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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 23 Documents
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Search results for , issue "Vol 15, No 1: March 2026" : 23 Documents clear
FPGA implementation and bit error rate analysis of the forward error correction algorithms in voice signals Khatik, Ramjan; Shaikh, Afzal; Sawant, Shraddha; Patil, Pritika
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v15.i1.pp86-96

Abstract

The idea of codes (VITERBI) is broadly utilized as a part of the wireless communication system as a result of their less complex nature in the decoding of transmitted message. This paper attempts to develop a performance analysis of the decoder by methods for bit error rate (BER) examination. The Galois field based decoder calculation is only utilized as a part of the communication systems. The decoder calculation-based Viterbi based decoder is carried out using field programmable gate arrays (FPGA) and MATLAB. This paper looks at the execution examination of both the calculations. The reconfigurable processor called Microblaze on the Spartan 3E FPGA is utilized for this purpose. MATLAB based code is used to see the BER analysis after the FPGA implementation output.
Multi-modal sensor integration in chicken-fish-vegetable greenhouse agriculture based on internet of things Risal, Muhammad; Wahyuningsih, Pujianti; Jura, Suwatri; Iskandar, Irmawaty; Jalil, Abdul
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v15.i1.pp138-149

Abstract

Integrated chicken-fish-vegetable farming is a type of agriculture that combines the benefits of them within a single ecosystem. The objective of this study is to develop a control and monitoring system for integrated greenhouse-based chicken-fish-vegetable farming using the internet of things (IoT). The monitoring method employs the integration of multi-modal sensors in the greenhouse, consisting of a camera, water level, DHT11, pH, TDS, DS18B20, light dependent resistor (LDR), and infrared (IR) sensor. The camera functions as a visual monitoring tool for the farm, water level sensor detects hydroponic water levels, DHT11 measures air temperature and humidity, pH sensor measures water acidity, TDS sensor detects water nutrients, DS18B20 measures pond water temperature, LDR detects weather conditions, and IR sensor measures sunlight intensity. The processing units used to control the sensors and output devices are the ESP32 and Raspberry Pi. The system outputs include a relay for pump control, an LCD for text messages, and IoT information visualization using the Blynk platform. The results of this study demonstrate that the multi-modal sensor device can effectively monitor the conditions of integrated greenhouse-based chicken-fish-vegetable farming, achieving an accuracy of up to 96%, with an average data transmission time of 6 seconds through the Blynk IoT platform.
Design of a solar system with a PID controller based on the Tyrannosaurus optimization algorithm Rahimah, Kadhim Sabah; Abed, Issa Ahmed; Kadhim, Afrah Abood Abdul
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v15.i1.pp170-182

Abstract

Although photovoltaic (PV) power generation systems are an efficient way to use solar energy, their conversion efficiency is very low. Keeping the DC output power from the panel consistent is the key challenge with solar PV systems. Radiation and temperature are two variables that can impact a panel's output power. This study proposes a unique hunting-based optimization technique called the Tyrannosaurus optimization algorithm (TROA). It is demonstrated that the TROA can be used to achieve maximum power point tracking (MPPT) for lithium-ion battery charging with solar panels. Tyrannosaurus Rex hunting techniques served as the model for this approach. MPPT is used to regulate the solar array's output in PV systems. A buck converter is used by the charge controller to convert DC to DC. To provide the most power, it is utilized to balance the impedance of batteries and solar panels. To maximize power transfer, the algorithm modifies the gating signal's duty cycle based on the voltage and current detected by the solar panel. Three well-known optimization methods are contrasted with TROA's performance: gorilla troops optimization (GTO) algorithm, perticle swarm optimization (PSO), and cultural algorithm (CA). In contrast to current approaches, the proposed approach has yielded superior results.
Energy-efficient multilevel inverter for electric vehicles using wireless sensor network monitoring Delcy, Nishalini; Josh, Francis Thomas; Suriyan, Kannadhasan
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v15.i1.pp130-137

