Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
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A Compact Violin-Shaped Monopole Antenna for Ultra-Wideband Applications
Full text linkA case study of a miniature monopole planar fiddle or violin-shaped antenna that can be used in ultra-wideband (UWB) application is carried out. Such violin-shaped antenna is a circular patch accompanied with two circular cuts and overlapped with an elliptical patch on the top of it. It is small in size, simple in structure, feasible to construct and experimentally feasible to be manufactured and validated in lab. Furthermore, the handle of the fiddlelike structure serves as a microstrip 50 feeding line connected to the main patch structure body. However, the prototype of the designed antenna is manufactured on a substrate of a dielectric material of FR4 with a dielectric constant that equals 4.3 with dimensions of 28×18×1.6 mm3. The gain at the resonant frequencies reached different values throughout the covered frequency band; that is of (3.1 GHz up to 13.5 GHz) ranging between the values of (≈1.1 dBi up to ≈5.5 dBi) according to the return loss of the performance outcome. The empirically measured and simulated results have a suitable settlement and/or agreement and computations display that the antenna has a respectable frequency band, radiation, and characteristics of time domain in spite of the antenna’s small size and simple design
The Analytical Approach to Evaluate the Bit Error Rate Performance of PLC System in Presence of Cyclostationary Non-White Gaussian Noise
Full text linkIn a Powerline Communication (PLC) system, improper connections of associated hardwires can lead to the generation of unwanted RF signals, overriding the transmitted signal and producing undesired RF spurious signals. Noise in powerlines also arises from the corona effect, voltage impulses, and arcs occurring in transmission and distribution lines, significantly compromising the integrity of the PLC network. Analysis indicates that powerline noise exhibits a non-white cyclostationary characteristic. Due to its severity, PLC noise is categorized primarily as background noise and impulsive noise. This paper evaluates the characteristics of a powerline network under severe noisy conditions, particularly focusing on Cyclostationary Non-White Additive Gaussian Noise (CNWAGN) across broadband and narrow frequency communication channels. Accordingly, an analytical model is developed to specifically examine the bit error rate (BER) in environments affected by non-white additive Gaussian noise. BER and receiver sensitivity are also assessed for various bit rates using MATLAB simulations, demonstrating performance in terms of BER. This analytical model provides a straightforward method to evaluate results across different bit error rates in frequency-dependent and independent scenarios, surpassing traditional approaches. It proves highly effective in assessing Powerline Communication System performance, with analytically derived outcomes closely aligning with simulation results
Joint encryption and error correction schemes: A survey
Full text linkIn this recent era, the sharing of critical information is essential. Along with information security, error-free data transmission is equally important. Crypto-coding is the method combining encryption algorithms and error correcting codes to enhance performance in terms of security, time, resources, or complexity. Despite the significant research, a comprehensive systematic literature survey that explores the status of research is not available. The proposed study fills this gap by exploring the available research in detail and highlighting past contributions, allowing readers and upcoming researchers to have a detailed understanding of various software and hardware implementations of crypto-coding algorithms and their results. This paper presents a comparison of encryption methods based on various parameters. The crypto-coding research work in satellite communication is also added. The survey includes implementation methods, results, applications, and comparisons of previous work results. This systematic literature survey also covers open issues and future trends of solutions in this context. The paper will offer directions for potential research in the area of crypto-coding and will expand the research frame for upcoming scholarly research
To Enhance the Operational Planning of an Independent Microgrid Using a Novel Combination of Demand Response Programs
