Adaptive algorithms: a bibliographic review

The analysis of a research work developed in the company C.V.G CARBONORCA of Venezuela is presented, which has two gas purification plants for the cooking area, designed to purify the gas that comes from the cooking ovens. Each plant is made up of solenoid valves, pneumatic valves, transmitters, process mimic panel and a supervisory system. All these elements are governed by a SIEMENS S5-115U PLC which is in a state of obsolescence, which is why the replacement of these automata by ALLEN BRADLEY ContolLogix automata was designed, in order to guarantee continuity in operations in plant. The research was done with a descriptive design of the field experimental type. A code for each gas treatment plant was obtained in RSLOGIX 5000 v17.00.00 and the update of the database of the supervisory system. The operation of the program was also verified through a simulation of the plant in a supervisory system, the deployment of which was designed for this purpose. Keywords: Automation, Modernization, ControlLogix, Supervisory System, Mimic Panel References [1]M. Simao, N. Mendes, O. Gibaru y P. Neto, «A Review on Electromyography Decoding and Pattern Recognition for Human-Machine Interaction,» IEEE Access, vol. 7, pp. 39564 - 39582, 2019. [2]Instituto de Estadística de la Organización de las Naciones Unidas para la Educación, la Ciencia y la Tecnología, «Clasificación Internacional Normalizada de la Educación CINE,» UNESCO Institute for Statistics, Montréal, 2011. [3]Y. Zheng y H. Xiaogang, «Interference Removal From Electromyography Based on Independent Component Analysis,» IEEE Trans Neural Syst Rehabil Eng, vol. 27, nº 5, pp. 887-894, Mayo 2019. [4]B. Afsharipour, F. Petracca, M. Gasparini y R. Merletti, «Spatial distribution of surface EMG on trapezius and lumbar muscles of violin and cello players in single note playing,» Journal Electromyography Kinesiology, vol. 31, pp. 144 - 153, 2016. [5]M. Niegowski, M. Zivanovic, M. Gómez y P. Lecumberri, «Unsupervised learning technique for surface electromyogram denoising from power line interference and baseline wander,» de 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italia, 2015. [6]S. D. Soedirdjo, K. Ullah y R. Merletti, «Power line interference attenuation in multi-channel sEMG signals: Algorithms and analysis,» de Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., 2015. [7]A. Phinyomark, F. Quaine, S. Charbonnier, C. Serviere, F. Tarpin-Bernard y Y. Laurillau, «Feature extraction of the first difference of EMG time series for EMG pattern recognition,» Computer Methods and Programs in Biomedicine, vol. 117, nº 2, pp. 247-256, Noviembre 2014. [8]M. Malboubi, F. Razzazi, M. Aliyari y A. DAvari, «Power line noise elimination from EMG signals using adaptive Laguerre filter with fuzzy step size,» de 17th Iranian Conference of Biomedical Engineering (ICBME), Isfahan, Iran, 2010. [9]C. Luca, L. Gilmore, M. Kuznetsov y S. Roy, «Filtering the surface EMG signal: Movement artifact and baseline noise contamination,» J. Biomech, pp. 1573-1582, 28 Mayo 2010. [10]R. Mello, L. Oliveira y J. Nadal, «Digital Butterworth filter for subtracting noise from low magnitude surface electromyogram,» Comput Methods Programs Biomed, vol. 1, nº 87, pp. 28-35, 2007. [11]A. Botter y T. Vieira, «Filtered virtual reference: A new method for the reduction of power line interference with minimal distortion of monopolar surface EMG,» IEEE Transactions on Biomedical Engineering, vol. 62, nº 11, pp. 2638 - 2647, 2015. [12]J. R. Potvin y S. H. Brown, «Less is more: high pass filtering, to remove up to 99% of the surface EMG signal power, improves EMG-based biceps brachii muscle force estimates,» J. Electromyogr. Kinesiol., vol. 14, nº 3, pp. 389-399, 2004. [13]D. T. Mewett, K. J. Reynolds y H. Nazeran, «Reducing power line interference in digitised electromyogram recordings by spectrum interpolation,» Med. Biol. Eng. Comput., vol. 4, nº 42, pp. 524-531, 2004. [14]D. T. Mewett, H. Nazeran y K. J. Reynolds, «Removing power line noise from recorded EMG,» de 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Istanbul, Turkey, 2001.

