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Towards decision-making and task planning modules for autonomous mini-UAV mission planning in civil applications

10.11591/ijeecs.v42.i1.pp48-61
Asmaa Idalene , Sophia Faris , Hicham Medromi , Khalifa Mansouri
Autonomous mini unmanned aerial vehicles (UAVs) for civilian applications face a critical challenge: during flight, their mission planning cannot break down complex goals into real-time actions. It’s like having a brilliant strategy with no way to execute it in the moment conditions change. While current solutions can handle basic navigation, they often fail when conditions change. This lack of adaptability seriously limits autonomy in real-world applications, like infras tructure inspection or emergency response. The core problem? Nobody has yet built a system that can think in both layers, combining hierarchical goal decom positions with dynamic tasks without overloading the onboard computer. Our work addresses this gap by introducing an integrated mission planning system with two complementary modules. First: the decision-making module employs recursive goal tree construction to transform high-level mission goals into hier archical sub-goal structures in a systematic manner. Second: the task planning module converts these structured goals into concrete MAVLink command se quences. Together, these modules bridge the gap between abstract mission spec ifications and low-level flight operations while enabling dynamic replanning. To verify if our system actually works, we validated the framework through simulation-based experiments using a Python UAV mission simulator across 50 test runs. The results showed a 94% mission completion rate, with an average planning time of 1.8 seconds for missions with 5 to 8 waypoints. It adapted well to surprises: new targets (100% success), no-fly zones (92% success), and priority changes (96% success). Compared to traditional reactive baseline ap proaches, the framework reduced replanning time by 67%. This tells us that the modular approach is not just theoretically sound but it’s also practically viable for real-world civilian operations.
Volume: 42
Issue: 1
Page: 48-61
Publish at: 2026-04-01

Wearable and implantable antennas for healthcare applications: advancements, challenges, and future directions

10.11591/ijece.v16i2.pp827-841
Sameera P. , Priyadarshini K. Desai , Keerthi Kulkarni
The rise of personalized and remote healthcare solutions has accelerated the demand for reliable wireless communication systems integrated into medical devices. Among these, wearable and implantable antennas play a crucial role by enabling the seamless exchange of data between in-body or on-body sensors and external monitoring equipment. These antennas are key components in systems designed for continuous health monitoring, early diagnosis, and patient rehabilitation. Unlike conventional antennas, those used in medical applications must function efficiently in close contact with or inside the human body, often under challenging conditions such as body movement, varying tissue properties, and limited space. As a result, the design and development of these antennas require careful consideration of factors like flexibility, biocompatibility, low power operation, and electromagnetic safety. This study reviews recent publications from 2017 onwards on wearable and implantable antennas. The material type, operating frequency band, and operational environment are considered for the design of the wearable and implantable antenna. To minimize loss, the research employed a high-thickness substrate, gold, and graphene material for the radiating patch in most of the design. This review presents a detailed overview of recent advancements in wearable and implantable antennas tailored for healthcare applications, highlights current design challenges, and outlines future research opportunities in this rapidly evolving field.
Volume: 16
Issue: 2
Page: 827-841
Publish at: 2026-04-01

Enhancing AODV protocol for black hole attack detection and mitigation in VANETs: a lightweight dual-confirmation approach

