International Journal of Engineering and Manufacturing (IJEM)
ISSN: 2305-3631 (Print)
ISSN: 2306-5982 (Online)
DOI: https://doi.org/10.5815/ijem
Website: https://www.mecs-press.org/ijem
Published By: MECS Press
Frequency: 6 issues per year
Number(s) Available: 84
IJEM is committed to bridge the theory and practice of engineering and manufacturing. From innovative ideas to specific algorithms and full system implementations, IJEM publishes original, peer-reviewed, and high quality articles in the areas of engineering and manufacturing. IJEM is a well-indexed scholarly journal and is indispensable reading and references for people working at the cutting edge of engineering and manufacturing applications.
IJEM has been abstracted or indexed by several world class databases: Scopus, Google Scholar, Microsoft Academic Search, Baidu Wenku, Open Access Articles, Scirus, CNKI, CrossRef, JournalTOCs, etc..
IJEM Vol. 16, No. 2, Apr. 2026
REGULAR PAPERS
Identifying Optimal Wind Penetration for Agricultural Distribution Networks: Integrated Power Quality and Reliability Analysis
This research presents a systematic analysis framework for wind farm integration in scaled IEEE 33-bus agricultural distribution systems (×26.38, representing 98 MW peak agricultural load), focusing on comprehensive power quality, reliability, and economic assessment. The scaled system exhibits baseline voltage violations (24 buses) characteristic of extended radial distribution topologies serving concentrated agricultural loads, consistent with real utility system characteristics in Indonesian agricultural regions. Four wind penetration scenarios (0%, 15%, 30%, 45%) are evaluated using an integrated backward-forward sweep methodology coupling fundamental power flow with empirical harmonic calculations validated against full harmonic load flow analysis (±0.3% accuracy). The analysis employs realistic harmonic injection models for VFD-dominated agricultural loads and full-converter wind turbines, time-domain operational profiles capturing diurnal variations, and revised reliability modeling incorporating protection coordination constraints and battery energy storage system limitations. Results demonstrate that 30% penetration achieves optimal multi-objective performance validated through systematic sensitivity analysis across 10-45% penetration levels: active power loss reduction of 35.5% (210.9 kW to 136.1 kW), harmonic distortion mitigation up to 46.1% THD_V reduction at the wind connection point (1.52% to 0.82%), and realistic reliability improvements of 5.0% SAIDI reduction (8.52 to 8.09 hours/year) accounting for islanding effectiveness constraints (19% successful islanding events with proper battery energy storage and protection infrastructure). The 45% penetration scenario introduces voltage regulation challenges (V_max = 1.052 p.u.) that offset incremental benefits. Economic analysis reveals that system-wide benefits (loss reduction: $670,000/year) substantially exceed agricultural productivity gains ($4,800/year with realistic nonlinear yield models), positioning wind integration as primarily a power quality and efficiency enhancement with supplementary agricultural reliability benefits. Practical implementation requires comprehensive voltage regulation equipment ($2.1-2.5M investment), harmonic filters for VFD-dominated buses ($240,000), and advanced protection schemes for islanding operation ($450,000-$700,000). The integrated analysis framework provides utilities and agricultural operators with quantitative guidance for optimal distributed generation deployment, balancing technical performance, economic viability, and operational constraints.
[...] Read more.Beyond the grid, the Symbiosis of Virtual Power Plants and Communication Networks: A review
Virtual power plants (VPPs) are smart energy systems that aggregate geographically distributed energy resources (DERs), including renewable and non-renewable energy sources, energy storage devices, and controllable loads, into a single virtual plant. These plants are integrated into distribution networks to enhance grid stability and reliability. VPPs require a two-way communication framework to monitor and control all generation sources and loads, ensuring a balance between supply and demand while providing services to distribution or transmission network operators. In VPPs, various applications are used, each with specific communication quality requirements, such as reliability, latency, and bandwidth. Therefore, selecting communication technologies and protocols that meet these requirements and deliver optimal performance for each application is essential. This research paper presents a comprehensive review of the literature on VPPs, exploring their applications, communication requirements, and structural frameworks. It also examines the protocols and standards necessary to ensure reliability, security, and communication quality. Finally, the paper summarizes the global development and implementation of VPPs over the past two decades.
[...] Read more.Novel Approach to Create and Detect Invisible Cloak using Monocular Camera or Web Cam
The design of an invisible cloak has attracted attention owing to its potential use in espionage and military applications. Advances in computer vision and image processing have enabled the creation of invisible cloaks. This study presents the design and detection of an invisible cloak using a cost-effective monocular camera. The proposed algorithm uses the OpenCV library in Python to create and detect the cloak by analyzing individual pixels in video frames to identify areas with minimal or no change in pixel values. The approach relies on pixel-level analysis using Gaussian curves for detection. Experimental validation of self-created and publicly available datasets demonstrates the effectiveness of the method. Although the algorithm performs well under static environmental conditions, challenges remain in dynamic settings, which will be addressed in future work to improve robustness. This study contributes to the development of practical and affordable invisibility cloak technology and reliable detection methods to mitigate potential misuse.
