International Journal of Reconfigurable and Embedded Systems (IJRES)
The International Journal of Reconfigurable and Embedded Systems (IJRES), p-ISSN 2089-4864, e-ISSN 2722-2608, provides a global forum for academics, industry professionals, educators, and policymakers to disseminate innovative research in reconfigurable systems, embedded systems, very large-scale integration (VLSI) design, and the Internet of Things (IoT). As computing shifts from personal devices to embedded intelligence, IJRES showcases frontier developments such as FPGA-based reconfigurable computing, edge AI, low-power architectures, chiplet and 3D IC integration, IoT security, and cyber-physical systems. The journal covers both theoretical advances and practical applications, ranging from algorithms, architectures, and CAD tools to case studies in robotics, automotive, healthcare, smart cities, and intelligent power systems. IJRES particularly welcomes contributions addressing emerging challenges in energy efficiency, scalability, reliability, interoperability, and sustainability. Topics span advanced architectures such as SoC, MPSoC, and NoC, alongside hardware/software co-design, real-time operating systems, and machine learning integration for embedded platforms. Published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU), IJRES is an open access, peer-reviewed journal distributed under the Creative Commons Attribution-ShareAlike 4.0 International License. The IJRES has been indexed in Scopus, with all issues published since 2019 included in the database. The journal is currently ranked with a Source Normalized Impact per Paper (SNIP) of 0.393 and a SCImago Journal Rank (SJR) of 0.202. IJRES is committed to advancing frontier technologies and strengthening the global research community in reconfigurable and embedded systems.
Institute of Advanced Engineering and Science
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Energy-efficient reconfigurable architectures for Edge AI in healthcare IoT: trends, challenges, and future directions
The integration of Edge artificial intelligence (AI) with internet of things (IoT) technologies is transforming healthcare applications, including wearable monitoring, telemedicine, and implantable medical devices, by enabling low-latency and intelligent data processing close to patients. However, stringent requirements on energy efficiency, reliability, real-time responsiveness, and data privacy continue to hinder scalable and long-term deployment in resource-constrained healthcare environments. Energy-efficient reconfigurable architectures—such as field-programmable gate arrays (FPGAs), coarse-grained reconfigurable arrays (CGRAs), and emerging memory-centric and heterogeneous platforms—have emerged as promising solutions to address these challenges by balancing flexibility, adaptability, and power efficiency. This review systematically examines recent advances in reconfigurable Edge AI architectures for healthcare IoT, highlighting key trends in hardware–software co-design, AI-assisted design automation, memory-centric optimization, and domain-specific overlays. It further identifies critical challenges, including energy–performance trade-offs, runtime reconfiguration overheads, security and privacy vulnerabilities, limited standardization, and reliability concerns in dynamic clinical settings. Finally, future research directions are outlined, emphasizing self-optimizing and context-aware architectures, secure and trustworthy reconfiguration mechanisms, unified frameworks for heterogeneous healthcare workloads, and sustainable, carbon-aware edge computing. Collectively, this review positions energy-efficient reconfigurable architectures as a foundational enabler for next-generation Edge AI in IoT-enabled healthcare systems.
Copyright (c) 2026 Tole Sutikno, Aiman Zakwan Jidin, Lina Handayani http://creativecommons.org/licenses/by-sa/4.0
FPGA implementation of a coprocessor architecture for random data generation and encryption
Coprocessors are designed to perform some specific tasks to enhance system performance and speed. Information security is the main focus in internet of thing (IoT), cryptography, and cybersecurity applications. In this work, a coprocessor architecture is designed to generate 4-bits of random data and perform encryption. Coprocessor architecture uses true random number generator (TRNG) and pseudo-random number generator (PRNG) architectures to generate random data. Modified linear feedback shift register (LFSR)-based PRNG and modified transition effect ring oscillator (TERO) and ring oscillator-based TRNG architectures are designed and implemented for performing encryption. A serial-in-parallel-out (SIPO) shift register circuit is used to generate 4-bit random data. A 15-bit instruction word is assigned to coprocessor architecture to perform its task. The coprocessor architecture is designed using VHSIC Hardware Description Language (VHDL) and implemented on an Artix-7 field programmable gate array (FPGA). All simulation and synthesis results of the proposed coprocessor architecture are obtained by the Xilinx Vivado 2015.2 tool. Coprocessor architecture efficiency (throughput (Mbps)/LUTs) is 2.31, and it operates at a 100 MHz clock.