10.11591/ijeecs.v42.i1.pp252-262
Ahmed Abderraouf , Ramdane Taglout , Sofiane Boukli-Hacene
Vehicular ad hoc networks (VANETs) represent a specialized category of Mobile ad hoc networks that are specifically designed to enable communication among autonomous (self-driving or partially self-driving) vehicles. These vehicles are equipped with onboard computers, network interfaces, and sophisticated sensors for data capture and processing. Within a VANET, vehicles have the ability to communicate with each other as well as with surrounding infrastructure, thereby exchanging critical messages aimed at enhancing road safety, reducing traffic congestion, and enabling new services and applications for drivers and passengers. Due to its unique characteristics, VANETs have succeeded in enhancing transportation efficiency and safety. However, VANETs are vulnerable to black hole attacks, where malicious nodes discard packets, compromising safety. Existing solutions suffer from high overhead or infrastructure dependence. We propose a lightweight enhancement to AODV using dual-confirmation (RepAck/Info packets) to detect and isolate attackers in real time. Simulations show a 98% improvement in packet delivery ratio under attack, with minimal protocol modifications. While routing overhead increases by 25%, this trade-off ensures reliable communication in dynamic VANETs.
Volume: 42
Issue: 1
Page: 252-262
Publish at: 2026-04-01

Development of BAPOLAIC: AI chatbot for optical character recognition based-document extraction and voice assistant

10.11591/ijece.v16i2.pp1002-1009
Rival Fahreji , Ryan Satria Wijaya
Conventional chatbots often lack integrated functionalities for complex academic tasks, such as multi-format document handling and multimodal interaction. This paper presents the design, implementation, and performance evaluation of BAPOLAIC, a web-based, multimodal AI assistant developed to address this gap. The system architecture integrates optical character recognition (OCR), a dual-strategy natural language processing (NLP) module, and voice assistance, all orchestrated by the Gemini API. Quantitative evaluation confirmed high performance: the OCR module achieved a 98.69% average accuracy, and the retrieval-based NLP path correctly handled 90% of test queries. Furthermore, the API integration demonstrated exceptional efficiency with a median latency as low as 0.06 ms. Task-based evaluations validated BAPOLAIC's effectiveness in performing intelligent functions like summarization and content-based Q&A, with a superior capacity for handling up to 10 consecutive documents. The results validate BAPOLAIC as a successful proof-of-concept for a specialized academic tool, providing a framework for integrating multiple AI technologies to enhance educational productivity.
Volume: 16
Issue: 2
Page: 1002-1009
Publish at: 2026-04-01

Design and simulation of an electric vehicle charger with integrated interleaved boost converter and phase-shifted full-bridge converter using MATLAB/Simulink

10.11591/ijece.v16i2.pp687-698
Ahmad Saudi Samosir , Tole Sutikno , Alfin Fitrohul Huda , Luthfiyyatun Mardiyah
This paper presents the design and simulation of a high-efficiency electric vehicle (EV) charger that integrates a two-phase interleaved boost converter (IBC) with a phase-shifted full-bridge (PSFB) converter using MATLAB/Simulink. In contrast to existing studies that treat these converter stages independently, this work introduces a unified AC–DC–DC architecture that simultaneously minimizes input current ripple, improves DC-bus stability, and enables soft-switching operation for reduced switching losses. The values of the inductors and capacitors are derived analytically based on ripple constraints and switching frequency considerations, and example calculations are explicitly provided. Simulation results demonstrate that the proposed charger maintains a stable 600-V DC bus with less than 2% voltage ripple, delivers a controlled charging current of 100 A with 3 A ripple, and achieves an overall efficiency of 95%. These findings indicate that the integrated interleaved–PSFB topology provides superior conversion efficiency and power quality, making it a strong candidate for future EV fast-charging infrastructure.
Volume: 16
Issue: 2
Page: 687-698
Publish at: 2026-04-01

Comparison of adaptive tuning fuzzy PID and Ziegler-Nichols PID for photovoltaic cooling system