[...] Read more.Influence of Temperature and Relative Humidity on Call Drop and Radio Resource Control in Lokoja Metropolis
This study examines the relationship betweenatmospheric factors [temperature and relative humidity] and network performance [call drop and Radio Resource Control] in Lokoja Metropolis. Monthly data collected from Globacom[GLO] office in Lokoja and Nigerian Meteorological Agency [NIMET] office in Lokoja over a one-year period reveals significant correlations between atmospheric variables [Temperature and relative humidity] and network performance [Call drop and Radio Resource Control]. We observed that call drop is directly proportional to temperature and relative humidity, while Radio Resource Control [RRC] has an inverse proportionality with temperature and relative humidity, assuming other meteorological variables are kept constant. Statistically, call drop showed a positive correlation of 0.35493 with temperature and 0.63769 with relative humidity, while RRC showed a positive correlation of 0.37289 with temperature and 0.5756 with relative humidity. Taken together, these findings indicated that increased temperature and humidity increased call drops and lower Radio Resource Control [RRC] success rates. This research has provided insights useful to the telecom operators and regulatory bodies to ensure network reliability, better resource allocation, environmental consideration and quality of service in tropical regions similar to Lokoja. Additionally, this research can also be useful in identifying key performance indicators, developing mitigation strategies, improving network maintenance and enhancing customer experience. Correlations were measured using Pearson correlation coefficients at a 5% significance level. These findings imply that network operators must account for atmospheric variability in optimizing reliability, resource allocation, and planning for mobile services in tropical climates. Despite being weak, this correlation is statistically significant and meaningful in real-world scenarios where multiple environmental and operational factors collectively influence call drops.
[...] Read more.A Novel Quantum-Based Authentication Method for IoT Devices using Quantum Physical Unclonable Functions
The Internet of Things (IoT) has ushered in significant advancements in networked technologies, yet it simultaneously raises concerns about the security and privacy of connected devices. Traditional authentication methods based on cryptographic protocols are increasingly vulnerable to attacks as the number of devices grows and attackers develop more sophisticated strategies. In this paper, we propose a novel quantum-based authentication method using Quantum Physical Unclonable Functions (QPUFs) to address the security challenges in IoT devices. Quantum PUFs exploit the inherent quantum properties of devices to generate unique, unclonable responses to challenges, providing a high level of security and resilience against attacks such as cloning, spoofing, and interception. We present the architecture of the proposed quantum authentication system, discuss the challenge-response protocol, and evaluate the system’s performance. Experimental results show that this approach offers strong security guarantees with minimal computational overhead. We Evaluate the Security, Scalability of our approach in simulated adverse IoT environment.
[...] Read more.Enhancing Underwater Object Detection through CNN-based Image Enhancement and Classification
This research focuses on object detection using Convolutional Neural Networks (CNN) applied to underwater image datasets. Underwater images often suffer from issues such as low clarity and quality, which pose challenges for accurate object identification. To address this, the research employs image enhancement techniques, including image illumination methods, to improve image quality and facilitate object detection algorithms. Subsequently, the study developed algorithms capable of detecting objects and accurately predicting their categories. The primary objective is to achieve optimal accuracy and efficiency in underwater recognition. This research utilizes Machine Learning techniques through Tensor Flow and Image Processing to accomplish underwater object detection. Deep learning techniques, particularly feature learning, object classification, and detection, have gained significant attention and momentum. In this research we implemented different image enhancement techniques on dataset and evaluated their performance. While one metric, IQI (Image Quality Index), slightly favoured histogram equalization (HE), the other three metrics strongly favoured the enhanced version of HE known as Contrast Limited Adaptive Histogram Equalization (CLAHE).
[...] Read more.Deep Learning-Based Recognition of Handwritten Devanagari Multilevel Compound Characters: A Performance Comparison
Handwritten character recognition is a crucial challenge in artificial intelligence and computer vision, particularly for complex scripts like Devanagari. Devanagari is widely used in Hindi, Marathi, and Nepali and consists of intricate multilevel compound characters, ligatures, and highly variable handwriting styles. Despite advances in optical character recognition (OCR) technology, accurately recognizing handwritten Devanagari characters remains difficult. This study compares various deep learning models, including Convolutional Neural Networks (CNN), CNN-SVM, Long Short-Term Memory (LSTM), EfficientNet, and a newly proposed attention-based CNN model. Extensive experiments were conducted on a diverse dataset containing simple and compound handwritten Devanagari characters. The proposed attention-based CNN model outperforms traditional methods, achieving a recognition accuracy of 96.5%, significantly higher than CNN (88.0%), CNN-SVM (88.5%), LSTM (92.0%), and EfficientNet (93.0%). The study employs advanced data augmentation techniques to enhance model robustness, making it adaptable to various handwriting styles. The attention mechanism in the proposed CNN model allows for improved feature extraction, leading to higher recognition accuracy and efficiency. This research contributes to developing robust OCR systems for the Devanagari script, enabling improved digitisation and preservation of Indian languages. The proposed approach can be extended to other complex scripts like Bengali and Tamil, further advancing multilingual OCR technologies, is now exploratory and has not been experimentally verified in this study. Future work can address the need for thorough cross-script evaluation and transfer learning studies to verify the adaptability of the attention-based CNN architecture, despite its inherent script-agnostic nature. The findings of this study hold significant implications for text digitization, historical document preservation, and automated language processing applications.
