pepper, and Speckle performed in this paper. A signal to noise ratio as a measure of the quality of denoising was preferred.

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Wavelet Based Image Denoising Technique

Background: During last decade the use of ECG recordings in biometric recognition studies has increased. ECG characteristics made it suitable for subject identification: it is unique, present in all living individuals, and hard to forge. However, in spite of the great number of approaches found in literature, no agreement exists on the most appropriate methodology. This study aimed at providing a survey of the techniques used so far in ECG-based human identification. Specifically, a pattern recognition perspective is here proposed providing a unifying framework to appreciate previous studies and, hopefully, guide future research. Methods: We searched for papers on the subject from the earliest available date using relevant electronic databases (Medline, IEEEXplore, Scopus, and Web of Knowledge). The following terms were used in different combinations: electrocardiogram, ECG, human identification, biometric, authentication and individual variability. The electronic sources were last searched on 1st March 2015. In our selection we included published research on peer-reviewed journals, books chapters and conferences proceedings. The search was performed for English language documents. Results: 100 pertinent papers were found. Number of subjects involved in the journal studies ranges from 10 to 502, age from 16 to 86, male and female subjects are generally present. Number of analysed leads varies as well as the recording conditions. Identification performance differs widely as well as verification rate. Many studies refer to publicly available databases (Physionet ECG databases repository) while others rely on proprietary recordings making difficult them to compare. As a measure of overall accuracy we computed a weighted average of the identification rate and equal error rate in authentication scenarios. Identification rate resulted equal to 94.95 % while the equal error rate equal to 0.92 %. Conclusions: Biometric recognition is a mature field of research. Nevertheless, the use of physiological signals features, such as the ECG traits, needs further improvements. ECG features have the potential to be used in daily activities such as access control and patient handling as well as in wearable electronics applications. However, some barriers still limit its growth. Further analysis should be addressed on the use of single lead recordings and the study of features which are not dependent on the recording sites (e.g. fingers, hand palms). Moreover, it is expected that new techniques will be developed using fiducials and non-fiducial based features in order to catch the best of both approaches. ECG recognition in pathological subjects is also worth of additional investigations.

/pdf/individual-identification-via-electrocardiogram-analysis-1hm3xvuapg.pdf

Individual identification via electrocardiogram analysis

The electrocardiogram (ECG ) signal is prone to various high and low frequency noises, including baseline wandering and power-line interference, which become the source of errors in QRS and in other extracted features. This paper presents a new ECG signal-processing approach based on empirical mode decomposition (EMD) and an improved approximate envelope method. To reduce the number of the initial intrinsic mode functions (IMFs), a Butterworth lowpass filter is used to eliminate high frequency noises before the EMD. To correct baseline wandering and to eliminate low frequency noises, the two last-order IMFs are abandoned. An improved approximate envelope is proposed and applied after the Hilbert transform to enhance the energy of QRS complexes and to suppress unwanted P/T waves and noises. Then, an algorithm based on the slope threshold is used for R-peak detection. The proposed denoising and R-peak detection algorithm are validated using the MIT-BIH Arrhythmia Database. The simulation results show that the proposed method can effectively eliminate the Gaussian noise, baseline wander, and power-line interference added to the ECG signal. The method can also function reliably even under poor signal quality and with long P and T peaks. The QRS detector has an average sensitivity of Se=99.94 % and a positive predictivity of +P=99.87 % over the first lead of the MIT-BIH Arrhythmia Database.

Denoising and R-Peak Detection of Electrocardiogram Signal Based on EMD and Improved Approximate Envelope

The proliferation of Internet-of-Things (IoT) has led to the generation of zettabytes of sensitive data each year. The generated data are usually raw, requiring cloud resources for processing and decision-making operations to extract valuable information (i.e., distill smartness). Use of cloud resources raises serious design issues: limited bandwidth, insufficient energy, and security concerns. Edge-side computing and cryptographic techniques have been proposed to get around these problems. However, as a result of increased computational load and energy consumption, it is difficult to simultaneously achieve smartness, security, and energy efficiency. We propose a novel way out of this predicament by employing signal compression and machine learning inference on the IoT sensor node. An important sensor operation scenario is for the sensor to transmit data to the base station immediately when an event of interest occurs, e.g., arrhythmia is detected by a smart electrocardiogram sensor or seizure is detected by a smart electroencephalogram sensor, and transmit data on a less urgent basis otherwise. Since on-sensor compression and inference drastically reduce the amount of data that need to be transmitted, we actually end up with a dramatic energy bonus relative to the traditional sense-and-transmit IoT sensor. We use a part of this energy bonus to carry out encryption and hashing to ensure data confidentiality and integrity. We analyze the effectiveness of this approach on six different IoT applications with two data transmission scenarios: alert notification and continuous notification. The experimental results indicate that relative to the traditional sense-and-transmit sensor, IoT sensor energy is reduced by $57.1\times$ for electrocardiogram (ECG) sensor based arrhythmia detection, $379.8\times$ for freezing of gait detection in the context of Parkinson's disease, $139.7\times$ for electroencephalogram (EEG) sensor based seizure detection, $216.6\times$ for human activity classification, $162.8\times$ for neural prosthesis spike sorting, and $912.6\times$ for chemical gas classification. Our approach not only enables the IoT system to push signal processing and decision-making to the extreme of the edge-side (i.e., the sensor node), but also solves data security and energy efficiency problems simultaneously.

