Indonesian Journal of Electrical Engineering and Computer Science
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Lossy ECG signal compression based on RR intervals detection with wavelet transform and optimized run-length encoding
Get PDFIt is expensive to transmit or store significant amounts of electrocardiogram (ECG) records, particularly when using telecommunications channels that charge according to the volume of transferred data. The advancement of telemedicine renders compressing ECG signals even more necessary. Compression aims to reduce the size of data while maintaining the features of ECG signals. This paper presents a novel strategy for compressing ECG signals based on 3D format conversion. After identifying the RR intervals, we divide the signal into cardiac cycles and proceed with the cut and align process. A 3D discrete wavelet transform (DWT) is employed to minimize the correlation existing between two adjacent voxels. Moreover, an optimized run-length encoding (RLE), a novel lossless compression technique, has been proposed to increase the compression ratio (CR). The proposed strategy is applied to different types of ECG records of the Arryyhmia database. This algorithm demonstrates improved performance in terms of CR and percentage root-mean-square difference (PRD) compared to several recently published works
Survey on plant disease detection via combination of deep learning and optimization algorithms with IoT sensors
Get PDFCrop diseases are one of the main problems facing the farming sector. Detecting plant diseases using some automatic techniques is advantageous because it recognizes problems early and eliminates a significant amount of monitoring effort on massive farms. Numerous investigators have created various metaheuristic optimizing and an innovative technique for deep learning to recognize and classify plant illnesses. This research analyzes many IoT-based methods for automated plant disease identification and detection. The automatic module for detecting plant diseases provides data to a sink node that the system maintains to facilitate IoT-based monitoring. Numerous methods based on plant disease and computer vision exist. Thirty three papers in all are examined here. This research also offers a thorough understanding of how to enhance IoT-integrated plant disease detection and identification capabilities. In addition to this, various problems and research gaps are noted along with potential research
Optimal thermo-QoS-aware routing protocol for WBAN communication
Get PDFWireless body area network (WBAN) has emerged as a promising solution to address problems such as population aging, a lack of medical facilities, and different chronic ailments. WBANs have real-time applications, and there is an increasing demand for them. However, due to changing network structure, power supply limitations, and constrained computing capacity, energy constraints, it is difficult task to achieve quality of service (QoS). To mitigate these limitations, the paper proposed an optimal thermo-QoS aware routing protocol (OTQRP) for WBAN communication. The result was investigated in terms of temperature rise, energy consumption and delay. The paper shows better energy efficiency with respect to existing works. Finally, OTQRP feature comparison is also presented with recent research in terms of features such as complexity, latency, and energy economy and observed that OTQRP shows best performance as compared to others
Ultra-high isolation dual-port circular patch antenna at 2.4 GHz
Get PDFReliable wireless communication in the 2.4 GHz industrial, scientific, and medical band increasingly relies on antenna systems that can provide high inter-port isolation in multiple-input multiple-output (MIMO) configurations. This paper presents a circular microstrip patch antenna and its extension to a dual-port MIMO configuration designed for 2.4 GHz operation. The antenna is implemented on a low-loss substrate and evaluated using full-wave electromagnetic simulations to assess impedance matching, radiation performance, and MIMO diversity metrics. To enhance inter-port isolation in the array, an inverted U-shaped defected ground structure (DGS) is introduced between the two radiating elements. The optimized design achieves excellent matching around 2.4 GHz and ultra-high isolation of approximately -78.7 dB, while maintaining stable gain and radiation patterns across the operating band. These results indicate that the proposed antenna offers a simple and effective solution for compact, energy-efficient, and robust 2.4 GHz MIMO front ends in internet of things (IoT) and other shortrange wireless communication systems
YOLOv8m enhancement using α-scaled gradient-normalized sigmoid activation for intelligent vehicle classification
Get PDFVehicle classification plays a vital part in the development of intelligent transportation systems (ITS) and modern traffic management, where the ability to detect and identify vehicles accurately in real time is essential for maintaining road efficiency and safety. This paper presents an enhancement to the YOLOv8m model by refining its activation function to achieve higher accuracy and faster response in diverse traffic and environmental situations. In this study, two alternative activation functions—Mish and Swish—were integrated into the YOLOv8m structure and tested against the model’s default sigmoid linear unit (SiLU). Training and evaluation were carried out using a comprehensive dataset of vehicles captured under different lighting and weather conditions. The experimental findings show that the modified activation design leads to better model convergence, improved generalization, and a noticeable boost in detection performance, recording up to 5.4% higher accuracy and 6.6% better mAP scores than the standard YOLOv8m. Overall, the results confirm that fine-tuning activation behavior can make deep learning models more adaptive and reliable for vehicle classification tasks in real-world intelligent transportation environments
Relationship between voltage and resistance in hybrid nanoconductive ink on different substrates in wet and dry conditions