[...] Read more.A Federated Learning-based Hybrid compression Technique for 3D Medical Images
The use of multimedia communication has grown significantly in recent years, which has raised demand for image data compression. One popular technique for representing an image in an efficient format is image compression. It results in low rates of transmission by precisely lowering the number of bits required to store the images. Since medical image data is growing so quickly, there is a lot of research being done on how to upload and store large amounts of medical images in real time while having a limited amount of storage space and network bandwidth. But still, at this time, medical image compression technology is unable to optimize both rate and distortion. The goal of the proposed hybrid compression technique is to increase compression performance without losing the standard of the image. Even though they need a lot of storage, 3D medical images provide detailed information about disease. Optimal Multi-linear Singular Value Decomposition (OMLSVD) and deep auto-encoders are used in the current work to compress 3D healthcare images. The Federated Learning technique addresses the issues of data privacy and leakage by having each user train a model on its dataset before sending the model's local weights to a global federated server. So, use a federated server to get a global dataset weight without leaking or publishing datasets, protecting privacy. The quality of 3D compression images can be improved by using the proposed Hybrid approach. Experimental evaluation demonstrates SSIM values near 1 and high PSNR, indicating excellent reconstruction quality. Additionally, it compares the image with the compressed JPEG2000 and the proposed Hybrid approach. Since the images have different storage sizes, they all appear to be identical.
[...] Read more.Towards Automated Diagnosis of Heart Diseases: A Study of Machine Learning Techniques
The study applies machine learning algorithms to the diagnosis of heart disease. Data were collected from multiple sources in hospital and clinic records, along with time-based comparison other studies. The second part of the study, Clinical Decision Support, simulated the daily work of a physician and helped them make patient-centered medical decisions. The results revealed significant potential for machine learning to improve heart disease detection efficiency and accuracy, which could benefit future effective disease management and reduce patient burden. The study findings will enable future healthcare providers to harness new technology to achieve better prevention and superior care outcomes for heart disease screening. The study recommendations include optimal diagnostic skills and intervention-oriented preventive measures.
[...] Read more.Scalable AI-Driven Maize Plant Disease Detection using Convolutional Neural Network
Plant diseases have a significant impact on global food security, especially in staple crops like maize (Zea mays). Traditional disease detection systems depend on professional visual inspection, which is labor-intensive, time-consuming, and not scalable for large agricultural areas. Convolutional Neural Networks (CNNs) are used in this study's deep learning (DL) architecture to detect maize leaf diseases accurately and automatically. A curated dataset of approximately 7,000 high-resolution maize leaf photos was created, representing four classes: healthy, Common Rust (Puccinia sorghi), Northern Leaf Blight (Exserohilum turcicum), and Gray Leaf Spot (Cercospora zeae-maydis). Data were sourced from the Plant Village dataset, real-world field collections from Indian farms, and supplemented synthetically to simulate varied climatic circumstances. Advanced methods including as adaptive learning rate scheduling, gradient clipping, and significant data augmentation were used to train a bespoke CNN model that was improved by transfer learning with ResNet50 and VGG16 backbones. The model attained a test accuracy of 98.2%, beating classic machine learning algorithms like SVM (88.5%) and Random Forest (84.3%). Visualization approaches such as feature maps, Grad-CAM, and LIME improved interpretability and showed the model's capacity to locate disease-relevant features. Web-based user engagement is made possible by deployment-ready implementation, which enables farmers to upload leaf photos for immediate diagnosis. With the potential to cut maize crop losses by 20–30%, this research offers a scalable and affordable alternative to early disease detection in precision agriculture. Future research will investigate autonomous farm management with drone-based real-time surveillance and IoT system integration.
[...] Read more.Deep Learning Method for Early Detection of Lung Cancer using Deep Learning Algorithms
Lung cancer is responsible for many deaths from cancer around the globe, primarily because it is difficult to find malignant lung nodules early enough to be treatable. We developed a hybrid deep learning approach to the automated classification of lung nodules from chest computed tomography (CT) images. Our model uses convolutional neural networks (CNNs) for hierarchical feature extraction, an attention mechanism for feature refinement in targeted regions of interest, and a support vector machine (SVM) classifier for robust margin-based decision making. Furthermore, we use a patch-based learning strategy within the model to improve sensitivity to small and ambiguous lung nodules. The model is tested on the publicly available LIDC-IDRI dataset and achieves 94.2% accuracy, 95.1% recall, and an area under the receiver operating characteristic curve (AUC-ROC) score of 0.971, which outperforms multiple baseline deep learning methods. The proposed method provides a synergistic integration of attention-weighted feature enhancements and traditional machine learning classifications as compared to traditional end-to-end architectures, resulting in improved model generalization and interpretability. Grad-CAM visualizations are also used to provide qualitative insights into the model decision-making process. The proposed hybrid approach provides a novel and interpretable solution for the classification of lung nodules from CT images that may assist in the development of computerized systems to assist physicians in making diagnoses using medical images.