Copyright (c) 2026 Manoj Kumar http://creativecommons.org/licenses/by-sa/4.0
Optimizing call center agent efficiency through deep learning-based classifications using SMFCCAE
Call centers are vital to business operations worldwide, acting as the primary interface between companies and their customers. They handle customer inquiries, manage complaints, and facilitate telephonic sales, making them essential to customer service. However, ensuring quality in the call center industry remains challenging, primarily due to the heavy reliance on call center representatives (CSRs) who manage high volumes of calls. Traditional methods of evaluating CSR performance often rely on manual assessments of small call samples, which can be time-consuming and limited in scope. With the advancement of deep learning techniques (DLTs), there is an opportunity to more accurately assess CSR performance. This study introduces the selecting minimal features for call center agents efficiency (SMFCCE) approach, which optimizes feature selection from CSR data to enhance classification accuracy and speed. The proposed method achieves approximately 85% accuracy, offering valuable insights and recommendations for improving overall call center operations.
Copyright (c) 2025 Ramachandran Periyasamy, Manikandan Govindaraji, I. Nasurulla, V. Srinivasan, K. Rama Devi http://creativecommons.org/licenses/by-sa/4.0
Deployment and evaluation of facial expression recognition on Android and Temi V3 in controlled settings
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.
Copyright (c) 2026 Mohamad Hariz Nazamid, Rozita Jailani, Nur Khalidah Zakaria, Anwar P. P. Abdul Majeed http://creativecommons.org/licenses/by-sa/4.0
Online method for identifying Thevenin model parameters of Li-ion batteries and estimating SOC using EKF
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.
Copyright (c) 2026 Mouhssine Lagraoui, Ali Nejmi, Mouna Lhayani, Mohamed Benfars, Ahmed Abbou http://creativecommons.org/licenses/by-sa/4.0
FPGA implementation of high-performance Huffman encoder for image processing applications
An optimized Huffman encoder is essential in all applications where it is necessary to achieve the best performance, such as audio coding, data encryption, data compression, and image processing applications. This article presents a space-optimized encoding scheme to maximize performance and minimize latency in Dual Huffman encoding. The proposed approach employs dynamic tree selection using Dual Huffman encoding. A Dual Huffman code with dynamic tree selection can be run in parallel to support high-throughput applications. The resulting design optimally creates the Huffman dual encoding. This codeword table is based on a dynamic tree generation and selection algorithm, leading to a faster encoding process with lower latency. The architecture was developed using Vivado Xilinx 2023.2 and tested on three different field programmable gate array (FPGA) platforms (Zynq 7045, Zynq 7100, and Kria KV260 AI Vision board). A performance comparison between devices demonstrates that the Kria KV260 had the lowest latency (100 ns), as opposed to the Zync 7045 and Zynq 7100, which had latencies of 200 ns and 150 ns, respectively. These results elucidate the scalability of the architecture and its suitability for real-time image compression. When implemented on the Kria KV260, the dynamic tree selection-based Dual Huffman encoder is capable of fast, parallel image compression. The compression makes it a good candidate for advanced FPGA-based image processing systems with internet of things (IoT) applications.
Copyright (c) 2026 Masood Ahmad Mahammad, Appala Raju Uppala, Shaik Mazhar Hussain, Anusha Marouthu http://creativecommons.org/licenses/by-sa/4.0
Portable verification IP: a UVM-based approach for reusable verification environments in complex IP and SoC verification
Reusable and portable verification techniques are becoming more and more necessary due to the growing complexity of system-on-chip (SoC) designs and the need for quick time-to-market. In order to facilitate cross-project reusability, automation, and scalability in SoC verification, this paper introduces a portable verification IP (PVIP) framework based on the universal verification methodology (UVM). The suggested framework improves coverage efficiency and verification portability across heterogeneous platforms by integrating UVM with the portable stimulus standard (PSS). In comparison to traditional UVM-based methods, experimental evaluation shows that the PVIP framework achieves 92% functional coverage, enhances reusability by 87%, and shortens verification cycle time by 27%. These findings demonstrate how PVIP can greatly speed up verification closure, minimize engineering effort, and assist in the development of the next generation of intelligent, scalable, and industry-ready SoC verification environments.