Performance Evaluation of Wavelet De-Noising Schemes for Suppression of Power Line Noise in Electrocardiogram Signals

Power line noise introduces distortions to recorded electrocardiogram (ECG) signals. These distortions compromise the integrity and negatively affect the interpretation of the ECG signals. Despite the fact that the amplifiers used in biomedical signal processing have high common mode rejection ratio (CMRR), ECG recordings are still often corrupted with residual Power Line Interference (PLI) noise. Further improvement in the hardware solutions do not have significant achievements in PLI noise suppression but rather introduce other adverse effects. Software approach is necessary to refine ECG data. Evaluation of PLI noise suppression in ECG signal in the wavelet domain is presented. The performance of the Hard Threshold Shrinkage Function (HTSF), the Soft Threshold Shrinkage Function (STSF), the Hyperbola Threshold Shrinkage Function (HYTSF), the Garrote Threshold Shrinkage Function (GTSF), and the Modified Garrote Threshold Shrinkage Function (MGTSF) for the suppression of PLI noise are evaluated and compared with the aid of an algorithm. The optimum tuning constant for the Modified Garrote Threshold Shrinkage Function (MGTSF) is found to be 1.18 for PLI noise. GTSF is found to have best performance closely followed by MGTSF in term of filtering Gain. HTSF recorded the lowest Gain. Filtering against PLI noise in the wavelet domain preserves the key features of the signal such as the QRS complex.

Common-Mode Voltage Reduction in Capacitive Sensing of Biosignal Using Capacitive Grounding and DRL Electrode

A capacitive measurement of the biosignals is a very comfortable and unobtrusive way suitable for long-term and wearable monitoring of health conditions. This type of sensing is very susceptible to noise from the surroundings. One of the main noise sources is power-line noise, which acts as a common-mode voltage at the input terminals of the acquisition unit. The origin and methods of noise reduction are described on electric models. Two methods of noise removal are modeled and experimentally verified in the paper. The first method uses a passive capacitive grounding electrode, and the second uses an active capacitive Driven Right Leg (DRL) electrode. The effect of grounding electrode size on noise suppression is experimentally investigated. The increasing electrode area reduces power-line noise: the power of power-line frequency within the measured signal is 70.96 dB, 59.13 dB, and 43.44 dB for a grounding electrode area of 1650 cm2, 3300 cm2, and 4950 cm2, respectively. The capacitive DRL electrode shows better efficiency in common-mode noise rejection than the grounding electrode. When using an electrode area of 1650 cm2, the DRL achieved 46.3 dB better attenuation than the grounding electrode at power-line frequency. In contrast to the grounding electrode, the DRL electrode reduces a capacitive measurement system’s financial costs due to the smaller electrode area made of the costly conductive textile.

Delayed LMS Algorithm for Ballistocardiogram Biomedical Signal

The Ballisto-cardiogram (BCG) is a biomedical signal which is basically a measure of ballistic forces on heart. Just like ECG to detect the abnormalities in heart Ballisto-cardiography technique is used extensively now a day in research to analyze the abnormalities of the patient. When the blood pumps from heart to different parts of the body is represented in the form of graph for each heart beat. The frequency of Ballisto-cardiography signal is 1-20Hz .Ballisto-cardiography is most emerging techniques which is used to test the diseases related to heart called as cardiovascular disease. Various devices [1] like chairs, beds and weighing scales are projected to improvise the extraction of the BCG, but noise is one of the main issues with this BCG signal processing, noise is generated because of motion artifacts, shaking of the devices or may be power line noise. This noise [2] affects the quality of signal which we need to test. In order to overcome such issue this paper proposes a new architecture making use of LMS filtering algorithm Here weight update algorithm is used to update the error in extracted signal. The architecture proposed here includes FIR filter and also error computation blocks. Here the author has implemented 5-tap filtering algorithm. MATLAB and system generators are used to carry out the work.