[...] Read more.A Mobile-Integrated Deep Learning Framework for Early Detection of Maize Diseases
Maize is a cornerstone of food security and economic stability in Nigeria, yet its production is severely hampered by crop diseases that cause significant yield losses and threaten the livelihoods of millions of smallholder farmers. Despite advances in machine learning (ML) and deep learning (DL) for plant disease detection, existing solutions often lack generalizability, scalability, and accessibility for resource-limited settings. This research used a robust, predictive system that leverages convolutional neural networks, specifically ResNet50 and EfficientNet trained on diverse, annotated datasets of maize leaf images. By integrating computer vision, transfer learning, and user-centric mobile application design, the system aimed to provide real-time, accurate diagnosis and actionable recommendations for disease management. This study compared the performance of the ResNet50 and the EfficientNet. At the end of the research, ResNet50 achieved marginally higher accuracy than EfficientNet under the same experimental conditions, although the performance difference is small and not statistically tested. The ResNet50 model was thereafter deployed into a scalable mobile application tool that can empower farmers and extension workers with early disease detection capabilities, potentially reducing crop losses, improving productivity, and enhancing food security across sub-Saharan Africa.
[...] Read more.Development and Validation of a Cloud-Based Road Surface Condition Monitoring Framework using Citizen-Sourced Smartphone Sensor Data in the Philippines
This study presents the development and validation of a mobile-to-cloud road surface condition monitoring system using citizen-sourced smartphone sensor data in the Philippines. The proposed framework integrates mobile data acquisition, local preprocessing, secure cloud transmission, supervised classification, and geospatial visualization within a unified architecture. Tri-axial accelerometer and gyroscope signals were collected at 50 Hz, segmented into overlapping windows, and transformed into statistical feature vectors prior to cloud-based inference. Field deployment was conducted across 48 urban and peri-urban road segments, generating 12,485 processed feature windows. A labeled subset of 4,200 windows was used for supervised evaluation. The classification model achieved an overall accuracy of 88.4%, with balanced precision and recall across smooth, moderate, and severe surface condition categories. Confusion matrix analysis showed that misclassifications were primarily concentrated between adjacent condition levels rather than between extreme classes. System-level evaluation demonstrated near-real-time responsiveness, with an average end-to-end latency of 1.8 seconds under stable network conditions. Offline buffering achieved 100% synchronization success following connectivity restoration, ensuring data integrity in environments with intermittent network coverage. The results indicate that smartphone-based vibration sensing, when integrated with cloud analytics and geospatial visualization, provides a scalable and cost-efficient approach for preliminary road surface monitoring. The proposed framework offers a practical complement to conventional inspection methods and supports data-driven infrastructure prioritization in developing urban contexts.
[...] Read more.A Systematic Approach for Heart Disease Analysis using Machine Learning Algorithms
The number of heart disease patients has significantly increased in recent years, and heart illness is linked to a high death rate. Furthermore, as technology advanced, several sophisticated devices were created to assist patients in measuring their health at home and estimating their risk of developing heart disease. Using six machine learning models, the study seeks to determine how accurate self-measured physical health indicators are at predicting heart disease when compared to all indicators assessed by medical professionals. Logistics Regression, K Nearest Neighbors, Support Vector Model, Decision Tree, Random Forest, and Gradient Boosting Classifier were among the six models employed to forecast heart disease. Twelve different test findings and 1189 patients' heart disease risks are included in the database utilized for the study. While the metrics contains six outcomes that could be tested, the all metrics contained all twelve test results. The accuracy score and false negative rate were calculated for each of the five models that were built for the all metrics.The findings demonstrated that in all five models, all metrics had greater accuracy scores than existing metrics. For five machine learning models, all metrics had false negative rates that were either lower or equivalent to that of existing metrics. The results showed that all physical indicators were more accurate in predicting patients' risk of heart disease than metrics measured physical health indicators. Therefore, all physical health indicators are preferable for assessing the risk for cardiac illnesses in the absence of future development of indicators.
[...] Read more.Optimizing Multiple Log-Periodic Array Antenna Design for Low Sidelobe and High-Directivity Performance: A Parametric Approach
Multiple Log-Periodic Array (MLPA) antennas have attracted significant research interest due to their broadband capability and high directivity. Their performance, however, depends critically on design parameters, often referred to as design factors, which directly influence the radiation characteristics. This paper presents an analytical evaluation of the influence of design factors on MLPA antenna performance, focusing on directivity, side lobe level (SLL), and beamwidth. The study employs the magnetic vector potential method to derive the array factor and investigates how variations in divergence spacing, scaling factor, and element length distribution affect the radiation properties. Analytical derivations are validated with numerical simulations to reveal the trade-offs between bandwidth enhancement and radiation stability. Results show that optimal tuning of design factors enhances radiation efficiency while minimizing undesirable sidelobe effects. The findings contribute to the optimization of MLPA antennas for applications in wireless communications, radar, and electronic warfare systems.
[...] Read more.Towards the Development a Cost-effective Earthquake Monitoring System and Vibration Detector with SMS Notification Using IOT
As one of the countries situated in the Pacific Ring of Fire, the Philippines suffers from an inexhaustible number of natural disasters every year. One of the most destructible ones is the occurrence of earthquakes. Because of the high damage that earthquakes incur, along with their inevitability and unpredictability, developing effective methods of earthquake damage mitigation as well as disaster preparedness is imperative to lessen the negative impacts it is capable of producing in communities. One efficient way of doing this is by implementing an earthquake early warning (EEW) system that is capable of sending message alerts to receivers to warn them in the event of a hazardous earthquake. With this objective, this study centers on creating an earthquake detector with SMS messaging to function as an EEW system with an added advantage of being low-cost to make it more accessible to the public. Using electronic components based on an Arduino Mega 2560 and a Global System for Mobile Communications (GSM) module, the earthquake detector and its alert message system were created. A series of tests in different locations across Butuan City was then performed to assess the device’s accuracy in measuring different Intensity levels when subjected to surface vibrations. Comparative analysis showed that its recorded values. Corresponded with the values obtained from accelerometer-based mobile applications. In conclusion, the study was deemed functional in its ability to detect low and high surface vibrations, which proves that it is successful in detecting earthquake tremors and vibrations in the event of an earthquake.
