Time-Frequency Analysis Of Heart Sounds Using Windowed And Smooth Windowed Wigner-Ville Distribution

Heart sounds and murmurs are time-varying signals that would best be analyzed using time-frequency analysis. Windowed Wigner-Ville distribution (WWVD) and smooth windowed Wigner-Ville distribution (SWWVD) are used to obtain the timefiequency representation (TFR) of the signal. Determination of parameter setting of WWVD and SWWVD will eliminate the cross-terms and improve TFR. The accuracy of TFR will be determined based on the maiulohe width and signal-to-interference ratio. It is found that the most accurate TFR can be achieved using SWWVD

Digital Mapping of UMK Jeli Campus Using Drone Technology

Aerial or satellite images are conventionally used for geospatial data collection and in producing a topographic map. The Unmanned Aerial Vehicles (UAV) technology such as drone has developed by providing very high spatial and temporal resolution data at a lower cost. Nowadays, drones not only use for military purpose but also been utilized widely by the public community for mapping, monitoring, video capturing activities and as a hobby. This present study focuses on the utilization of drone technology to produce a digital map of UMK Jeli Campus. The objective of this study is to access the capability and the accuracy of the drone in producing a digital map. Parrot ANAFI and DJI FC6310 devices were used as a platform to acquire digital images of the study area. After capturing the digital images, ground control points were established with the aid of a handheld global positioning system (GPS) device. Images were processed using Agisoft Photoscan software to produce a digital map of UMK Jeli Campus. This study shows that UAV can be used for producing a digital map at sub-meter accuracy and it can also be used for diversified applications

The Quantitative Geomorphology of Upper Citarik Watershed and Its Implication to the Flash Flood Potential

The research area is the Upper Citarik Watershed, located in the eastern Bandung Basin, West Java, Indonesia. This research aims to identify the quantitative geomorphology, especially the morphometry of the Upper Citarik Watershed, and its implication for the potential of flash floods. This research was conducted through a studio analysis with the support of thematic maps such as geological and slope maps. The parameters used in this morphometry calculation consist of linear aspects (stream order, stream length, mean stream length, stream length ratio, bifurcation ratio, and mean bifurcation ratio); areal aspect (drainage density, drainage texture, form factor, ratio of circularity, ratio of elongation, and length of overland flow); and relief aspects (watershed relief and relief ratio). The research results show that the Upper Citarik Watershed consists of 17 sub-watersheds that share relatively similar characteristics, including elongated shape with high relief and rather steep - steep slopes. It is predominantly composed of volcanic rocks and slow rising of flash flood. It shows that from a quantitative geomorphology perspective, the research area has high resistance to flash floods. Land use changes need to be a concern to prevent a significant decrease in the ability of land to deal with the flash floods

Integrating Remote Sensing and GIS Techniques for Accurate Mapping and Analysis of Oil Palm Plantation Distribution in Kelantan: A Case Study

The research conducted in Kelantan focused on analysing the distribution of oil palm plantations using remote sensing data and ArcGIS, a Geographic Information System (GIS) platform. The demand for accurate and up-to-date information on oil palm plantations has been increasing due to advancements in technology and the need for effective management of the environment. The study aimed to compare the distribution of oil palm plantations in 2016 and 2021 by using vegetation analysis techniques such as Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Soil-Adjusted Vegetation Index (SAVI). Remote sensing data from Landsat 8, specifically Bands 5, 4, and 2, were utilized to derive these vegetation indices. Ground-truthing data, obtained through GPS coordinates, were employed to increase the accuracy of the analysis. The expansion of oil palm plantations and non-oil palm areas was assessed using the Supervised Classification Maximum Likelihood method. The distribution data of oil palm plantations is highly sought after by oil palm plantation companies and serves public and private purposes, contributing to environmental monitoring and promoting sustainable practices

Integrating Remote Sensing and GIS Techniques for Accurate Mapping and Analysis of Paddy Field Distribution in Kelantan: A Case Study

This research focuses on studying the distribution of paddy fields in Kelantan between 2016 and 2021, utilizing Geographic Information System (GIS) and satellite imagery from Landsat 8 acquired through remote sensing. The primary objective is to examine the spatial distribution of paddy fields within a specific area in Kelantan and analyze changes that occurred during the mentioned time frame. By combining satellite images, GPS technology, the study aimed to offer comprehensive insights into distribution patterns and changes in paddy fields, which can inform decisions related to paddy field production and agricultural management. Maps of Kelantan were obtained from Earth Explorer websites, processed, and analyzed to calculate vegetation indices like Enhanced Vegetation Index (EVI), Normal Difference Vegetation Index (NDVI), and Soil-Adjusted Vegetation Index (SAVI). The study's key findings reveal the distribution of paddy fields in Kelantan, with an area of 27,420.88 hectares in 2021, marking a 2.31% decrease compared to the previous year's 28,863.91 hectares. The research successfully achieved its goals, assessing vegetation indices and creating a distribution map of paddy fields in Kelantan. These findings have the potential to contribute to effective agricultural management and decision-making processes in the study area