Copyright (c) 2026 Harinagarjun Chippagi, Vangala Sumalatha http://creativecommons.org/licenses/by-sa/4.0
FPGA implementation and bit error rate analysis of the forward error correction algorithms in voice signals
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.
Copyright (c) 2026 Ramjan Khatik, Afzal Shaikh, Shraddha Sawant, Pritika Patil http://creativecommons.org/licenses/by-sa/4.0
An edge AIoT system for non-invasive biological indicators estimation and continuous health monitoring using PPG and ECG signals
This paper presents the design and implementation of an artificial intelligence of things (AIoT)-based system that integrates deep learning and edge computing for real-time non-invasive health monitoring, focusing on the estimation of mean arterial pressure (MAP) alongside vital parameters such as heart rate (HR), blood oxygen saturation (SpO₂), and body temperature. Photoplethysmography (PPG) and electrocardiography (ECG) signals are acquired using low-power MAX30102 and AD8232 sensors, preprocessed with lightweight digital filters, and processed through a 1D convolutional neural network (CNN) deployed on a SEEED Studio XIAO ESP32S3 microcontroller. The model trained using the cuff-less blood pressure estimation dataset, achieved a mean absolute error (MAE) of 2.51 mmHg on the embedded microcontroller and 2.93 mmHg when validated against a standard blood pressure monitor. Experimental results demonstrate high accuracy, achieving a MAE below 5 mmHg, thereby meeting the AAMI and British Hypertension Society (BHS) Grade A standards for blood pressure measurement. The system achieves real-time inference with an average latency of 16 ms and efficient memory utilization, ensuring suitability for wearable and embedded devices. Physiological data are transmitted via Wi-Fi to a Firebase cloud platform and visualized through a cross-platform mobile application. The proposed system demonstrates strong potential for remote healthcare applications, particularly in continuous monitoring and early health risk detection.
Copyright (c) 2026 Hung K. Nguyen, Manh V. Pham http://creativecommons.org/licenses/by-sa/4.0
Home grocery listing hardware system and mobile application with speech recognition feature
A home grocery list is a crucial aspect of household management that ensures sufficient kitchen supplies. The classic pen-and-paper grocery list is ineffective since it is time-consuming and prone to human error. Therefore, in this study, we proposed a microcontroller-based home grocery listing system using a barcode scanner and speech recognition. The proposed system consists of hardware and a mobile application. The main hardware components are the ESP32-S3 microcontroller, MH-ET barcode scanner v3.0, 20×4 LCD, and 2.4 GHz wireless keyboard. The mobile application is developed using the MIT App Inventor. Through the hardware, the system receives user input from barcode scanning or manual data entry using the keyboard. The data captured using a barcode scanner or keyboard is stored in the memory. Subsequently, the data is transmitted to the mobile application of the home grocery listing system via WiFi. Moreover, the mobile application is also equipped with user input via speech recognition and manual data entry using the keyboard. Hence, users have the flexibility to input the grocery list using four methods within the system. The developed home grocery listing system gives a new, satisfying experience to the users and a convenient way for them to make a home grocery list.
Copyright (c) 2026 Mohamad Faris Eizlan Suhaimi, Aiman Zakwan Jidin, Haslinah Mohd Nasir, Mohd Haidar Md Hamzah, Mohd Syafiq Mispan http://creativecommons.org/licenses/by-sa/4.0
Inquisitive biometric feature analysis and implementation for recognition tasks using camouflaged segmentation with AI and IoT
A vital role in reconfigurable and embedded systems which are deployed in smart environements and healthcare monitoring applications is played by human activity recognition (HAR). However, the potential leakage of sensitive user attributes raises serious privacy issues due to collection of data from the end devices and it needs to be transmitted to more powerful platforms for inference. Addressing this key challenge is principally crucial for resource-constrained embedded systems where efficiency of energy is a chief design requirement. The aim of this paper is present an energy-aware, privacy-preserving HAR framework appropriate for low-power embedded platforms. A machine learning–based camouflaged signal segmentation technique is proposed to transform the data collected from the sensor by eliminating sensitive information while preserving activity-relevant features. For characterization of trade off between the energy consumption and accuracy of recognition, parameters are extensively tuned by careful optimization in this proposed model. Experimental evaluations demonstrate that the method significantly reduces the inference of sensitive attributes such as gender, age, height, and weight, with minimal impact on HAR accuracy. Furthermore, the system supports configurable trade-offs between energy usage and classification performance, making it suitable for implementation on low-power embedded devices.
