Wavelet diagnosis of ECG signals with kaiser based noise diminution

The evaluation of distortion diagnosis using Wavelet function for Electrocardiogram (ECG), Electroen- cephalogram (EEG) and Phonocardiography (PCG) is not novel. However, some of the technological and economic issues remain challenging. The work in this paper is focusing on the reduction of the noise inter- ferences and analyzes different kinds of ECG signals. Furthermore, a physiological monitoring system with a programming model for the filtration of ECG is presented. Kaiser based Finite Impulse Response (FIR) filter is used for noise reduction and identifica- tion of R peaks based on Peak Detection Algorithm (PDA). Two approaches are implemented for detect- ing the R peaks; Amplitude Threshold Value (ATV) and Peak Prediction Technique (PPT). Daubechies wavelet transform is applied to analyze the ECG of driver under stress, arrhythmia and sudden cardiac arrest signals. From the obtained results, it was found that the PPT is an effective and efficient technique in detecting the R peaks compared to ATV

Hybrid of Eddy Current Probe Based on Permanent Magnet and GMR Sensor

The eddy current testing (ECT) is used to inspect a material to determine its properties without destroying its utility. The applications include detection of flaws in aircrafts, pipeline, etc. An ECT is a weak sensitivity to a subsurface defect. Applications of giant magnetic sensors (GMR) are increasingly applied to the measurement of weak magnetic fields related to the currents they cause. In this paper, GMR sensor with magnet bar (permanent) is utilized. The proposed probe system is utilized to study the impact of the width and depth defect on the signal of eddy current testing. The maximum depth of flaw in a mild steel can be revealed by using this probe. The graph of the difference between the peak amplitude and the penetration depth of each slot of a different width of the two bands of mild steel shows the increase of the signal for each slot and flat above 3mm. The experimental result proves the inability of a PMGMR probe to detect a defect at a depth of 3mm on a surface defect

Analytical modelling of sensing performance of carbon nanotubes for gas sensing

Carbon nanotubes as a new variety of quasi one-dimensional (1D) materials belong to the family of carbon-based nanostructures, which have recently ignited tremendous research interest. The latest discoveries of the outstanding properties of carbon nanotubes (CNTs) in terms of their electronic and structural behaviours such as, large surface-to-volume ratio, tunable band gap, high mechanical strength, high mobility and extreme sensing capability make them a great candidate for nanoelectronic devices of the future. Due to the importance of nanoscale sensors and biosensors in various areas of our lives, using promising materials such as carbon nanotubes has widely captured the attention of researchers to achieve better sensitivity and accuracy in these devices. Up until now, the majority of investigations have focused on experimental studies for sensors. Therefore, there is a lack of analytical models in comparison to experimental investigations. In order to model the transport parameters of a CNT-based gas sensor, the field effect transistor (FET)-based structure has been employed as a primary model for a gas detection sensor. The conductance of the carbon nanotube has been affected under exposure to the target analyte – NH3 gas molecules. The absorption of NH3 gas concentration on the CNT surface follows a chemical reaction between CNTs and the NH3 gas. Therefore, it modulates the current-voltage (I-V) characteristics and conductance of the proposed CNT-based gas sensor. The I-V characteristics of the CNT-based sensors have been proposed as a criterion to detect the effect of gas absorption. Finally, the accuracy of the proposed models were validated by benchmarking them on existing experimental works

Hybrid of Eddy Current Probe based on Permanent Magnet and GMR Sensor

The eddy current testing (ECT) is used to inspect a material to determine its properties without destroying its utility. The applications include detection of flaws in aircrafts, pipeline, etc. An ECT is a weak sensitivity to a subsurface defect. Applications of giant magnetic sensors (GMR) are increasingly applied to the measurement of weak magnetic fields related to the currents they cause. In this paper, GMR sensor with magnet bar (permanent) is utilized. The proposed probe system is utilized to study the impact of the width and depth defect on the signal of eddy current testing. The maximum depth of flaw in a mild steel can be revealed by using this probe. The graph of the difference between the peak amplitude and the penetration depth of each slot of a different width of the two bands of mild steel shows the increase of the signal for each slot and flat above 3mm. The experimental result proves the inability of a PMGMR probe to detect a defect at a depth of 3mm on a surface defect

Design and simulation study of antenna for wireless body area network (WBAN)

In this work, a flexible microstrip patch antenna has been designed to be operated at frequency 2.45GHz (ISM band). Three different materials have been used in this work which is rubber with permittivity of 3.0, loss tangent of 0.02 and thickness of 2.70mm, polydimethylsiloxane (PDMS) with dielectric constant of 2.71, loss tangent of 0.0134 and thickness of 1mm, and jeans with permittivity of 1.7, loss tangent is 0.025 and thickness of 1mm. Different substrate permittivity affect the antenna performance in various ways. The antenna is designed using CST Studio Suite 2019 software and the parameter such as return loss, VSWR, gain, directivity, and radiation pattern are analyzed. Here, the antenna performance when bending at five different angle, SAR value using human body layer and antenna performance when attach to the human arm model will be discuss in this paper