Full text linkProviding electricity in rural or isolated areas involves high capital costs due to the cost of constructing transmission and distribution facilities. An independent Microgrid consisting of distributed generations (including both renewable and non-renewable energy sources) near the load could be an effective alternative. However, the unpredictability of renewable energy sources like wind and solar creates a problem in Microgrid operation, as there are instances when generation may not be enough to satisfy peak demand. Energy storage technology is generally employed to address this uncertainty. The Demand Response Program (DRP) is another technique that makes the Microgrid operation reliable and safe by lowering peak demand and switching it to low-load periods. This article addresses the short-term Unit Commitment Economic Dispatch (UCED) problem for an Independent Microgrid to reduce the overall operating costs using various DRPs. This paper presents a novel combination of DRP to enhance Microgrid’s operation and financial effectiveness and benefit its users. DRP modeling is done based on price elasticity and consumer benefit models. Mixed-integer nonlinear programming (MINLP) is used to formulate and solve the UCED problem in the GAMS software. 11-Bus Microgrid is considered for demonstration. According to the optimization results, implementation of TOU-RTP-CPPDLC DRPs reduces the operating cost by 13.68%, 13.31%, 17.16%, and 8.41%, respectively, with reduced load shedding. Consumers get benefits only in DLC-DRP. The proposed TOU+DLC-DRP combination reduces the operating costs by 13.48% with increased consumer benefits compared to DLC-DRP alone. Therefore, the proposed method is profitable for both the Microgrid operator and its users
Design of Service Oriented Architecture for an IoT Healthcare Management System
Full text linkHealthcare services maturities are increasing dramatically over the last decade towards better patient anomaly detection, early diagnosis, and more accuracy in manipulation. The applications of IoT in healthcare are becoming more popular day after day with a good focus on the autonomy of detection and decision-making of patient’s vital data during admission phases, which could enable an envisioned environment for the right decisions on time. This paper focuses on developing a framework of architecture, protocols, and algorithms for IoT Healthcare system aimed at increasing the efficiency of systems operation and enhancing the reachability of different types of devices in the same patient or across several patients. The proposed architecture ensures that each individual device is autonomous and can work independently with the surrounding environment. The study includes as well as proof of concept pilot with a-capability to measure the patient’s vital information on a non-invasive basis, such as the pulse sensor unit, room temperature, and the display out device. The concept is validated, proving that devices can communicate together optimally, reliably, intelligently and autonomously in the same patient or across patient categories according to the status of patients without human intrusion
Ransomware Detection Using Stacked Autoencoder for Feature Selection
Full text linkIn response to the escalating malware threats, we propose an advanced ransomware detection and classification method. Our approach combines a stacked autoencoder for precise feature selection with a Long Short-Term Memory classifier which significantly enhances ransomware stratification accuracy. The process involves thorough preprocessing of the UGRansome dataset, training an unsupervised stacked autoencoder for optimal feature selection, and fine-tuning via supervised learning to elevate the Long Short-Term Memory model's classification capabilities. We meticulously analysed the autoencoder's learned weights and activations to pinpoint essential features for distinguishing 17 ransomware families from other malware and created a streamlined feature set for precise classification. Our results demonstrate the exceptional performance of the stacked autoencoder-based Long Short-Term Memory model across the 17 ransomware families. This model exhibits high precision, recall, and F1 score values. Furthermore, balanced average scores affirm its ability to generalize effectively across various malware types. To optimise the proposed model, we conducted extensive experiments, including up to 400 epochs, and varying learning rates and achieved an exceptional 98.5% accuracy in ransomware classification. These results surpass traditional machine learning classifiers. Moreover, the proposed model surpasses the Extreme Gradient Boosting (XGBoost) algorithm, primarily due to its effective stacked autoencoder feature selection mechanism and demonstrates outstanding performance in identifying signature attacks with a 98.5% accuracy rate. This result outperforms the XGBoost model, which achieved a 95.5% accuracy rate in the same task. In addition, a prediction of the ransomware financial impact using the proposed model reveals that while Locky, SamSam, and WannaCry still incur substantial cumulative costs, their attacks may not be as financially damaging as those of NoobCrypt, DMALocker, and EDA2
Forest fire risk monitoring using fuzzy logic and IoT technology
Full text linkForest fire is one of the leading causes of ecological damage and environmental problems. This work aims to develop a forest fire risk monitoring system in which an artificial intelligence technique, fuzzy logic, has been used to determine the forest method risk (temperature, relative humidity, and wind speed). Fuzzy set theory implements categories or groupings of data whose boundaries are not clearly defined (i.e. fuzzy), consisting of rule bases, membership functions, and inference methods. We also use wireless sensor networks (WSN) and Internet of Things (IoT) technologies. In order to collect environmental information through WSN based environmental sensors, the collected information is transmitted to a database on a server through an Internet connection. Users can monitor the saved data using an internet browser in each whey. This provides the ability to analyze detailed data and then take the necessary precautions to protect threatened forests
