Computer Engineering and Applications Journal
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101 research outputs found
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Drift-Resilient IoT Energy Monitoring for Low-Cost Voltage and Current Sensors
No full textLow-cost voltage and current sensors such as the ZMPT101B and ACS712 are widely used in IoT-based energy monitoring due to their affordability and ease of integration. However, their outputs suffer from drift caused by thermal variation, material degradation, and electromagnetic interference, leading to cumulative errors that compromise load monitoring, forecasting, and anomaly detection. This work presents a drift-resilient framework that integrates lightweight filtering and regression-based calibration into a unified pipeline deployable on ESP32-class devices. Moving average and adaptive Kalman filters suppress noise and track drift trends, regression models align sensor outputs with reference standards, and spectrogram-based analysis detects transient drift events for adaptive correction. Experiments under realistic conditions show substantial improvements: voltage RMSE decreased by over 90% (3.45V to 0.30V), current RMSE by 92% (0.065A to 0.005A), and MAPE to below 0.5%. Signal-to-noise ratio improved by approximately 21dB, confirming significant restoration of measurement fidelity. Compared with data-intensive deep learning or AutoML frameworks, the proposed method offers a scalable, interpretable, and resource-efficient solution for long-term IoT energy monitoring. By bridging drift mitigation strategies with the practical constraints of low-cost sensors, this framework enhances the reliability of smart grid and IoT-based infrastructures
Application of Additive Manufacturing Technology in Custom Surgery and Orthopedic Implants through 3D Bioprinting: Rapid Review
No full textMusculoskeletal disorders, which are the leading cause of global disability, require better implant reconstruction solutions, given the limitations of conventional implants in terms of anatomical fit, stability, and stress shielding risk. The objective of this rapid review is to summarize the latest evidence on the application of Additive Manufacturing (AM), 3D Printing, and 3D Bioprinting technologies in the manufacture of custom orthopedic implants. The method used was a Rapid Review with the PRISMA framework, which involved searching 3,291 articles in the PubMed and ScienceDirect databases and filtering them down to 14 selected articles. The results show that the integration of 3D imaging, 3D printing, and Artificial Intelligence (AI) significantly improves visual-spatial understanding in orthopedic education, as well as improves implant placement accuracy (e.g., in THA), reduces operating time, blood loss, and radiation exposure through the use of AI-based 3D preoperative planning, custom models, and 3D-printed surgical guides. However, challenges remain in terms of cost, preoperative production time, and lack of long-term follow-up data. In conclusion, 3D and AI technologies have revolutionized orthopedic practice by improving accuracy, efficiency, and personalization of therapy, requiring large-scale research and long-term evaluation for sustainable clinical implementation
Air Quality Monitoring System based on the TI MSP430 Microcontroller Family
No full textAir quality monitoring devices are generally sensor circuits coupled with signal processing devices, where the output signal provides intelligible information to users. In this project report, a low-cost portable air quality monitoring device based on the TI MSP430G2553 microcontroller is described and designed. The device can monitor the air quality in one’s immediate environment and hence it gives individuals an idea of how clean or polluted the air in their surroundings is. A design is presented which applies basic gas sensing techniques and analog-to-digital conversion (ADC) principles to achieve the needed functionality. The device is built with off-the-shelf components, which are easy to comprehend and assemble. The device can detect the presence of ammonia (NH3), nitrogen oxides (NOx), benzene (C6H6), Carbon dioxide (CO2), smoke, and other hazardous gases and it is powered by a dc supply voltage ranging between +7V and +12V
Detection of Ventricular Septal Defect in Pediatric Cardiac Ultrasound Videos Using Parasternal View and Faster R-CNN
No full textCongenital heart disease (CHD), particularly ventricular septal defect (VSD), remains a major contributor to pediatric morbidity, while echocardiographic diagnosis is highly dependent on operator expertise and image quality. This study examines the feasibility of an object-detection-based intelligent imaging framework for localizing VSD in pediatric cardiac ultrasound videos acquired from the parasternal long-axis view. Rather than proposing a novel detection algorithm, this work adopts a system-oriented approach by evaluating the Faster R-CNN framework under practical clinical constraints, including limited annotated data and heterogeneous ultrasound characteristics. Three convolutional neural network backbones such as ResNet50, ResNet101, and Inception-ResNet V2 are comparatively analyzed within a unified detection pipeline. Experimental results indicate that the ResNet101-based model achieves the highest localization performance at an intersection-over-union threshold of 0.5, while ResNet50 provides more consistent precision across stricter localization thresholds. Although false-positive detections are observed in acoustically challenging frames, the proposed framework maintains real-time feasibility at approximately 7–8 frames per second. The findings offer practical insights into accuracy–efficiency trade-offs and backbone selection for the development of clinically aware intelligent echocardiography systems, supporting the application of information and communication technology in pediatric cardiac imaging
