A review of technologies for heart attack monitoring systems

Every year, approximately 1.35 million people die in car accidents. One of the causes of traffic accidents is a heart attack while driving. Common heart attack warning signs are pain or discomfort in the chest or one or both arms or shoulders, light-headedness, faintness, cold sweat, and shortness of breath. When having a heart attack, a car driver has strong pain in the centre or left side of the chest. Current technology for heart attack detection is based on sensory signal properties such as the electrocardiogram (ECG), heart rate and oxygen saturation (SpO2). This paper is intended to give the readers an overview of technologies for heart attack monitoring system that has been used at the hospital, at the home and in the vehicle. The result shows that ECG, heart rate and SpO2 properties are mostly used by numerous researchers for heart attack monitoring systems at hospitals. Meanwhile, many researchers developed a system by using heart rate, ECG, SpO2 and images as properties for heart attack monitoring systems at home. Existing technologies for heart attack monitoring systems in the vehicle used heart rate and ECG as properties in a system. However, there are no review papers yet on heart attack monitoring systems using image processing in vehicles. We believe that researchers and practitioners will embrace this technology by addressing image processing in the heart attack monitoring system in vehicles

Contrast modification for pre-enhancement process in multicontrast rubeosis iridis images

Existing researchers for rubeosis iridis disease focused on image enhancement as a collective group without considering the multi-contrast of the images. In this paper, the pre-enhancement process was proposed to improve the quality of iris images for rubeosis iridis disease by separating the image into three groups; low, medium and high contrast. Increment, decrement and maintenance of the images’ original contrast were further operated by noise reduction and multi-contrast manipulation to attain the best contrast value in each category for increased compatibility prior subsequent enhancement. As a result, this study proved that there have three rules for the contrast modification method. Firstly, the histogram equalization (HE) filter and increasing the image contrast by 50% will achieve the optimum value for the low contrast category. Experimental revealed that HE filters successfully increase the luminance value before undergoing the contrast modification method. Secondly, reducing the 50% of the image contrast to achieve the optimum value for the high contrast category. Finally, the image contrast was maintained for the middle contrast category to optimise contrast. The mean square error (MSE) and peak signal-to-noise ratio (PSNR) of the outputs were then calculated, yielding an average of 18.25 and 28.87, respectively

Hilbert-peano and zigzag: Two approaches mapping pattern of digital watermarking for text images authentication

The wide increment of information and communication technology nowadays in line with the usage of digital documents, the user from different organizations such as education, military, medical, business, and others tend to transfer any official file through various digital platforms. Thus, to secure this confidential data, a digital watermarking technique was chosen. This paper proposed an improved mapping pattern method of a fragile watermarking authentication algorithm for text images. There are various methods for watermark embedding, which mapping pattern is one of them. The aim is to validate and compare the SCAN pattern for digital watermarking in order to produce a fast and efficient authentication algorithm. We proposed a Zigzag SCAN pattern algorithm and compared it with the proposed Hilbert-Peano scheme. The result from this paper shows that Zigzag SCAN algorithm contributed to a superior performance in regard to processing time, while PSNR and MSE are similar

Analysis of Unclean Hand System Detection Using Template Matching Technique

The aim of this project is to audit the handwashing technique of hospital staff that may cause infection to the patients. This project is to detect unclean washed hands using image processing technique specifically template matching. The detection and recognition of palm in images is the key methodology of this paper. The prototype used for capturing hand images is a dark box with UV light and a camera. Target will need to apply Glogerm on their hands that imitate bacteria. Hence, when they wash their hands inappropriately, Glogerm can be seen in the captured images under the UV light as the unwanted stain on washed hands, the target handwashing technique needs to be improved. Templates of the missed area of washed hands are used to compare the correctness of hand washed techniques by the target. Data of 100 images were taken, results are; 100% accuracy of the hand image without Glogerm, 56.67% of the image that did not wash using water after applying the Glogerm and 45.45% accurate when user wash their hand by using water after applying Glogerm. The overall efficiency of the system in detecting the missed part is 51% accuracy As a summary, this project accurately detects stain percentage that represents the missed part when applying the template matching technique

Three-Dimensional Convolutional Approaches for the Verification of Deepfake Videos: The Effect of Image Depth Size on Authentication Performance

Deep learning has proven to be particularly effective in tasks such as data analysis, computer vision, and human control. However, as this method has become more advanced, it has also led to the creation of DeepFake video sequences and images in which alterations can be made without immediately appealing to the viewer. These technological advancements have introduced new security threats, including in the field of education. For example, in online exams and tests conducted through video conferencing, individuals may use Deepfake technology to impersonate another person, potentially allowing them to cheat by having someone else take the exam in their place. Several detection approaches have been proposed to address these issues, including systems that use both spatial and temporal features. However, existing approaches have limitations regarding detection accuracy and overall effectiveness. The paper proposes a technique for detecting Deepfakes that combines temporal analysis with convolutional neural networks. The study explores various 3-D Convolutional Neural Networks-based (CNN-based) model approaches and different sequence lengths of facial photos. The results indicate that using a 3-D CNN model with 16 sequential face images as input can detect Deepfakes with up to 97.3 percent accuracy on the FaceForensic dataset. Detecting Deepfakes is crucial as they pose a threat to the authenticity of visual media. The proposed technique offers a promising solution to this issue