[...] Read more.Machine Learning Approaches for Cancer Detection
Accurate prediction of cancer can play a crucial role in its treatment. The procedure of cancer detection is incumbent upon the doctor, which at times can be subjected to human error and therefore leading to erroneous decisions. Using machine learning techniques for the same can prove to be beneficial. Many classification algorithms such as Support Vector Machines (SVM) and Artificial Neural Networks (ANN) are proven to produce good classification accuracies. The following study models data sets for breast, liver, ovarian and prostate cancer using the aforementioned algorithms and compares them. The study covers data from condition of organs, which is called standard data and from gene expression data as well. This research has shown that SVM classifier can obtain better performance for classification in comparison to the ANN classifier.
[...] Read more.Big Data in Cyber-Physical Systems, Digital Manufacturing and Industry 4.0
A cyber physical system (CPS) is a complex system that integrates computation, communication, and physical processes. Digital manufacturing is a method of using computers and related technologies to control an entire production process. Industry 4.0 can make manufacturing more efficient, flexible, and sustainable through communication and intelligence; therefore, it can increase the competitiveness. Key technologies such as the Internet of Things, cloud computing, machine-to-machine (M2M) communications, 3D printing, and Big Data have great impacts on Industry 4.0. Big Data analytics is very important for cyber-physical systems (CPSs), digital manufacturing, and Industry 4.0. This paper introduces technology progresses in CPS, digital manufacturing, and Industry 4.0. Some challenges and future research topics in these areas are also presented.
[...] Read more.Gas Leakage Detector and Monitoring System
Leakage of gas is a major issue in the industrial sector, residential buildings, and gas-powered vehicles, one of the preventive methods to stop accidents associated with gas leakage is to install gas leakage detection devices. The focus of this work is to propose a device that can detect gas leakage and alert the owners to avert problems due to gas leakages. The system is based on a microcontroller that employs a gas sensor as well as a GSM module, an LCD display, and a buzzer. The system was designed for gas leakage monitoring and alerts with SMS via an Arduino microcontroller with a buzzer and an MQ2 gas sensor. The circuit contains a Microcontroller MQ2 gas sensor, buzzer, LCD display, and GSM module, when the sensor detects gas leakage it transmit the information to the Microcontroller while the microcontroller makes a decision and then forwarded a warning message to the user as SMS to a mobile phone for decision to be taken accordingly. The output of this research will be significant in averting problems associated with gas leakages now and in future.
[...] Read more.Development of a Low-Cost Air Quality Data Acquisition IoT-based System using Arduino Leonardo
Air pollution is responsible for an estimated 5.5 million deaths in 2013 which costed the global economy approximately US$225 billion in lost labor income. To address the problems caused by air pollution, this study aims to develop a low-cost and portable air quality monitoring system that detects the levels of CO, PM2.5, PM10, temperature, and humidity. Using Internet of Things (IoT), the data that the system gathers can be accessed through the internet. Moreover, the system assesses the obtained data through a comparative analysis with the AQI. The Iterative Design Loop method was used in the development of the air quality monitoring system. Furthermore, the sensors were programmed using the Arduino Integrated Development Environment (IDE). Using the Welch’s t-test, it was found that the obtained data of the system is not significantly different to that of the standard air quality monitoring systems. To achieve more accurate data from the developed system, the raw data of the developed and standard system were calibrated through an equation from the trendline. Through the use of Acer CloudProfessor, the study successfully developed an air quality monitoring system that can be accessed through the internet.
[...] Read more.Automatic plant Irrigation Control System Using Arduino and GSM Module
The evolving information technology abridges the hardship in the daily life of consumers all over the world, hence the application of this knowledge in the irrigation field is necessary nowadays. The exponential growth of demand in food is due to the ever-evolving population of the world, thus it becomes necessary to expand the present area of cultivation. Considering the present situation of weather change due to global warming as a result of industrial activities, farming via irrigation is the reliable process of food production. Water remains the only source for survival for crop production, thus optimal management and proper use of water become pertinent with the ever-increasing land for irrigation. Arduino based automatic plant irrigation control system; provides a simple approach to automated irrigation. This work makes use of the GSM module for the notification of the user about the situation in the farm, this project aims to design and implement an automatic plant irrigation control system using Arduino and GSM module. In this proposed system, there are two main parts hardware and software units. Mechanical units which are the hardware unit comprises of instrumentation systems and watering irrigation systems. The equipment system is based on microcontroller, flow meter, moisture sensor, LCD, and GSM module. The software part comprises of C++ code, this is to enable the linkage between various modules. The main control of this system is the microcontroller unit that serves as the brain for coordinating control for various modules of the system, it synchronizes and operates the watering system and notifies the user about the condition of the field and watering section via GSM module. Implementation of this project will significantly help in a water-saving of about 30 – 50% as compared to the conventional watering system like the sprinkler, improve growth and discourage weeds because water will only be served to the needed area, simple method and timer-based system for automatic watering can be incorporated for efficiency.