Two-Electrode ECG for Ambulatory Monitoring with Minimal Hardware Complexity

This article introduces a two-electrode ground-free electrocardiogram (ECG) with minimal hardware complexity, which is ideal for wearable battery-powered devices. The main issue of ground-free measurements is the presence of noise. Therefore, noise suppression methods that can be employed for a two-electrode ECG acquisition system are discussed in detail. Experimental measurements of a living subject and patient simulator are used to investigate and compare the performance of the three proposed methods utilizing the ADS1191 analogue front-end for biopotential measurements. The resulting signals recorded for the simulator indicate that all three methods should be suitable for suppressing power-line noise. The Power Spectral Density (PSD) of the signals measured for a subject exhibits differences across methods; the signal power at 50 Hz is −28, −24.8, and −26 dB for the first, second, and third method, respectively. The digital postprocessing of measured signals acquired a high-quality ECG signal comparable to that of three-electrode sensing. The current consumption measurements demonstrate that all proposed two-electrode ECG solutions are appropriate as a battery-powered device (current consumption < 1.5 mA; sampling rate of 500 SPS). The first method, according to the results, is considered the most effective method in the suppression of power-line noise, current consumption, and hardware complexity.

Normalized shaping regularization for robust separation of blended data

Simultaneous source acquisition has attracted more and more attention from geophysicists because of its cost savings, whereas it also brings some challenges that have never been addressed before. Deblending of simultaneous source data is usually considered as an underdetermined inverse problem, which can be effectively solved with a least-squares (LS) iterative procedure between data consistency ([Formula: see text]-norm) and regularization ([Formula: see text]-norm or [Formula: see text]-norm). However, when it comes to abnormal noise that follows non-Gaussian distribution and possesses high-amplitude features (e.g., erratic noise, swell noise, and power line noise), the [Formula: see text]-norm is a nonrobust statistic that can easily lead to suboptimal deblended results. Although abnormal noise can be attenuated in the common source domain at first, it is still challenging to apply a coherency-based filter due to the sparse receiver or crossline sampling, e.g., that commonly found in ocean bottom node (OBN) acquisition. To address this problem, we have developed a normalized shaping regularization to make the inversion-based deblending approach robust for the separation of blended data when abnormal noise exists. Its robustness comes from the normalized shaping operator defined by the confidence interval of normal distribution, which minimizes the abnormal risk to a normal level to satisfy the assumption of LS shaping regularization. In special cases, the proposed approach will revert to the classic LS shaping regularization once the normalized coefficient is large enough. Experimental results on synthetic and field data indicate that the proposed method can effectively restore the separated records from blended data at essentially the same convergence rate as the LS shaping regularization for the abnormal noise-free scenario, but it can obtain better deblending performance and less energy leakage when abnormal noise exists.

Design Considerations for Improved Noise Rejection in Surface EMG Systems

Bioelectric signals are distorted by unwanted electric noise interference. This paper focuses on techniques that can be applied to surface electromyographic systems design to improve the signal-to-noise ratio. Three case studies are presented in this manuscript: Effects of the front-end instrumentation amplifier gain, use of dc-dc converters for single-supply operation, and dedicated hardware for 60 Hz power line noise rejection. Results show that the quality of the signal is highly improved when the suggested techniques are applied.

Separation Enhancement of Power Line Noise from Human ECG Signal Based on Stone Technique

The cardiac signal is very important for the heart disease diagnosis and evaluation. The noise cancelation represent one of the most preprocessing step in ECG signal processing, usually, this signal is very sensitive and varies with time. The ECG signal is mostly contaminated by different signals like Power line noise signal, Baseline signal and muscle signal. The power line interference signal is the most effected signal on the ECG during data recording. Several papers try to cancel the noise based on different ways and to extract the useful information. In this paper a novel approach based on stone blind source extraction is used to extract the pure ECG signal from raw ECG, the main advantage of the proposed approach compared with the classical technique is to separate all the useful information without filtering or cancelling the suitable data from the recording signal. Real ECG data from MIT-BIH databases is taken and the MATLAB program is used to evaluate the experimental results. The performance of the proposed approach is measured based on SNR and MSE. The main contribution of this paper is to use Stone blind source separation technique as a first time in ECG signal analysis and prove that this method is the best technique compared with conventional ways. The obtained result proves Stone BSS technique is very efficient to remove the power line noise.