Automatic Gram Staining for Sputum Slide

The Gram stain is the most important and universally used staining technique in the bacteriology laboratory. Gram staining method is used to do staining of the clinical material or the bacteria from colonies on laboratory media and provide a direct visualization of the morphology of the organisms based on their reactions to the chemical present in stains. A sputum sample slide need to be stained before the quality of the sputum sample is determined. However, due to human inconsistency, some of the slides are heavily stained with dark color whereas some of it is lightly stained. This inconsistency would create a difficulty for automated sputum quality system using image processing. Therefore, an automated gram staining for sputum slide is needed in order to standardize the slide staining. The automated Gram-staining will undergo staining, washing, and drying process. Each process periods are controlled by a timer built in the microcontroller. The analysis is done on the accuracy of the position of the slide stain, the consistency of the amounts of staining solution drop on the slide and the time efficiency of this automated system is compared to manual operation

Implementing book-end division approach using classpoint to energize electrical and electronics engineering student engagement

This study investigates the efficacy of using ClassPoint in improving student engagement during class and its impact on academic performance among electrical and electronics engineering students by using student engagement framework that established by technology-enhanced learning (TEL) environment microsystem. To achieve this objective, five instructors teaching various courses incorporated ClassPoint into their classes. Then, quantitative data on student engagement and academic performance were collected via surveys. The student’s comments are extracted from university teaching evaluation survey (EPAT) for thematic analysis. The descriptive analysis revealed a significant increase in student engagement after ClassPoint was implemented. Furthermore, students appreciated the use of ClassPoint features such as slide-drawing, multiple choice questions, and word clouds during classes. Survey results also show students have greater attentiveness, active participation, and improved interactions with their peers and instructors. Likert scale responses indicate positive correlation between the use of ClassPoint and students’ enhanced performance in class discussions, idea integration, increased interest in learning, and improved classroom dynamics. Moreover, thematic analysis shows the empowering of five element in TEL microsystem with ClassPoint increase the student engagement. This study highlights ClassPoint's effectiveness in creating an inclusive and interactive learning environment, thus, transforming teaching methods for electrical and electronics engineering students

Hand segmentation for chest pain behaviour of a car driver in vehicle by using fusion watershed and blob analysis

Hand segmentation is an important identification of chest pain behaviours among drivers. Chest pain is an early detection of heart-related diseases. Among the many methods in hand segmentation, YCbCr is determined as the most effective colour space to detect the colour of human skin. The threshold applied technique has great robustness in skin colour detection and segmentation of human body objects such as the face, arm, hands and neck through the undertaking of blob analysis. However, one of several obstacles in hand segmentation which resulted in its low accuracy rate of 62% is identified to be an existing connection between the hands and face regions. This connection problem is specifically overcome by the introduction of a fusion watershed technique with blob analysis. As an effective technique for image segmentation, watershed transform would separate the connected area into multiple regions. The conducted analysis then confirmed the hand as the smallest area across other separated regions. Enhanced execution of hand segmentation was subsequently achieved through extraction of the hand region via the output of the fusion watershed by the employment of blob analysis. Experimental results ultimately confirmed a higher accuracy rate of 75% through the employment of the fusion technique

Empowering higher education through a micro-credential program in power electronics course

In line with the global and national shifts towards enhancing educational accessibility, flexibility, and quality, Malaysia has embarked on a transformative journey with the implementation of the Malaysian Education Blueprint 2015–2025 (Higher Education) and E-Learning Guidelines for Malaysian Higher Education Institutes. This strategic initiative acknowledges the pivotal role of e-learning in shaping the future of education by enabling greater access to knowledge and skills. The COVID-19 pandemic has further accelerated the growth of e-learning, emphasizing the need for innovative educational approaches. This paper presents a micro-credential program in the field of power electronics, aligned with Malaysia's commitment to advancing higher education through technology-driven solutions. The curriculum, which is tailored to meet the needs of a wide range of learners, covers all the fundamentals of power electronics and leaves participants with specific knowledge and abilities. This micro-credential program is designed to empower professionals and students, supporting their career objectives in the dynamic power electronics industry, by emphasizing accessibility, flexibility, and high-quality learning experiences

Comparative study for cursor detection at endoscopic images for telepointer

Communication over the internet is a common practice among computer users. A pointer is an essential tool for effective communication, pointing to a landmark or an intended object. Telepointer have become an important gadget for telemedicine to pinpoint the exact location of lesions, especially for endoscopic images. The endoscopic image will be displayed on the monitor at the surgeon's site, and the same view will be displayed at the remote expert site. However, the challenges for endoscopic images are the unconscious movement of the tissues in the endoscopic images, uniform texture, and varied illumination, which make it hard to keep track of the intended object. In this paper, a comparative study to detect the cursor over the endoscopic images was explored. RGB color space and HSV color space were used for comparative study. Experimental results revealed that HSV color space works well for cursor detection with an accuracy of 99.59%