[...] Read more.Smart Vehicle Accident Prevention and Road Safety System with Real Time Data Acquisition
Smart Vehicle Accident Prevention System is an innovative solution aimed at enhancing road safety and reducing the occurrence of accidents. Leveraging the Internet of Things (IoT) technology, this system combines real-time data acquisition, analysis, and intelligent decision-making algorithms to provide an effective accident prevention mechanism. The Vehicle Accident Prevention System is a com-prehensive project that aims to enhance road safety by utilizing Arduino microcontrollers and various sensors, including an alcohol sensor, temperature sensor, IR sensor and ultrasonic sensor. This report provides a detailed overview of the system’s design, implementation, and functionality.
[...] Read more.Fully Automated Hydroponics System for Smart Farming
This project is focused on developing a Fully Automatic Hydroponics system which helps in monitoring and controlling temperature, Humidity, pH and EC in Hydroponics. Hydroponics is a method of growing crops without soil. Plants are grown in rows or on trellises, just like in a traditional garden, but they have their roots in water rather than in dirt. Although, there are different ways in which hydroponics can be implemented, there is no individual system which can measure and control pH and EC level of nutrient solution along with its surrounding temperature and humidity automatically. We use PIC16F877A microcontroller and four pumps, three of which are used to pump water, nutrient solution, pH solution and the fourth pump is used to control the humidity. A fan is used to control the temperature which increases its speed as the temperature increases. The pumps are turned on depending on the EC and pH values obtained from the electrodes. A passive LCD display is used to display variations in the values. Different Analysis like water usage, plant growth in comparison with regular farming method and hydroponics is successfully completed which results in hydroponics system is significant method in comparison with soiled cultivation method in terms of yield and water usage. This project is expected to produce high yield crops by taking minimal space, makes work easier for farmers in growing of plants, and also consumes less amount of water when compared to traditional method resulting in conservation of water.
[...] Read more.Reliability Analysis Techniques in Distribution System: A Comprehensive Review
Quality of electricity with continuity is the reliability of the power system which is inversely proportional with the duration of power supply interruption. It depends on some expected or unexpected faults/failures on the systems, speed of protecting systems, preventive maintenance, and motivation of technical staffs. The detailed study of the distribution system is more crucial as its reliability is the concern of utility’s fame, service, customers’ satisfactions and reflects to the overall revenue. The relevant articles from the various sources has been collected and analyzed different reliability indices with their significance. Also, to realize the methodology related with reliability analysis, a comparative study among its different components has been carried out and the best techniques for maintaining system reliability are suggested.
[...] Read more.Mapping Sabkha Land Surfaces in the United Arab Emirates (UAE) using Landsat 8 Data, Principal Component Analysis and Soil Salinity Information
Sabkha is an Arabic word for a salt-flat area found mainly along arid area coastlines and inlands within sand dunes areas. The sabkha that form within the sand are relatively flat and very saline areas of sand or silt that forms just above the water-table where the sand is cemented together by evaporite salts from seasonal ponds. Such shallow water is normally highly saline. Here the crust is rich in gypsum and halite veins where the underline thin layer is made of sand and silt. Such sabkha have an average thickness of a meter or slightly less. On the other hand, marine sabkha represent transitional environments between the land and the sea. The UAE is home to some of the largest concentrations of sabkha both coastal and inland. The coastal areas of Abu Dhabi include several small shoals, islands, protected lagoons, channels and deltas, an inner zone of intertidal flats with algal mats and broad areas of supratidal sabkha salt flats.
Identifying sabkha habitats from remotely sensed data is a challenging process. Traditional classification techniques of multispectral data alone, usually fail to properly identify sabkha pixels or provide lower rates of mapping accuracy for sabkha habitats. The primary objective of this research is to develop a much more accurate methodology for properly mapping and identifying sabkha areas from remotely sensed data. Properly mapping sabkha habitats from remotely sensed data is the first steps towards studying the ecological changes within such habitats using earth observation techniques. Furthermore, sabkha habitats can in certain situations be a geotechnical hazard due to its highly salinity and with adverse effects on concrete, asphalt, steel and other structures, in addition to their sporadic heaves and collapses. As the UAE continue to build major infrastructure and development projects identifying the location of such habitats is vitally important.
In this research a new technique that combines the multispectral information of Landsat 8, principal component analysis and spectral soil salinity detection is developed. The study area is located in the western part of the UAE along the border with the Kingdom of Saudi Arabia, an area known to include large tracks of inland and coastal sabkha. Landsat 8 data from path 161 and row 43 was acquired for the study. A multi-source classification approach was followed that utilizes the multispectral data of Landsat 8 along with components from the principal components analysis and the spectral salinity index maps. The preliminary results confirmed by field observations show that the combined data improved the classification accuracy to almost 90% in comparison to multispectral data alone of 78%.