10.11591/ijece.v16i2.pp1063-1074
Yusnan Badruzzaman , Aggie Brenda Vernandez , Septiantar Tebe Nursaputro , Pangestuningtyas Diah Larasati
Renewable energy, particularly solar power, is widely recognized as a clean and sustainable resource, with rooftop photovoltaic (PV) systems playing a vital role in electricity generation. However, high temperatures can significantly reduce their efficiency, making effective cooling systems essential. This study proposes a proportional-integral-derivative (PID) based cooling control system for rooftop PV panels, integrating an adaptive Mamdani fuzzy logic controller to optimize PID parameters dynamically. The methodology includes system modeling, hardware and software implementation, and comparative testing between the Mamdani fuzzy-PID controller and the Ziegler-Nichols PID method. Experimental results show that both controllers effectively regulate PV panel temperature at 36 °C. The Ziegler-Nichols PID achieves faster settling time of 6.45 minutes with a steady-state error of 1.345%, whereas the Mamdani fuzzy-PID reduces the steady-state error to 0.93% but with a longer settling time of 9.15 minutes. These results indicate that the fuzzy-PID controller offers better accuracy and system stability, making it a promising solution for maintaining PV performance under varying environmental conditions. The key novelty of this study lies in its adaptive approach, where the Mamdany fuzzy-PID controller continuously adjust control parameters (Kp,Ki,Kd) in real time, resulting in more consistent and precise temperature regulation than conventional PID tuning methods.
Volume: 16
Issue: 2
Page: 1063-1074
Publish at: 2026-04-01

Energy management in smart grids using internet of things and price-based demand response with a hybrid EVO-PDACNN approach

10.11591/ijece.v16i2.pp699-716
Manju Jayakumar Raghvin , Manjula R. Bharamagoudra , Ritesh Dash
Network control systems for energy distribution play an essential role when renewable energy sources (RES) expand and the smart grid (SG) infrastructure increases. A new approach to energy management (EM) in SGs combines energy valley optimizer (EVO) with pyramidal dilation attention convolutional neural network (PDACNN) to achieve its objectives. Through EVO-PDACNN, the system performs accurate energy consumption forecasting with PDACNN, while the EVO algorithm supports systematic scheduling capabilities. Due to its use, this method reduces the peak-to-average ratio (PAR) by 22% also the cost of electricity (COE) by 12%. This method performs better than the wind-driven bacterial forging algorithm (WBFA), genetic algorithm (GA), particle swarm optimization (PSO), modified elephant herd optimization algorithm (MEHOA), and ant colony optimization (ACO) because it has a new prediction ability and quick response. EVO-PDACNN establishes better performance through lower root mean square error (RMSE), together with mean squared error (MSE) and mean absolute error (MAE), which indicates enhanced cost efficiency and resource management capabilities for SGs. The method strengthens both energy forecasting and operational scheduling operations while effectively dealing with changes in supply and demand, which helps build resilient power systems.
Volume: 16
Issue: 2
Page: 699-716
Publish at: 2026-04-01

Design and implementation of smart meter for optimizing and managing electrical energy in Morocco

10.11591/ijece.v16i2.pp663-674
Alhussein Bagayogo , Omar Kabouri , Aboubakr El Makrini , Mohamed Azeroual , Hassane El Markhi
The growth of renewable energy sources necessitates the use of accurate and fast smart meter solutions. This article presents a low-cost internet of things (IoT) based smart meter adapted to the Moroccan electricity grid, supporting bidirectional energy measurement, DLMS/COSEM-based communication and control relays for automated energy flow management. The experimental validation shows a maximum measurement error of less than ±0.5%, satisfying the IEC-oriented accuracy requirements. The measured end-to-end latency is approximately 700 ms, including data acquisition (≈450 ms), signal processing (≈60 ms), data serialization (≈75 ms), network transmission (≈90 ms), and server-side processing (≈25 ms). These results demonstrate that the proposed system allows an almost real-time monitoring and control of imported and exported energy, which makes it suitable for the integration of residential renewable energies and the application of smart grids.
Volume: 16
Issue: 2
Page: 663-674
Publish at: 2026-04-01

A sub-threshold CMOS temperature sensor circuit core with 2.41 mV/°C sensitivity for ultra-low-power applications (-100°C to 100°C)