Get PDFHybrid graphene nanoplatelet/silver (GNP/Ag/SA) conductive inks are increasingly used in flexible electronics, yet there is limited understanding of how substrate type, solvent composition, and moisture exposure jointly control the electrical performance on metal and polymer substrates. This work aims to clarify how terpinol content (5T, 10T, 15T) and substrate properties of copper (Cu), polyethylene terephthalate (PET), and thermoplastic polyurethane (TPU) influence voltage, resistance, and resistivity of screen-printed GNP/Ag/SA tracks under dry and postimmersion wet conditions. GNP/Ag/SA inks were formulated with fixed butanol and varied terpinol contents, printed on Cu, PET, and TPU, and characterized using electrical measurements, adhesion evaluation, and microstructural observations to relate resistivity trends to morphology, surface energy, and hygroscopic behavior. The Cu substrate showed the best performance, with Cu 10T achieving the lowest dry resistivity of approximately 1.2×10-5 Ω.m and Cu 15T the lowest wet resistivity of approximately 2.0×10-5 Ω.m, supported by dense, well-adhered microstructures. The PET exhibited higher resistivity values up to about 10-3 Ω.m and clear degradation after water immersion, while TPU showed very high or unmeasurable resistivity in wet conditions caused by severe ink loss and hygroscopic swelling, highlighting the important role of substrate surface energy and moisture response in determining the reliability of GNP/Ag/SA inks for applications in humid or wet environments
Cryptojacking detection using model-agnostic explainability
Get PDFCryptojacking is the illicit use of computing resources for cryptocurrency mining. It has emerged as a serious cybersecurity threat that degrades critical system performance and increases operational costs. This paper proposes an advanced machine learning (ML) framework that integrates transformer based language models with post hoc explainable artificial intelligence (XAI) to detect cryptojacking using complementary network traffic and process memory data. Numerical and categorical features are discretized and tokenized to enable semantic modelling and contextual learning. Experimental results show that transformer models effectively capture cryptojacking-related behavioral patterns, with decoding-enhanced BERT with disentangled attention (DeBERTa) achieving high detection performance and recall exceeding 80%. bidirectional encoder representations from transformers (BERT) attains comparable recall with lower computational overhead, making it well suited for real-time environments, while robustly optimized BERT approach (RoBERTa) and DeBERTa are more appropriate for offline or batch-based analysis. Model performance is evaluated using standard classification metrics, and XAI techniques provide interpretable insights into feature relevance, supporting transparent and reliable detection. In general, the proposed framework delivers an effective and deployment-ready solution for cryptojacking detection
Incipient anomalous detection in a brain using the IBIGP algorithm
Get PDFThe detection of an incipient anomalous growth of tissue in a brain is often a difficult task. Various algorithms for brain anomalous detection have been suggested abundantly in the existing literature. In the last decade, many detection methods have been suggested to improve and facilitate abnormal tissue detection. However, the most attractive techniques to many researchers are maybe those that are magnetic resonance imagery (MRI)- based algorithms. A technique known as the inverse of the belonging individual Gaussian probability (IBIGP) is applied to MRI in this work in order to mitigate incipient anomalous tissue detection in a brain. This study demonstrates that the IBIGP technique, applied to the MRI image, is extremely effective in early detecting an anomalous change in the brain MRI image. Although this technique is still in its infancy, it has a great potential to enhance brain anomalous early detection
Artillery fire control based on artificial intelligence algorithm of unmanned aerial vehicle
Get PDFThe article presents the developed artillery fire remote control complex using unmanned aerial vehicles (UAVs) based on an artificial intelligence (AI) algorithm. The developed complex for artillery fire control includes sensor modules for assessing the environment, collecting and processing information, planning and decision-making, and developing a command for the commander of an artillery battalion, division, or brigade. The main advantage of the developed artillery fire control system using UAVs based on an AI algorithm is the most rapid decision-making without human intervention, based on a quick assessment of the environment, the type of enemy weapons, and their category of importance, and an assessment of the distance to the enemy’s military arms. An algorithm is proposed to minimize the power of artillery fire to suppress the enemy
An enhanced NLP approach for BI-RADS extraction in breast ultrasound reports using deep learning
Get PDFBreast cancer stands as one of the top causes of death around the globe, making the accurate interpretation of breast ultrasound reports vital for early diagnosis and treatment. Unfortunately, key findings in these reports are often buried in unstructured text, complicating automated extraction. This study presents a deep learning-based natural language processing (NLP) approach to extract breast imaging reporting and data system (BI-RADS) categories from breast ultrasound data. We trained a recurrent neural network (RNN) model, specifically using a BiLSTM architecture, on a dataset of reports that were manually annotated from a hospital in Saudi Arabia. Our approach also incorporates uncertainty estimation techniques to tackle ambiguous cases and uses data augmentation to boost model performance. The experimental results indicate that our deep learning method surpasses traditional rule-based and machine-learning techniques, achieving impressive accuracy in classification tasks. This research plays a significant role in automating radiology reporting, aiding clinical decision-making, and pushing forward the field of breast cancer research