Abstract

This research presents a unique energy-efficient routing strategy aimed at optimizing energy consumption and prolonging network longevity using an innovative clustering probability. Cluster-based routing algorithms facilitate versatile configurations and extend the network's lifetime until the last node ceases operation. This study introduces an energy-efficient hierarchical clustering algorithm for wireless sensor networks (WSNs), enhancing the low-energy adaptive clustering hierarchy (LEACH) algorithm. The objective of this algorithm is to reduce power consumption by the strategic selection of new cluster heads (CH) in each data transfer round and to prevent network conflicts. This objective is accomplished by employing an efficient function to identify the optimal CH nodes in each cycle, considering the current energy levels of the sensors. The suggested technique enhances the cluster formation process by utilizing the reduced distance to the base station. This study findings will enhance packet scheduling algorithms for data aggregation in WSNs to minimize the number of packets transmitted from sensors to CH. Simulation findings validate the system's efficacy in comparison to alternative compression techniques and non-compression scenarios utilized in LEACH and multi-hop LEACH.
Deployment and evaluation of facial expression recognition on Android and Temi V3 in controlled settings Hariz Nazamid, Mohamad; Jailani, Rozita; Khalidah Zakaria, Nur; P. P. Abdul Majeed, Anwar
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v15.i1.pp42-53

Abstract

Facial expression recognition (FER) is vital for improving human-robot interaction (HRI). This study presents the deployment and evaluation of an optimized FER model on android devices, specifically tested on the Temi V3 robot in controlled environments. Trained using FER+ and CK+ datasets and optimized with TensorFlow Lite (TFLite) and MobileNetV2, the model achieved a validation accuracy of 92.32%. Its performance was assessed on the Temi V3 robot and a Samsung A52 smartphone, focusing on CPU usage, memory, and power consumption. Cross-device compatibility and real-time performance challenges were addressed through model quantization and thread optimization. Real-time testing on the Temi V3 showed an overall accuracy of 82.28%, with emotion-specific accuracies ranging from 46.19% to 92.28%. This study offers practical insights for optimizing FER systems across android platforms, with potential applications in education, healthcare, and customer service. The results support the feasibility of implementing FER models as backends in android applications, enabling more intuitive and responsive HRI. Future work will focus on improving model efficiency for lower-end devices and exploring on-device learning techniques to boost accuracy in diverse real-world environments.
Design and implementation of a novel approximate carry look ahead adder for low-power FIR filter applications Kumar, Badiganchela Shiva; Reddy, Galiveeti Umamaheswara
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v15.i1.pp248-258

Abstract

Approximate computing is a low-power circuit design strategy that trades off computational accuracy for gains in speed, power efficiency, and area reduction. This approach achieves considerable power and area efficiency by introducing acceptable errors. The acceptable error in computation systems refers to a loss in accuracy that does not affect overall system performance. Approximate computing is mainly suitable for multimedia and signal processing applications. In this work, a novel approximate carry look-ahead adder (CLA) based on logical level modification is proposed. The new carry prediction term is derived to reduce the overall propagation delay of the addition operation. The proposed multi-bit adder design uses a square root based division method to partition the adder stages. Moreover, the proposed adder is applied in finite impulse response (FIR) filter implementation to evaluate the performance in real-time applications. The proposed adder and FIR filter are coded in Verilog and verified using the Xilinx simulator. The result shows that the proposed FIR filter achieves better results in terms of all parameters.
Online method for identifying Thevenin model parameters of Li-ion batteries and estimating SOC using EKF Lagraoui, Mouhssine; Nejmi, Ali; Lhayani, Mouna; Benfars, Mohamed; Abbou, Ahmed
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v15.i1.pp54-67

Abstract

Accurate state of charge (SOC) estimation is critical for the reliable operation of battery management systems (BMS) in electric vehicles (EVs) and energy storage applications. This paper presents a method for online identification of Thevenin model (TM) parameters and SOC estimation using the extended Kalman filter (EKF). The objective is to improve estimation accuracy by precisely characterizing the SOC-dependent variations of model parameters, including open-circuit voltage (VOCV), internal resistance R1, polarization resistance R2, and capacitance C2. These parameters are identified using least squares regression based on experimental discharge data from a 1.83 Ah lithium-ion (Li-ion) battery. The resulting model is validated under pulsed discharge conditions, achieving a mean absolute error (MAE) of 0.0059 V and root mean square error (RMSE) of 0.0074 V, indicating high modeling accuracy. Subsequently, an EKF algorithm is implemented using the identified model to estimate SOC in real time. Experimental results show excellent performance with an SOC estimation MAE of 0.059% and RMSE of 0.0798%, demonstrating high precision, fast convergence, and stability. The method effectively combines empirical parameter identification with a recursive filtering technique, offering a practical and embeddable solution for BMS applications. The study concludes that accurate parameter modeling significantly enhances EKF-based SOC estimation, providing a robust foundation for real-time battery monitoring and control. 
ELLMW: an enhanced vision–language model for reliable text extraction from manually composed scripts Venkatesh, Dhivya; Sivaraj, Brintha Rajakumari
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v15.i1.pp194-203

