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International Journal of Reconfigurable and Embedded Systems (IJRES)
Published by Institute of Advanced Engineering and Science
ISSN : 20894864     EISSN : 27222608     DOI : -
Core Subject : Economy,
Economics, Econometrics & Finance
The centre of gravity of the computer industry is now moving from personal computing into embedded computing with the advent of VLSI system level integration and reconfigurable core in system-on-chip (SoC). Reconfigurable and Embedded systems are increasingly becoming a key technological component of all kinds of complex technical systems, ranging from audio-video-equipment, telephones, vehicles, toys, aircraft, medical diagnostics, pacemakers, climate control systems, manufacturing systems, intelligent power systems, security systems, to weapons etc. The aim of IJRES is to provide a vehicle for academics, industrial professionals, educators and policy makers working in the field to contribute and disseminate innovative and important new work on reconfigurable and embedded systems. The scope of the IJRES addresses the state of the art of all aspects of reconfigurable and embedded computing systems with emphasis on algorithms, circuits, systems, models, compilers, architectures, tools, design methodologies, test and applications.
Arjuna Subject : -
Articles 505 Documents
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Binary hybrid pathfinder algorithm for efficient feature selection in resource-constrained embedded systems Rahul Mirajkar; Premanand Ghadekar; Vijay Dasharath Chougule; Renuka Bhandari; Hridaynath Khandagale; Mahavir A. Devmane; Mangesh Hajare; Kuldeep B. Vayadande
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 15, No 2: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v15.i2.pp504-513

Abstract

Feature selection is critical for embedded machine learning systems where computational resources and memory are severely constrained. This paper presents the binary quadratically interpolated hybrid pathfinder algorithm (BQIHPFA), a novel metaheuristic optimization method designed for efficient feature subset selection in resource-limited classification tasks. BQIHPFA adapts the continuous QIHPFA to binary search spaces through sigmoid transfer functions and employs a hybrid two-group enhancement strategy combining pathfinder dynamics with salp swarm algorithm-inspired exploration. We evaluate BQIHPFA against three established binary optimization algorithms (binary particle swarm optimization (BPSO), binary grey wolf optimizer (BGWO), and binary whale optimization (BWO)) on three benchmark datasets with varying dimensionalities: Língua Brasileira de Sinais (Brazilian Sign Language) movement (90 features), Parkinson's disease detection (22 features), and Sonar Rock vs. Mine (60 features). Experimental results demonstrate that BQIHPFA achieves competitive classification accuracy (average 83.57%) with substantial feature reduction (average 64.1%) while executing 5.2 times faster than complex baselines and consuming minimal memory (peak: 45-58 MB). Ablation experiments demonstrate that every algorithmic part makes a 8-24% contribution to the total performance. BQIHPFA offers an easy-to-use, non-specific feature selection method to automated resource-constrained embedded classification systems, applicable to be deployed to low-power computing environments, and internet of things (IoT) edge systems.
Using OOA-based proportional-integral-derivative controller to enhance the charging and discharging of battery voltage Hassanin Falah Abdul Hassan; Issa Ahmed Abed
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 15, No 2: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v15.i2.pp364-372

Abstract

Today, hybrid energy harvesters are critical in promoting technological advancement by generating sustainable energy and addressing the financial and environmental concerns around batteries. Because of their unexpected input behavior, hybrid energy harvesters present a challenge in producing the necessary stable energy. Thus, this study provides a power conditioning circuit with an optimal controller. Three proportional-integral-derivative (PID) controllers control the charging and discharging of the battery's bidirectional converter. To improve system performance actively and optimally, optimization algorithms are implemented for the optimization of the PID parameters. Osprey optimization algorithm (OOA)-based PID is used, and its performance is compared with five optimization algorithims (Chimp optimization algorithm (ChOA)-based PID, hony badger algorithm (HBA)-based PID, Zebra optimization algorithm (ZOA)-based PID, and cheetah optimization algorithm (COA)-based PID. The comparison between algorithms was done based on the minimum fitness function value, which shows that the OOA is the best one. All results are implemented in MATLAB/Simulink using the 2021a version as follows: (ChOA 3.061%, CO 4.737%, HBA 3.03%, ZOA 3.058%, and OOA 1.52%).
Configurable embedded solution for multi-mode motor control Ufuk Guner
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 15, No 2: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v15.i2.pp339-349