Comparison of time-frequency analysis performance on hearts and murmurs

Time-frequency analysis is one of the methods to deal with non-stationary and time-varying signal such as heart sounds and murmurs. Wigner-Ville distribution (WVD) is a popular tool to obtain time-frequency representation of signal. Windowed Wigner-Ville distribution (WWVD) and smooth windowed Wigner-Ville distribution (SWWVD) are the improvement method to overcome the problem faced by WVD. Determination of parameter setting of WWVD and SWWVD eliminates the cross-terms and improve time-frequency representation. The accuracy of time-frequency representation of simulated heart sounds and murmurs are compared and determined based on the mainlobe width, peak-to-sidelobe average ratio and signal-to-interference ratio. It is found that the most accurate time-frequency representation can be obtained using the SWWVD

Time-frequency analysis and classification of heart sounds and murmurs

Heart sounds and murmurs are time-varying and non-stationary signals. It can show the difference between normal heart and pathological heart murmur. The signal gathered from heart auscultation and phonocardiogram do not provide permanent record of examination result for future evaluation. In addition, the auscultation needs a skilled physician to determine the heart condition correctly. Time-frequency distribution (TFD) is a method that can represent non-stationary and time-varying signals. Contrast to time-domain or frequency-domain, TFD is able to show the variation in the frequency content of the signal with time. In this study, the selected TFD for analysis purposes are the Wigner-Ville distribution (WVD), windowed Wigner-Ville dis$bution (WWVD) and smooth windowed Wigner-Ville distribution (SWWVD). The main contribution is to determine the distribution that accurately shows the time-frequency representation of heart sounds and murmurs. Comparison is made based on the mainlobe width (MLW), peak-to-sidelobe average ratio (PSAR) and signal-to-interference ratio (SIR). In general, the SWWVD shows the most accurate time-frequency representation based on the SIR which achieved 16.40 dB for normal heart compared to -4.82 dB using the WVD. From the time-frequency representation, a further operation of signal detection using the Moyal's formula can be applied. The Moyal's forrnula is used to classify the murmurs in the presence of noise. On the average, time-frequency classification performs better over the timedomain correlation by -9.94 dB of noise power added for all signal used. This is mainly due to the non-stationarity of the signal which is good to be analyzed in TFD

Analysis of Heart Sounds Based on Continuous Wavelet Transform

This paper presents the application of wavelet transform analysis method to the heart sounds signal. The heart sounds is a non-stationery signal, thus it is very important to study the frequency and time information. One of the time-frequency analysis methods is short time Fourier transforms. However, the STFT analysis is limited by the time and frequency resolution. The wavelet transform was introduced to curb the resolution problem in STFT. The wavelet transform is a multi-resolution time-scale analysis that gives high resolution for low frequency components and low resolution for high frequency components. Since majority of heart sounds component lies in low frequency, thus the application of wavelet transform to heart sounds is very suitable. Results in time-frequency representation clearly show that the wavelet transform is capable to distinguish between the normal with a few types of abnormal heart sounds. The murmurs caused by particular heart diseases such as aortic regurgitation, aortic stenosis, mitral regurgitation, mitral stenosis, pulmonary regurgitation and tricuspid regurgitation were clearly shown under continuous wavelet representation

Resolving Gender Difference in Problem Solving Based On the Analysis of Electroencephalogram (EEG) Signals

Problem solving is regarded as one of the core work-related abilities and skills, which are highly demanded by the workplace and industry. Current literature suggests that problem solving abilities might differ from one individual to another due to biological factors such as brain activationa, cognitive functions and hormones, as well as due to socio-cultural and socio-economic factors like gender roles, self-perceptions and stereotyping. Hence, this study used electroencephalogram (EEG) signals to investigate the differences in problem solving skills among the Malaysian undergraduates based on their gender differences. 29 undergraduate students from the Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM) served as the subjects of the experiments in this research. Specifically, 16 female and 13 male subjects engaged in two main problem-solving tasks: mental arithmetic task and Tower of Hanoi (TOH) task. The EEG data were analysed using partial directed coherence (PDC) and power spectrum estimation (PSE). Based on the results, female subjects achieved only 1% higher performance in mental arithmetic task, while male subjects achieved about 13% higher performance in TOH task. The differences in terms of the functional connectivity between brain regions, i.e. in PDC, as well as the power distribution of 6 EEG waveforms, i.e. delta, theta, alpha, beta, gamma and high gamma bands are also highlighted and represented graphically in this paper

Oil palm tree enumeration based on template matching

Remote sensing imagery is one of the methods for agricultural monitoring provided with a proper technique of application and implementation. Since Malaysia is one of the top nations providing the palm oil product with best quality, thus research of sustainable utilization and development associating with oil palm tree and fruits should be the forefront of agenda. Currently, Malaysia is the world price benchmark for crude palm oil (CPO) even though Indonesia has taken the title of being the world's largest CPO producer in 2006. Thus, Malaysia need to enhance the research and development by utilizing remote sensing technology where oil palm plantation can be monitored and managed in a more effective, efficient and low-cost maintenance. This can be possible if the numbers of oil palm tree can be automatically enumerated and then categorized into healthy and disease trees based on remote sensing images. Observation from on-site plantation request higher cost and may lead to human error in providing accurate statistics of oil palm trees. This study intend to tackle this problem by integrating template matching analysis on WorldView-2 imagery data to discriminate the features in remote sensing imageries for enumerating oil palm trees. Thus, this study will focus on investigating the implementation of correlation coefficient of template matching techniques that best fit for WorlView-2 imagery data for recognition and characterization of oil palm tree