https://ieeexplore.ieee.org/ielaam/6687315/8630102/8432098-aam.pdf

Smart, Secure, Yet Energy-Efficient, Internet-of-Things Sensors

Automatic extraction of time plane features is extremely important for the diagnosis of cardiac diseases This paper introduces an effective technique based on a second-generation wavelet transform for the denoising of electrocardiogram (ECG) signals An improved half-soft threshold based on the lifting wavelet is used to overcome the drawbacks of thresholds applied in the classic wavelet As ECG signals are corrupted by non-stationary noises, after the threshold point of denoising, Hilbert transform and an improved approximate envelope are applied to the ECG signals to enhance the QRS complexes and suppress unwanted P/T waves and noises A detection algorithm based on the slope threshold is then used for feature detection The results of simulation demonstrated that the proposed method allows for the detection of the locations of the R peak, P peak, T peak, start and end of the QRS complex, and premature ventricular contraction The performance of the algorithm was evaluated against the MIT-BIH Arrhythmi

Detection of electrocardiogram characteristic points using lifting wavelet transform and Hilbert transform

In this paper a robust R Peak and QRS detection using Wavelet Transform has been developed. Wavelet Transform provides efficient localization in both time and frequency. Discrete Wavelet Transform (DWT) has been used to extract relevant information from the ECG signal in order to perform classification. Electrocardiogram (ECG) signal feature parameters are the basis for signal Analysis, Diagnosis, Authentication and Identification performance. These parameters can be extracted from the intervals and amplitudes of the signal. The first step in extracting ECG features starts from the exact detection of R Peak in the QRS Complex. The accuracy of the determined temporal locations of R Peak and QRS complex is essential for the performance of other ECG processing stages. Individuals can be identified once ECG signature is formulated. This is an initial work towards establishing that the ECG signal is a signature like fingerprint, retinal signature for any individual Identification. Analysis is carried out using MATLAB Software. The correct detection rate of the Peaks is up to 99% based on MIT-BIH ECG database.

/pdf/robust-r-peak-and-qrs-detection-in-electrocardiogram-using-wg21txysxt.pdf

Robust R Peak and QRS detection in Electrocardiogram using Wavelet Transform

A novel S-scheme Ws2/BiYWO6 electrostatic heterostructure for enhanced photocatalytic degradation performance towards the degradation of Rhodamine B

A Detailed Review Based on Secure Data Transmission Using Cryptography and Steganography

Globally, people’s health and wealth are affected by the outbreak of the corona virus. It is a virus, which infects from common fever to severe acute respiratory syndrome. It has the potency to transmit from one person to another. It is established that this virus spread is augmenting speedily devoid of any symptoms. Therefore, the prediction of this outbreak situation with mathematical modelling is highly significant along with necessary. To produce informed decisions along with to adopt pertinent control measures, a number of outbreak prediction methodologies for COVID-19 are being utilized by officials worldwide. An effectual COVID-19 outbreaks’ prediction by employing Squirrel Search Optimization Algorithm centric Tanh Multi-Layer Perceptron Neural Network (MLPNN) (SSOA-TMLPNN) along with Auto-Regressive Integrated Moving Average (ARIMA) methodologies is proposed here. Initially, from the openly accessible sources, the input time series COVID-19 data are amassed. Then, pre-processing is performed for better classification outcomes after collecting the data. Next, by utilizing Sine-centered Empirical Mode Decomposition (S-EMD) methodology, the data decomposition is executed. Subsequently, the data are input to the Brownian motion Intense (BI) - SSOA-TMLPNN classifier. In this, the diseased, recovered, and death cases in the country are classified. After that, regarding the time-series data, the corona-virus’s future outbreak is predicted by employing ARIMA. Afterwards, data visualization is conducted. Lastly, to evaluate the proposed model’s efficacy, its outcomes are analogized with certain prevailing methodologies. The obtained outcomes revealed that the proposed methodology surpassed the other existing methodologies.

P Sasikala

Papers

Robust R Peak and QRS detection in Electrocardiogram using Wavelet Transform

A novel S-scheme Ws2/BiYWO6 electrostatic heterostructure for enhanced photocatalytic degradation performance towards the degradation of Rhodamine B

A Detailed Review Based on Secure Data Transmission Using Cryptography and Steganography

An Efficient COVID-19 Disease Outbreak Prediction Using BI-SSOA-TMLPNN and ARIMA

Secure Data Deduplication and Data Portability in Distributed Cloud Server Using Hash Chaining and LF-WDO