Abstract

Precise robotic systems often require multiple motor types, which increases hardware complexity, cost, and synchronization effort. This study presents an open-source multi-mode motor control platform based on four half-bridge power stages, enabling direct current (DC), brushless direct current (BLDC), and step per motor control on a single hardware architecture. Unlike existing software based multi-mode approaches, the proposed system introduces automatic motor type identification and safe connection verification at the hardware level, re quiring only a microcontroller and a integrated power stage. This represents a key novelty of the platform. Experimental validation was performed using three different motor types. The system achieved correct motor classification over re peated identification tests, with no false detections. Position control experiments confirmed stable operation across DC, BLDC, and stepper motor. The results demonstrate that the proposed platform significantly reduces system complexity while providing reliable multi-motor operation in a compact and low-cost structure.
An IoT-enabled vision-aid for the blind integrating ultrasonic obstacle detection and GPS-based location tracking Varuna Kumara; Akshatha Naik; Ashwini Ashwini; Navilgone Krishna Vaishnavi; Ruchitha Kamath Subhashchandra; Trapthi Trapthi
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 15, No 2: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v15.i2.pp386-395

Abstract

Visual impairment significantly affects independent mobility and personal safety, creating a need for affordable and reliable assistive navigation technologies. This paper presents the design and implementation of a low-cost wearable Vision-Aid system to support visually impaired individuals during outdoor navigation. The primary objective of the study is to enhance obstacle awareness, location tracking, and emergency communication using accessible embedded technologies. The proposed system integrates ultrasonic sensors for real-time obstacle detection, an Arduino microcontroller for data processing, a global positioning system (GPS) module for location tracking, and a global system for mobile communication (GSM) module for emergency alert transmission. Audio feedback is provided through a voice module to guide the user safely. Experimental evaluations were conducted under various environmental conditions to assess obstacle detection accuracy, response time, and location reliability. The results demonstrate accurate obstacle detection, timely audio alerts, and reliable real-time location sharing with caregivers. The proposed system improves user confidence, mobility, and safety while maintaining low implementation cost. This work highlights the potential of embedded and internet of things (IoT)–based assistive devices to enhance autonomy for visually impaired individuals and provides a foundation for future integration of artificial intelligence (AI)-based object recognition.
An efficient hybrid genetic–cuckoo search algorithm for the quadratic assignment problem Firas Abdullah Attia; Iraq Tareq Abbas
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 15, No 2: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v15.i2.pp461-467

Abstract

Quadratic assignment problem (QAP) is one of the most difficult NPhard combinatorial optimization problems with applications ranging from facility layout design, scheduling and manufacturing systems to software optimization. This paper introduces a hybrid metaheuristic algorithm based on genetic algorithm (GA) and cuckoo search (CS); an improved solution quality and convergence speed is observed for QAP instances where GA itself performs poorly as well. This approach leverages the exploration capabilities of GA with computationally intensive exploitation that is easy for CS, to create a balanced yet robust searching mechanism across complex optimization landscapes. We tested the algorithm on benchmark instances taken from quadratic assignment problem library (QAPLIB) and compared it with many classical heuristics such as standard GA, particle swarm optimization (PSO) method and original CS algorithm. Experimental findings showcase that the presented hybrid GA-CS algorithm outperforms traditional standalone GAs regarding solution quality and computational time with significance by promptly converging toward high-quality solutions, especially for medium- to large-scale test instances. In addition, performance improvements over competing methods are shown as statistically significant using the Wilcoxon signed-rank test. The results show that the proposed hybrid framework is an efficient and accurate optimization technique for solving challenging QAP.

Page 51 of 51 | Total Record : 505

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