Machine Learning Approaches for Cancer Detection
Accurate prediction of cancer can play a crucial role in its treatment. The procedure of cancer detection is incumbent upon the doctor, which at times can be subjected to human error and therefore leading to erroneous decisions. Using machine learning techniques for the same can prove to be beneficial. Many classification algorithms such as Support Vector Machines (SVM) and Artificial Neural Networks (ANN) are proven to produce good classification accuracies. The following study models data sets for breast, liver, ovarian and prostate cancer using the aforementioned algorithms and compares them. The study covers data from condition of organs, which is called standard data and from gene expression data as well. This research has shown that SVM classifier can obtain better performance for classification in comparison to the ANN classifier.
[...] Read more.Towards the Development a Cost-effective Earthquake Monitoring System and Vibration Detector with SMS Notification Using IOT
As one of the countries situated in the Pacific Ring of Fire, the Philippines suffers from an inexhaustible number of natural disasters every year. One of the most destructible ones is the occurrence of earthquakes. Because of the high damage that earthquakes incur, along with their inevitability and unpredictability, developing effective methods of earthquake damage mitigation as well as disaster preparedness is imperative to lessen the negative impacts it is capable of producing in communities. One efficient way of doing this is by implementing an earthquake early warning (EEW) system that is capable of sending message alerts to receivers to warn them in the event of a hazardous earthquake. With this objective, this study centers on creating an earthquake detector with SMS messaging to function as an EEW system with an added advantage of being low-cost to make it more accessible to the public. Using electronic components based on an Arduino Mega 2560 and a Global System for Mobile Communications (GSM) module, the earthquake detector and its alert message system were created. A series of tests in different locations across Butuan City was then performed to assess the device’s accuracy in measuring different Intensity levels when subjected to surface vibrations. Comparative analysis showed that its recorded values. Corresponded with the values obtained from accelerometer-based mobile applications. In conclusion, the study was deemed functional in its ability to detect low and high surface vibrations, which proves that it is successful in detecting earthquake tremors and vibrations in the event of an earthquake.
[...] Read more.Development of a Low-Cost Air Quality Data Acquisition IoT-based System using Arduino Leonardo
Air pollution is responsible for an estimated 5.5 million deaths in 2013 which costed the global economy approximately US$225 billion in lost labor income. To address the problems caused by air pollution, this study aims to develop a low-cost and portable air quality monitoring system that detects the levels of CO, PM2.5, PM10, temperature, and humidity. Using Internet of Things (IoT), the data that the system gathers can be accessed through the internet. Moreover, the system assesses the obtained data through a comparative analysis with the AQI. The Iterative Design Loop method was used in the development of the air quality monitoring system. Furthermore, the sensors were programmed using the Arduino Integrated Development Environment (IDE). Using the Welch’s t-test, it was found that the obtained data of the system is not significantly different to that of the standard air quality monitoring systems. To achieve more accurate data from the developed system, the raw data of the developed and standard system were calibrated through an equation from the trendline. Through the use of Acer CloudProfessor, the study successfully developed an air quality monitoring system that can be accessed through the internet.
[...] Read more.Automated Wall Painting Robot for Mixing Colors based on Mobile Application
The final stage, which is the building paint or the adopted design, is where most real estate developers and constructors struggle. Where extensive painting is required, which takes a lot of time, effort, and accuracy from the firm doing the work. Additionally, it might be challenging to decide on the precise color grades for the design and calculate the right amount of paint to use for the job. Where these activities are extremely expensive, and the complex implementation is accompanied by worries and skepticism. These are the motivations behind the development of painting machines that blend colors. Artificial intelligence is used in the machine's design to make it efficient and quick at what it does. High accuracy is needed when selecting the proper colors, and this machine is distinguished by its ability to select the proper color tone. The color sensor (TCS34725 RGB) determines the relevance and accuracy of the desired color by comparison with the system database with the assistance of the light sensor (STM32), which measures the degree of illumination of the chosen place. By combining basic colors, this technique saves the customer the hassle of looking at specialized stores for the level of color they require. By giving the system the codes assigned to each color, it may also blend colors. The system also has the feature of controlling the machine remotely via smart phone application by enabling bluetooth and wifi features.
[...] Read more.Gas Leakage Detector and Monitoring System
Leakage of gas is a major issue in the industrial sector, residential buildings, and gas-powered vehicles, one of the preventive methods to stop accidents associated with gas leakage is to install gas leakage detection devices. The focus of this work is to propose a device that can detect gas leakage and alert the owners to avert problems due to gas leakages. The system is based on a microcontroller that employs a gas sensor as well as a GSM module, an LCD display, and a buzzer. The system was designed for gas leakage monitoring and alerts with SMS via an Arduino microcontroller with a buzzer and an MQ2 gas sensor. The circuit contains a Microcontroller MQ2 gas sensor, buzzer, LCD display, and GSM module, when the sensor detects gas leakage it transmit the information to the Microcontroller while the microcontroller makes a decision and then forwarded a warning message to the user as SMS to a mobile phone for decision to be taken accordingly. The output of this research will be significant in averting problems associated with gas leakages now and in future.