Abstract

While conventional optical character recognition (OCR) systems can digitize text, they struggle with diverse handwriting styles, noisy inputs, and unstructured layouts, limiting their effectiveness. This study proposes enhanced large language model whisperer (ELLMW), a vision–language framework for accurate text extraction (TE) from fully handwritten scripts. The methodology integrates advanced preprocessing (noise reduction, binarization, and skew correction), deep learning–based handwriting recognition convolutional neural network–long short-term memory (CNN–LSTM), and LLM-based post-correction to ensure context-aware and structurally coherent outputs. The system converts scanned images, portable document formats (PDFs), and irregularly formatted answer sheets into machine-readable text, while automatically correcting errors in spelling, grammar, and layout. Experimental evaluation on a curated dataset of handwritten examination answer scripts (HEAS) demonstrates that ELLMW achieves 97.8% accuracy, 1.04%-character error rate (CER), and 3.24%-word error rate, outperforming widely used OCR tools including Tesseract, EasyOCR, Google Cloud Vision (GCV), PaddleOCR, ABBYY FineReader, and Transym OCR. The results highlight the model’s robustness across varying handwriting styles, noisy backgrounds, and complex document structures.
Design and development of an enhanced U-shaped microstrip antenna for super wideband applications in next-generation wireless systems Periyasamy, Mani; Jayalakshmi, Shankar Sharma Karthikeyan
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v15.i1.pp204-213

Abstract

The proposed enhanced U-shaped microstrip antenna is conceived with the aim of meeting the emerging needs of super wideband (SWB) applications in contemporary wireless communication systems. An efficient upgraded U-shaped patch design, in combination with substrate enhancements and impedance matching methods, is introduced in this work to remarkably increase the operational bandwidth, gain, and radiation efficiency of antenna. The antenna aims SWB achievement with the help of optimized dimensions and it is designed in such a way that it minimizes ground wave losses. It maximizes the impedance matching over a frequency range of 2 MHz to 20 GHz. Through various simulation outputs and experimental verifications, the antenna designed demonstrates excellent performance with a broad impedance bandwidth greater than 100% and the radiation patterns that are stable beyond entire frequency band. This work illustrates that the enhanced U-shaped microstrip antenna can attain the needs of next-generation communication technologies with specific criteria, and it establishes an efficient solution to SWB systems without sacrificing performance, cost, or size issues.
IoT cloud integration with EfficientNet-B7 for real-time pest monitoring and leaf-based classification Shanmugam, Sabapathi; Natarajan, Vijayalakshmi
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v15.i1.pp150-158

Abstract

The increasing prevalence of pest infestations poses a significant threat to global agricultural productivity, often resulting in substantial yield losses and economic damage. To address this challenge, this paper proposes an intelligent, cloud-enabled pest detection and classification framework leveraging state-of-the-art deep learning techniques. The proposed system integrates YOLOv8 for rapid and accurate pest detection with EfficientNet-B7 for fine-grained species-level classification. The framework is trained and evaluated using the Pestopia dataset, which contains annotated images representing diverse pest species. To enhance data diversity, robustness, and model generalization, data augmentation techniques such as center cropping and horizontal flipping are applied during preprocessing. YOLOv8 is employed to detect and localize pest instances within images, while EfficientNet-B7 extracts high-level discriminative features from detected regions to enable precise species identification. Furthermore, the system incorporates cloud-based real-time monitoring through Adafruit IO, enabling scalable, remote access to pest information for timely decision-making. The performance of the proposed framework is evaluated using standard metrics, including accuracy, precision, recall, and F1-score, achieving values of 97.8%, 98.9%, 98.4%, and 98.9%, respectively. The experimental results demonstrate the effectiveness and reliability of the proposed approach for real-time pest management. The cloud-integrated architecture facilitates proactive pest control strategies, supporting smarter, data-driven agricultural practices, and improved crop protection.

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