An enhanced stress indices in signal processing based on advanced mmatthew correlation coefficient (MCCA) and multimodal function using EEG signal

Stress is a response to various environmental, psychological, and social factors, resulting in strain and pressure on individuals. Categorizing stress levels is a common practise, often using low, medium, and high stress categories. However, the limitation of only three stress levels is a significant drawback of the existing approach. This study aims to address this limitation and proposes an improved method for EEG feature extraction and stress level categorization. The main contribution of this work lies in the enhanced stress level categorization, which expands from three to six levels using the newly established fractional scale based on the quantities' scale influenced by MCCA and multimodal equation performance. The concept of standard deviation (STD) helps in categorizing stress levels by dividing the scale of quantities, leading to an improvement in the process. The lack of performance in the Matthew Correlation Coefficient (MCC) equation is observed in relation to accuracy values. Also, multimodal is rarely discussed in terms of parameters. Therefore, the MCCA and multimodal function provide the advantage of significantly enhancing accuracy as a part of the study's contribution. This study introduces the concept of an Advanced Matthew Correlation Coefficient (MCCA) and applies the six-sigma framework to enhance accuracy in stress level categorization. The research focuses on expanding the stress levels from three to six, utilizing a new scale of fractional stress levels influenced by MCCA and multimodal equation performance. Furthermore, the study applies signal pre-processing techniques to filter and segregate the EEG signal into Delta, Theta, Alpha, and Beta frequency bands. Subsequently, feature extraction is conducted, resulting in twenty-one statistical and non-statistical features. These features are employed in both the MCCA and multimodal function analysis. The study employs the Support Vector Machine (SVM), Random Forest (RF), and k-Nearest Neighbour (k-NN) classifiers for stress level validation. After conducting experiments and performance evaluations, RF demonstrates the highest average accuracy of 85%–10% in 10-Fold and K-Fold techniques, outperforming SVM and k-NN. In conclusion, this study presents an improved approach to stress level categorization and EEG feature extraction. The proposed Advanced Matthew Correlation Coefficient (MCCA) and six-sigma framework contribute to achieving higher accuracy, surpassing the limitations of the existing three-level categorization. The results indicate the superiority of the Random Forest classifier over SVM and k-NN. This research has implications for various applications and fields, providing a more effective equation to accurately categorize stress levels with a potential accuracy exceeding 95%

Analysis of Passive Magnetic Inspection Signals Using the Haar Wavelet and Asymmetric Gaussian Chirplet Model (AGCM)

Nowadays, Non-Destructive Testing (NDT) techniques are an essential foundation of infrastructure retrofit and rehabilitation plans, mainly due to the huge amount of construction, as well as the high cost of demolition and reconstruction. Modern NDT methods are moving toward automated detection methods to increase the speed and probability of detection, which enlarges the size of inspection data and raises the demand for new data analysis methods. NDT methods are divided into two main groups; active and passive. The external potentials are discharged into an object in an active method, and then the reflection wave is recorded. However, the passive methods use the self-created magnetic field of the object. Therefore, the magnetic value of ferromagnetic material in a passive method is less than the magnetic value of an active method, and defects and anomalies detection needs more variety of functional signal processing methods. The Passive Magnetic Inspection (PMI) method, as an NDT-passive technology, is used in this thesis for ferromagnetic materials quantitative assessment. The success of the PMI depends on the detection of anomalies of the passive magnetic signals, which is different for every single test. This research aims to develop appropriate signal processing methods to enhance the PMI quality of defect detection in ferromagnetic materials. This thesis has two main parts and presents two computer-based inspection data analysis methods based on the Haar wavelet and the Asymmetric Gaussian Chriplet Model (AGCM). The Passive Magnetic Inspection method (PMI) is used to scan ferromagnetic materials and produce the raw magnetic data analyzed by the Haar wavelet and AGCM. The first part of this study describes the Haar wavelet method for rebar defect detection. The Haar wavelet is used to analyze the PMI magnetic data of the embedded reinforcement steel rebar. The corrugated surface of reinforcing steel makes the detection of defects harder than in flat plates. The up and down shape of the Haar wavelet function can filter the repeating corrugations effect of steel rebars on the PMI signal and thereby better identify the defects. Toogood Pond Dam piers’ rebar defects, as a case study, were detected using the Haar wavelet analysis and verified by the Absolute Gradient (AG) method using visual comparison of the resultant signals and the correlation coefficient. The predicted number of points with a rebar area loss higher than 4% is generally the same with the AG and the Haar wavelet methods. The mean correlation coefficient between the signals analyzed using the AG and the Haar wavelet for all rebars is 0.8. In the second part of this study the use of the AGCM to simulate PMI signals is investigated. Three rail samples were scanned to extract a three-dimensional magnetic field along specific PMI transit lines of each sample for the AGCM simulations. Errors, defined as the absolute value of the difference between signal and simulation, were considered as a measure of simulation accuracy in each direction. The samples’ lengths differed, therefore error values were normalized with respect to the length to scale data for the three samples. The Simulation Error Factor (SEF) was used to measure the error and sample 3 showed the lower value. Finally, statistical properties of the samples' SEF, such as standard deviation and covariance, were evaluated, and the best distribution was fitted to each of the data sets based on the Probability Paper Plot (PPP) method. The Log-Normal probability distribution demonstrated the best compatibility with SEF values. These distributions and statistical properties help to detect outlier data for future data sets and to identify defects