10.11591/ijeecs.v42.i1.pp40-47
Abdelhakim Megueddem , Khaled Bekhouche
This paper presents a sub-threshold complementary metal-oxide semiconductor (CMOS) temperature sensor core for ultra-low-power applications, with the key advantage of reliable operation over an exceptionally wide temperature range from –100 °C to 100 °C, which is rarely reported in existing CMOS-based designs. The proposed architecture operates entirely in the sub-threshold region and is evaluated using circuit level simulations, with validation through comparison to a previously reported temperature sensor. Simulation results show excellent linearity across the full temperature range, achieving a coefficient of determination of R² = 0.99997 and a sensitivity of approximately 2.41 mV/°C. At a supply voltage of 1.4 V and 25°C, the sensor core consumes only 22 nW, highlighting its suitability for energy-constrained applications. These results demonstrate the potential of sub-threshold CMOS temperature sensing for wide-range, ultra-low-power sensing systems.
Volume: 42
Issue: 1
Page: 40-47
Publish at: 2026-04-01

Smartphone data privacy and security awareness among university students in Malaysia

10.11591/ijece.v16i2.pp850-862
Ahmed Al-Rassas , Zaheera Zainal Abidin
This study examines the level of data privacy and security awareness (DPSA) among Malaysian university students who depend on smartphones for academic activities. An enhanced cybersecurity education (CE) technological proficiency–perceived control (CTP) model is proposed, incorporating technological innovation and cultural norms (TICN) as a mediating factor between technological proficiency (TP) and awareness. A total of 356 students from public and private institutions in Melaka participated. The Krejcie and Morgan table was used to determine the sample size. Descriptive analysis was conducted using IBM SPSS 27, and SmartPLS-SEM was used to evaluate both measurement and structural models. Reliability and validity were confirmed through a pilot study with 50 respondents. Findings show that TICN significantly strengthens the translation of technical skills into protective behavior, outperforming the original model that used frequency of smartphone usage (FSU) as a mediator. The enhanced model provides a deeper understanding of the socio-technical determinants of smartphone privacy awareness. Implications, limitations, and directions for future research are also discussed.
Volume: 16
Issue: 2
Page: 850-862
Publish at: 2026-04-01

The developing a smart grid control system based on Konnex electrical equipment and internet of things technology

10.11591/ijece.v16i2.pp1020-1029
Tran Duc Chuyen , Mai Van Tao , Hoang Dinh Co
In this research, the authors present a method for developing a smart grid control system based on Konnex (KNX) electrical equipment and internet of things (IoT) technology to control and monitoring electrical energy processes such as: voltage, current, frequency, and power for independent or grid-connected power systems in industry and civil use. The system includes: KNX electrical equipment (KNX-connectivity), IoT control board, Solar panels that produce electricity to supply the system, battery storage devices, converters and controllers, power consumption loads, and many measuring, switching and protection devices for the system. With computer control programming devices, software, and control algorithms, access is possible via website, computer, smartphone, iPhone, and iPad. The goal is to monitor electricity and automatically control the smart building system, which is being used for high-end apartment buildings (luxury housing estate); offices, hotels, and garden villas. The system was researched and tested at the practice workshop for industrial factories and enterprises, bringing high results. The system aims to save energy in the context of increasingly depleted fossil energy, both in Vietnam and around the world.
Volume: 16
Issue: 2
Page: 1020-1029
Publish at: 2026-04-01

Cascaded speech enhancement system using deep learning method

10.11591/ijece.v16i2.pp806-817
Kavitha A , Mahesh Chandra , Vijay Kumar Gupta
Here, a two-stage cascaded noise minimization from noisy speech is proposed for noise cancellation from highly corrupted speech signals. In the first stage, corrupted speech is passed through speech enhancement system based on wavelet domain adaptive filter using least mean square algorithm (WDAF-LMS) and performance is evaluated for noisy signal corrupted by babble noise, car noise and machine gun noises. Then this output is given to second stage for further improvement. This is fully connected deep neural network using stochastic gradient descent with momentum optimizer (FCDNN-SGDM) used to improve the quality of speech signal. The system is tested for highly corrupted noisy speech signals where noise signal power level is equal to or more than clean signal power. Input signal-to-noise ratio (SNR) level is taken as 0 dB and -5 to -13 dB. The proposed system improved the quality and intelligibility of speech at all SNR levels for all three noises.
Volume: 16
Issue: 2
Page: 806-817
Publish at: 2026-04-01