Design and analysis of an early heart attack detection using openCV

Millions of people die every year from heart attacks, according to research. The healthcare industry generates massive volumes of data related to heart attacks, but this data is sadly not being processed for hidden insights that could improve decision-making. Early detection of heart attack symptoms is a crucial part of treatment at the moment. Numerous researchers, each applying their own unique machine learning approach, have used the UCI machine learning heart attack dataset. This research aims to detect cardiac events with the use of four different algorithms: logistic regression, decision trees, random forest, and k nearest neighbor using python language. Next, in this project, website prediction of the heart attack prediction are build using python and flask framework. Hyper-parameter tuning method also has been applied to see does the algorithm increase accuracy or not

The Fabrication of PDMS mould for Microelectrode Array Biochip using NIL

In recent years, low-cost micro and nano fabrication process have gain intention from the manufacturing industry. Biochip is a platform of miniaturized microarrays arranged on a solid substrate that allows various biological tests to achieve immediate results. The development of biochip has established a new platform in biomedical industry. However, to fulfill the demands and availability in the market with affordable cost requires high volume manufacturing techniques for the fabrication of the biochips. In this article we will discuss the fabrication of PDMS mould for replicating microelectrode array of biochip. The fabrication of the microelectrodes utilizes the Nanoimprint lithography (NIL) technique. Finally, the fabrication of PDMS mould has been demonstrated successfully for using Nanoimprint lithography (NIL) technique and achieved 13 % of size difference in overall

Investigation of traffic sign image classification for self driving car

Artificial Intelligence has had a good impact on all fields and is making our lives easier. With the growth of autonomous vehicles, the automotive industry is improving rapidly. Autonomous vehicles are a certain conclusion in the future, and they are intended to be both safe and convenient. One of the most critical issues for autonomous vehicles is traffic sign classification. Half occlusion, colour fade by surrounding barriers, variations in shadows, reflections on signboards during the day, and movement blurring different lighting and weather situations are some of the most typical issues that might occur when identifying and detecting traffic signs. In the classification and identification of road signs, the performance of a Convolutional Neural Network (CNN) has outperformed the same of humans. The purpose of this study is to boost the accuracy of this classification in order to minimize accidents and enhance the credibility of selfdriving vehicles. Otherwise, the ecology of traffic may be jeopardised. Using image processing and machine vision processing technologies, as well as the use of in-depth learning in target classification, the traffic sign recognition method based on CNN is studied. A traffic sign detection and classification method with high efficiency and high efficiency are proposed. The German Traffic Sign Recognition Benchmark (GTSRB) is employed to test the approach method, and the results reveal that it outperforms state-of-the-art approaches

ECG noise reduction technique using Antlion Optimizer (ALO) for heart rate monitoring devices

The electrocardiogram (ECG) signal is susceptible to noise and artifacts and it is essential to remove the noise in order to support any decision making for specialist and automatic heart disorder diagnosis systems. In this paper, the use of Antlion Optimization (ALO) for optimizing and identifying the cutoff frequercy of ECG signal for low-pass filtering is investigated. Generally, the spectrums of the ECG signal are extracted from two classes: arrhythmia and supraventricular. Baseline wander is removed using the moving median filter. A dataset of the extracted features of the ECG spectrums is used to train the ALO. The performance of the ALO with various parameters is investigated. The ALO-identified cutoff frequency is applied to a Finite Impulse Response (FIR) filter and the resulting signal is evaluated against the original clean and conventional filtered ECG signals. The results show that the intelligent AL0-based system successfully denoised the ECG signals more effectively than the conventional method. The percentage of the accuracy increased by 2%

Particle size and rate of biochar affected the phytoavailability of Cd and Pb by mustard plants grown in contaminated soils

Various amendments are used to reduce the phytoavailability of heavy metals in contaminated soils, but recently the use of biochar is receiving serious attention. In this study, two particle sizes of an oil palm empty fruit bunch biochar (EFBB); 2 mm (C-EFBB) were applied at either 0, 0.5, or 1% (w/w) to soils contaminated with either Cd or Pb and the phytoavailability of these metals by mustard plants grown on the soils was evaluated. Results revealed that the application of EFBB at 1% significantly increased plant growth parameters as compared with the control in Cd-soil. However, there was no significant effect of EFBB application rate on plant growth parameters in Pb-soil. There was a significant difference in the concentrations of Cd and Pb in the plant root and shoot between soils receiving different particle sizes of EFBB. The treatment of 1% F-EFBB gave the lowest concentration of the Cd concentration in the shoot (115.200 mgkg−1) and Pb concentration in the root and shoot (4196.000 and 78.467 mgkg−1, respectively) as compared with the other treatments. Therefore, F-EFBB application at high rates can be recommended for reducing the phytoavailability of Cd and Pb in contaminated soils