Implementation of Color Matching in Ball Image Processing Using OpenCV
No full textThis research focuses on object detection through color matching, enabling machines to detect objects based on specific colors using OpenCV, a Python library widely used in computer vision projects. As a form of Machine Learning and Artificial Intelligence, this method allows machines to automatically learn and classify objects by simulating the human visual system. The study aims to enable a machine to detect and locate a ball through digital image processing using a webcam. The research method includes digital image processing, implementation on a Raspberry Pi, and testing on a robot, where logic is applied to guide the robot toward the ball by detecting its color. The outcome is an object detection system that identifies the ball’s position in two dimensions based on its specific color. In this case, the RGB code (164, 122, 0) and a minimum ball size of 10 radians were successfully implemented on the robot. However, the system has limitations under certain conditions. Future improvements will involve integrating TensorFlow for dataset processing and OpenCV for real-time object detection to achieve more accurate results.
Keywords: Artificial Intelligence, Color Matching, Computer Vision, OpenCV, Digital Image Processing. 
TeleOTIVA: Advanced AI-Powered Automated Screening System for Early Detection of Precancerous Lesions
No full textIn 2023, the Indonesian Ministry of Health launched the Rencana Aksi Nasional (RAN) to enhance the detection and management of cervical cancer in Indonesia. One of the main pillars in this movement is the implementation of early screening for precancerous lesions aimed at identifying and treating these lesions before they develop into cervical cancer. This effort includes improving public access to healthcare services, providing education and awareness about the importance of early detection, and utilizing the latest technology in screening procedures. It is hoped that, through these targeted and effective interventions, the incidence of cervical cancer can be significantly reduced. This research aims to facilitate the early detection screening process for cervical precancerous lesions, particularly in difficult areas for medical experts to reach. This study also seeks to assist obstetricians and gynecologists in detecting precancerous lesions automatically, quickly, and accurately. By developing an advanced technology-based screening system, it is hoped that early detection of precancerous lesions can be carried out more efficiently, thereby increasing the chances of timely treatment and reducing the incidence of cervical cancer across various regions in Indonesia. This system is designed to provide reliable and user-friendly diagnostic support as it is developed on a mobile platform that can be accessed anytime and anywhere. This research developed a system for early screening called TeleOTIVA. The TeleOTIVA application system is an advanced platform that uses artificial intelligence (AI) based approaches to provide optimal services in early detection of precancerous lesions. This application is designed for mobile, allowing users to access and use its advanced features anytime and anywhere. With the integration of AI technology, TeleOTIVA can detect and analyze cervical precancerous lesions accurately and quickly to provide accurate and efficient screening results. The TeleOTIVA application system is capable of providing satisfactory detection results. The performance of the proposed model achieves accuracy, sensitivity, and specificity levels above 90%. With this high performance, TeleOTIVA ensures that the detection of precancerous lesions is carried out with high reliability and precision, instilling greater confidence in healthcare professionals and users during the screening and diagnosis process. The implementation of our application model offers numerous advantages over traditional methods. It significantly enhances efficiency by automating processes, reduces human error through rigorous error-checking mechanisms, and accelerates the processing of large datasets. These improvements streamline operations and ensure more reliable and rapid data analysis
Cervical Pre-cancer Classification Using MLP Based on Hybrid Features from GLCM, LBP, and MobileNetV2
