Ensemble empirical mode decomposition of photoplethysmogram signals in biometric recognition

This research focuses on using photoplethysmogram (PPG) signals for biometric recognition. Specifically, the biometric traits studied are the ensemble empirical mode decomposition (EEMD) and power spectral density (PSD) of the PPG signals. The classifiers used for testing the performance of the algorithm were K-nearest neighbors algorithm (KNN), support vector machine (SVM), and random forest (RF). Training, testing, and k-fold cross validation were done using data from public database. PPG was found to be suitable for biometric recognition, although with weakness that may be addressed through gathering and training of larger sets of data. © 2019 IEEE

Ensemble empirical mode decomposition of photoplethysmogram signals for assessment of ventricular fibrillation

Ventricular fibrillation is a type of cardiac arrhythmia which is responsible for several cases of sudden cardiac arrests. As many cases of arrhythmia result to fatality, it is the goal of this research to develop a method to analyze this condition through the use of ensemble empirical mode decomposition (EEMD). EEMD is a variant of empirical mode decomposition (EMD) which solves its weakness in terms of mode mixing. EEMD results to the decomposition of a signal into its intrinsic mode functions(IMFs). The IMFs, together with their power spectral densities (PSDs) of photoplethysmogram (PPG) signals are analyzed for cases with and without ventricular fibrillation. Also, IMFs and PSDs are used as the features for classifying the presence of this condition. Principal component analysis (PCA) is used to reduce the large dimension of the features. In classifying, k-NN classifier was used. It was found that the IMFs of a signal with and without ventricular fibrillation resampled at 250 Hz and at window length of 1000 has most of its signal energy at the 5thto 8th siftings. The highest overall classification accuracy of 83.75%was achieved with noise width of 0.1. Thus, the ensemble empirical mode decomposition of PPG signals was successfully used for assessment of ventricular fibrillation and further modifications with the parameters and pre-processing techniques may be done to improve classification accuracy based on this feature. © 2018 IEEE

A machine learning approach for coconut sugar quality assessment and prediction

This study presents a machine learning approach to accurately assess the quality of coconut sugar using RGB values. Python and scikit-learn were used to run the following machine learning algorithms: artificial neural network (ANN), stochastic gradient descent (SGD), k-nearest neighbors (k-NN) algorithm, support vector machine (SVM), decision tree (DT) and random forest (RF). Comparisons were made between the aforementioned machine learning algorithms by evaluating the accuracy and the average running time of each training model. Results of the study show that the SGD is superior in terms of accuracy but falls short to k-NN and SVC in terms of running time. In this fashion, a plot between the accuracy and the running time was made and it was observed that algorithms with higher accuracies correspondingly have also higher running times. By this very nature, experimental results show that the SGD holds merit in accurately assessing the coconut sugar quality, despite its expense in running time. © 2018 IEEE

Implementation of a normalized cross-correlation coefficient-based template matching algorithm in number system conversion

In digital image processing, template matching is a technique used for finding or searching for areas of an image that could either match or be similar to the template image. In this study, an algorithm that utilizes both Python programming and the OpenCV library for template matching in number system conversion was successfully demonstrated. Images containing binary numbers were tested for template matching and converted to string. Then, these strings were converted to their respective decimal equivalents. It was found that OpenCV offers a tool that is easy to use for systems that require recognizing patterns of an image. Furthermore, it was observed that the ease of use is accompanied with various limitations such as dependence to pre-processing or having fixed scale, rotation, font, and background color. © 2017 IEEE.