[...] Read more.Reliability Analysis Techniques in Distribution System: A Comprehensive Review
Quality of electricity with continuity is the reliability of the power system which is inversely proportional with the duration of power supply interruption. It depends on some expected or unexpected faults/failures on the systems, speed of protecting systems, preventive maintenance, and motivation of technical staffs. The detailed study of the distribution system is more crucial as its reliability is the concern of utility’s fame, service, customers’ satisfactions and reflects to the overall revenue. The relevant articles from the various sources has been collected and analyzed different reliability indices with their significance. Also, to realize the methodology related with reliability analysis, a comparative study among its different components has been carried out and the best techniques for maintaining system reliability are suggested.
[...] Read more.PID Temperature Controller Design for Shell and Tube Heat Exchanger
Heat exchangers are one of the most important thermal devices. Shell and tube heat exchangers are the common types of heat exchangers and sustained a wide range of operating temperature and pressure. Modeling and controlling heat exchanger system is a difficult assignment because of its nonlinearity. As the flow rates changes, the gain, time delay and time constant varies, hence causing system nonlinearity. The solution for such problems is finding acceptable mathematical model and design a controller which provides better performance indices. In this paper mathematical model (experimental or empirical based) to represent the real system and design suitable controller which remove the offset and settle fast with minimum steady state error has been proposed. To this end, system model design the Proportional-Integral-Derivative controller for shell and tube heat exchanger using Ziegler Nichols method, Cohen-coon method and Chein et al. method. Since two opposing dynamic effects are existing in the system and has a problem of dynamics of inverse response and large overshoot. Therefore, Chein et al. tuning method have better performance than that of the others. In case of Chein et al. the overshoot of 2.577 % and settling time of 63.1 s.
[...] Read more.Neural Networks-based Process Model and its Integration with Conventional Drum Level PID Control in a Steam Boiler Plant
Controlling drum level is a major and crucial control objective in thermal power plant steam boilers. The drum level as a controlled variable is highly characterized by complex non-linear process dynamics as well as measurement noise and long-time delays. Developing a data-driven process model is particularly advantageous as it could be built from ongoing operational data. Such a model could be used to assist existing controllers by providing predictions regarding the drum level. The aim of this paper is to develop such a model and to propose a control architecture that can be easily integrated into existing control hardware. For that purpose, different neural networks are used, Multilayer Perceptron (MLP), Nonlinear Autoregressive Exogenous (NARX), and Long Short Term (LSTM) neural networks. LSTM and MLP were able to capture the dynamics of the process, but LSTM showed superior performance. The results demonstrate that the use of traditional machine learning criteria to evaluate a process model is not necessarily adequate. Using the model in an open-loop and a closed-loop simulation is more suitable to test its ability to capture the dynamics of the process. A novel architecture that integrates the process model within an existing closed-loop controller is proposed. The architecture uses adaptive weights to ensure that a good model is given more influence than a bad model on the controller’s output.
[...] Read more.Fully Automated Hydroponics System for Smart Farming
This project is focused on developing a Fully Automatic Hydroponics system which helps in monitoring and controlling temperature, Humidity, pH and EC in Hydroponics. Hydroponics is a method of growing crops without soil. Plants are grown in rows or on trellises, just like in a traditional garden, but they have their roots in water rather than in dirt. Although, there are different ways in which hydroponics can be implemented, there is no individual system which can measure and control pH and EC level of nutrient solution along with its surrounding temperature and humidity automatically. We use PIC16F877A microcontroller and four pumps, three of which are used to pump water, nutrient solution, pH solution and the fourth pump is used to control the humidity. A fan is used to control the temperature which increases its speed as the temperature increases. The pumps are turned on depending on the EC and pH values obtained from the electrodes. A passive LCD display is used to display variations in the values. Different Analysis like water usage, plant growth in comparison with regular farming method and hydroponics is successfully completed which results in hydroponics system is significant method in comparison with soiled cultivation method in terms of yield and water usage. This project is expected to produce high yield crops by taking minimal space, makes work easier for farmers in growing of plants, and also consumes less amount of water when compared to traditional method resulting in conservation of water.
[...] Read more.Automatic plant Irrigation Control System Using Arduino and GSM Module
The evolving information technology abridges the hardship in the daily life of consumers all over the world, hence the application of this knowledge in the irrigation field is necessary nowadays. The exponential growth of demand in food is due to the ever-evolving population of the world, thus it becomes necessary to expand the present area of cultivation. Considering the present situation of weather change due to global warming as a result of industrial activities, farming via irrigation is the reliable process of food production. Water remains the only source for survival for crop production, thus optimal management and proper use of water become pertinent with the ever-increasing land for irrigation. Arduino based automatic plant irrigation control system; provides a simple approach to automated irrigation. This work makes use of the GSM module for the notification of the user about the situation in the farm, this project aims to design and implement an automatic plant irrigation control system using Arduino and GSM module. In this proposed system, there are two main parts hardware and software units. Mechanical units which are the hardware unit comprises of instrumentation systems and watering irrigation systems. The equipment system is based on microcontroller, flow meter, moisture sensor, LCD, and GSM module. The software part comprises of C++ code, this is to enable the linkage between various modules. The main control of this system is the microcontroller unit that serves as the brain for coordinating control for various modules of the system, it synchronizes and operates the watering system and notifies the user about the condition of the field and watering section via GSM module. Implementation of this project will significantly help in a water-saving of about 30 – 50% as compared to the conventional watering system like the sprinkler, improve growth and discourage weeds because water will only be served to the needed area, simple method and timer-based system for automatic watering can be incorporated for efficiency.
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