Virtual decomposition with time delay control for underactuated robot manipulator

10.11591/ijece.v16i2.pp791-805
Imane Cheikh , Khaoula Oulidi Omali , Hachmia Faqihi , Mohammed Benbrahim , Mohammed Nabil Kabbaj
The importance of controlling robot manipulators is undeniable. However, faults in these systems can significantly impact the workspace environment and personal safety. To address these challenges, a new adaptive approach has been proposed that easily adapts to a faulty actuator while precisely tracking its desired position. The virtual decomposition control (VDC) method decomposes the robot into subsystems, each with its sub-controller, while ensuring the overall system remains stable. Meanwhile, time delay estimation (TDE) is used to estimate unknown and uncertain parameters. A co-simulation was conducted to test the TD-VDC method on a 6 DoFs robot, which becomes underactuated during its running. The results of the root main square error of the proposed controller were lower of 6% than those of sliding mode control based on partial feedback linearization control (SMC-PFLC), which proves the proposal's effectiveness and efficiency.
Volume: 16
Issue: 2
Page: 791-805
Publish at: 2026-04-01

Impact of electric vehicle demand forecasting on charging station infrastructure development

10.11591/ijece.v16i2.pp1010-1019
Chartrin Kronghinlad , Yuenyong Nilsiam , Nalinpat Bhumpenpein , Siranee Nuchitprasitchai , Sakchai Tangprasert
This research addresses the challenge of forecasting electric vehicle (EV) demand in Thailand and its influence on the development of charging infrastructure. To improve predictive capability in environments with restricted historical data, we employed the grey model (GM) and genetic algorithms (GA) both independently and in combination. Using EV registration records from 2019 to 2023 obtained from the Automotive Information Center of Thailand, the optimized GM-GA hybrid model achieved markedly superior accuracy, with a mean absolute error (MAE) of 0.0016 and root mean squared error (RMSE) of 0.0031. These results demonstrate the model’s capacity to deliver precise forecasts despite data limitations, making it a valuable decision-making tool for charging station planning and deployment. The outcomes underscore the importance of forward-looking infrastructure strategies to support the growth of Thailand’s EV market and its transition toward sustainable mobility.
Volume: 16
Issue: 2
Page: 1010-1019
Publish at: 2026-04-01

Cross-lingual semantic alignment and transfer learning using multilingual language models

10.11591/ijece.v16i2.pp973-980
Niranjan G C , Ramakanth Kumar P , Pavithra H , Minal Moharir
Multilingual language models (MLMs) are widely used for cross-lingual tasks, yet their ability to achieve consistent semantic alignment and transfer to low-resource languages remains limited. This work examines cross-lingual semantic alignment and transfer learning through a comparative evaluation of MLMs at both the word and sentence levels. We analyze general-purpose models such as BLOOM and task-specialized models including LaBSE and XLM-R across English, French, Hindi, and Kannada. Word-level experiments show that LaBSE achieves substantially higher cosine similarity scores of above 0.80 across languages. In sentence-level natural language inference, XLM-R outperforms other models, achieving an F1 score of 68.62% on Kannada and 74.81% on French. These results indicate that model specialization and training objectives play a crucial role in cross-lingual performance, particularly for low-resource languages, and should be carefully considered when deploying multilingual natural language processing (NLP) systems.
Volume: 16
Issue: 2
Page: 973-980
Publish at: 2026-04-01
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