An Approach for Improving Accuracy and Optimizing Resource Usage for Violence Detection in Surveillance Cameras in IoT systems
Full text linkSmart farming that uses information and communication technology is developed as a critical technique to address the challenges related to agricultural production, environmental effects (climate change), food security, and supply chain. The recent statistics reveal that the world's population has been increased significantly, which is expected to reach 7.7 billion. It is essential to achieve a significant rise in food output to meet the requirement of such a massive growth of population. However, due to the natural conditions and a variety of plant illnesses, food productivity and farms are reduced. In order to diagnose food diseases in farming, new technologies like the Internet of Things and artificial intelligence are now essential. To this end, the research paper introduces a novel artificial intelligence model represented by a twelvelayer deep convolution neural network to identify and classify plant image diseases. 38 distinct types of plant leaf photos are used for training and testing the suggested model, which are obtained by adjusting different parameters such as (a) hyperparameters; (b) coevolutionary layers; (c) and pooling layers in number. The proposed model consists of an extractor and classifier of functions. The first section involves three phases, i.e., it consists of two convolution layers and a maximum pooling layer for each phase. The second section consists of three levels: flattening, hidden, and output layers. The proposed model is compared with LeNet, VGG16, AlexNet, and Inception v3, which are considered state-of-the-art pre-trained models. The results demonstrate that the accuracy of LeNet, VGG16, AlexNet, and Inception v3 is given as 89%, 93%, 96.11%, and 97.6%, respectively. The findings provided in this research show that the suggested model outperforms state-of-the-art models in terms of training speed and computing time. Also, the results show that the proposed model achieves a considerable improvement in terms of accuracy and the mean square error compared to the state-of-the-art methods. In particular, The outcomes demonstrate that the suggested model achieves a mean square error and prediction accuracy of 98.76% and 0.0580, respectively. The results also depict that the proposed model is more reliable, allows fast convergence time in obtaining the results, and requires only a small number of trained parameters to identify the plant diseases accurately
Webcam Based Robust and Affordable Optical Mark Recognition System for Teachers
Full text linkThe growing need for efficient automated grading solutions has driven advancements in optical mark recognition (OMR) systems for multiple-choice assessments. This paper introduces a novel webcam-based OMR system that employs advanced image processing and computer vision techniques to eliminate the dependency on specialized hardware. The proposed system enhances image quality, extracts relevant data, and accurately processes marked responses through a robust pipeline of preprocessing, segmentation, and recognition algorithms. Addressing challenges such as inconsistent handwriting styles and varying lighting conditions, the system demonstrates high accuracy and reliability, achieving an impressive accuracy rate of 100%. Experimental validation highlights significant improvements in grading efficiency, reduced human error, and enhanced consistency when compared to manual grading methods. The scalability of the system makes it applicable to remote learning environments, online exams, and large-scale assessment scenarios. Future research directions include integrating machine learning techniques to extend the system’s capabilities to subjective assessments and potential collaborations with educational institutions and online platforms. This research contributes to the field by providing an accessible and scalable automated grading solution that optimizes assessment workflows and improves the educational experience
Passive Intermodulation Cancellation in 5G Systems Using Artificial Neural Networks
Full text linkPassive intermodulation (PIM) has been a serious challenge in 5G Frequency Division Duplexing (FDD) carrier aggregated (CA) wireless systems leading to the degradation of system performance. Digital cancellation techniques have been used to dynamically cancel the time-varying PIM resulting from passive nonlinearities. These techniques are usually based on Volterra-like behavioral models which are very complex and hard to implement. In this paper, a Feedforward Neural Network (FFNN)-based PIM cancellation scheme is proposed for PIM cancellation in a CA FDD wireless system. Simulation of the proposed scheme shows that the FFNN cancellation scheme is capable of acheving above 20-dB PIM cancellation ratio over a 30-dB input power range