No full textThe early and accurate diagnosis of cervical intraepithelial neoplasia lesions (CIN), particularly in a resource-limited environment, is paramount in helping to control the rising epidemic of cervical cancer. This research offers a hybrid classification model that merge texture features like Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP), alongside semantic features from MobileNetV2. These features, after being extracted, are merged and supplied to a Multilayer Perceptron (MLP) for multiclass classification into Normal, CIN1, CIN2, or CIN3. The model was trained and evaluated using a 5-fold stratified cross-validation technique on an IARC dataset that contains 200 cases of colposcopy images. The experimental results illustrate that the model developed with a stratified k-fold cross-validation performed consistently well with high performance, average accuracy reported as 86.75% ± 2.62% and Cohen\u27s kappa 0.7963 ± 0.0524 showed substantial to almost perfect in agreement across folds. The best performance was recorded for Fold 4 achieving 90.31% accuracy, while maintaining robust F1-scores across all classes. This hybrid approach offers a promising direction for developing efficient and accurate computer-aided diagnosis (CAD) systems for cervical lesion classification
Deep Learning for ECG-Based Arrhythmia Classification Based on Time-Domain Features
No full textArrhythmia is a disturbance in the electrical activity of the heart that can affect the rhythm and duration of the heartbeat. Early detection of arrhythmia is crucial to prevent more serious complications. Electrocardiogram (ECG) is an effective non-invasive diagnostic tool in detecting arrhythmia, but manual detection by experts takes time. To overcome this limitation, this research develops an arrhythmia classification system by utilizing deep learning. This study involves a series of stages, starting from pre-processing, feature extraction, and arrhythmia classification models using convolutional neural networks (CNN) and long short-term memory (LSTM). The results showed that feature extraction successfully improved model efficiency and accuracy. Evaluation of model performance using accuracy, recall, precision, specificity, and F1-score metrics showed that the LSTM model achieved 95% accuracy, 96% recall, 96% precision, 99% specificity, and 96% F1-score, outperforming the CNN model which achieved 91% accuracy, 90% recall, 89% precision, 98% specificity, and 89% F1-score. Thus, these results indicate that the LSTM model is superior in arrhythmia classification
IoT-Enabled Real-Time Monitoring and Loss-of-Life Estimation of Distribution Transformers
No full textA distribution transformer is required in power distribution networks to step down the voltage relevant and usable for consumers. Its failure not only disrupts electricity supply but also incurs high replacement costs, with broader economic implications. Ensuring reliable operation, therefore, requires accurate and continuous monitoring of its performance. This paper presents IoT-Enabled Real-Time Monitoring and Loss-of-Life Estimation of Distribution Transformers developed and tested on a 10 kVA, 0.415 kV prototype distribution transformer, connected to three residential loads. A dedicated data acquisition system was developed, which monitors key parameters: load current, phase voltage, transformer oil level, ambient temperature, and oil temperature in real time over 14 days. An algorithm was implemented to analyze daily load profiles and hotspot temperature data, which were then used to estimate transformer loss of life. The results show that transformer ageing is highly sensitive to load variation. During weekdays, the cumulative equivalent ageing reached 2.22 hours per day, corresponding to a daily loss of life of 0.00296%. On weekends, higher residential loads increased cumulative ageing to 4.79 hours, with a corresponding life loss of 0.0063%. A simulated one-hour peak load of 1.43 pu resulted in 25.75 hours of ageing, translating to a life loss of 0.034%, demonstrating the severe impact of overloads. These findings emphasize that peak load periods dominate insulation ageing and can substantially reduce service life if unchecked
Analyzing Co-Authorship Networks in Indonesian PTN-BH Institution Through Social Network Analysis
No full textThis study involved an examination of bibliographic information from Indonesia. Our approach centered on utilizing social network analysis to explore the co-authorship relationships among Indonesian authors, focused on the co-authorship network within the context of authors affiliated with Indonesian state universities known as "PTN-BH," which specialize in higher education and legal studies. To conduct our analysis, we gathered publication data from the Scopus database, spanning a time frame from 1948 to 2020. The primary methodology entailed constructing a graph composed of nodes and edges, representing the co-authorship connections among these authors. By employing the Louvain method, we were able to identify prominent communities within this graph. We carried out a comprehensive analysis at both macro and micro levels, involving measurement techniques tailored to these perspectives. Through this approach, we revealed and examined the collaboration patterns among authors associated with PTN-BH institutions, as illuminated by the co-authorship network analysis