Copyright (c) 2025 Mahesh Shankarrao Patil, Harsha J. Sarode, Abhijit Banubakode, Prakash Tukaram Patil, Nutan Patil, Vijayakumar Varadarajan, Deshinta Arrova Dewi http://creativecommons.org/licenses/by-sa/4.0
Energy-efficient multilevel inverter for electric vehicles using wireless sensor network monitoring
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.
Copyright (c) 2026 Nishalini Delcy, Francis Thomas Josh, Kannadhasan Suriyan http://creativecommons.org/licenses/by-sa/4.0
Multi-modal sensor integration in chicken-fish-vegetable greenhouse agriculture based on internet of things
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.
Copyright (c) 2026 Muhammad Risal, Pujianti Wahyuningsih, Suwatri Jura, Irmawaty Iskandar, Abdul Jalil http://creativecommons.org/licenses/by-sa/4.0
IoT cloud integration with EfficientNet-B7 for real-time pest monitoring and leaf-based classification
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.
Copyright (c) 2026 Sabapathi Shanmugam, Vijayalakshmi Natarajan http://creativecommons.org/licenses/by-sa/4.0
Learning customer preference dynamics using rank-aware matrix factorization and enhanced collaborative filtering model
Understanding how customer preferences evolve over time is a critical challenge for modern recommender systems operating in large-scale, implicit-feedback–driven e-commerce environments. The primary objective of this study is to develop a unified and interpretable framework that simultaneously models ranking-based preferences, collaborative similarity structures, and temporal behavioral evolution of customers. To achieve this, the study proposes a novel hybrid framework that integrates rank-aware matrix factorization (RA-MF), enhanced collaborative filtering (CF), K-means clustering, and temporal cluster migration matrices (TCMM) for learning customer preference dynamics. The ranking factorization model effectively captures implicit signals such as purchase frequency and recency decay, while CF provides complementary similarity-based insights. K-means segmentation reveals diverse customer personas, including high-value loyal buyers and exploratory shoppers, with significant differences in spending and purchasing behavior. Quantitative evaluations demonstrate strong performance improvements, with 11–18% gains in NDCG@10, 10–15% increases in Precision@10, and notable reductions in root mean square error (RMSE) and mean absolute error (MAE). The results highlight the framework’s ability to deliver both accurate recommendations and interpretable behavioral insights, offering valuable contributions to personalized marketing, customer retention, and data-driven e-commerce strategy.
Copyright (c) 2026 Sathya Sundar, Eswara Thevar Ramaraj, Padmapriya Arumugam http://creativecommons.org/licenses/by-sa/4.0
Design of a solar system with a PID controller based on the Tyrannosaurus optimization algorithm
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.
Copyright (c) 2026 Kadhim Sabah Rahimah, Issa Ahmed Abed, Afrah Abood Abdul Kadhim http://creativecommons.org/licenses/by-sa/4.0
Heart disease prediction using hybrid deep learning and medical imaging with wavelet-based feature extraction
The process of heart disease prediction is based on patient medical information, which can be addressed in terms of medical image as well as the results of an electrocardiogram (ECG) conducted to determine the risk of developing heart disease. The hybrid deep learning (DL) algorithms are developed using past data that can identify trends related to cardiovascular disease (CVDs). In the current paper, it is possible to offer a new method of heart disease prediction that would combine high-quality image processing and hybrid DL to enhance the effectiveness of predictions and avoid the shortcomings of the modern approaches. First, medical images like ECG images are pre-processed with butterworth adaptive 2D wavelet filter, which ensures maximal noise reduction, followed by maintenance of spatial and frequency information. The Gabor Wavelet-based feature extraction technique is applied to extract meaningful patterns, including both spatial and frequency domain information, which is essential for detecting heart-related anomalies. The resultant features are then categorized, along with both convolutional neural networks (CNN) and long short-term memory (LSTM), to make reliable and precise predictions of heart disease. The performance indicators, including accuracy (92.4%), precision (91.2%), recall (93.5%), and F1-score (91.0%), are utilized. Applying the model yields significant levels of reliability and generalization compared to traditional applications.
Copyright (c) 2026 Chairmadurai Palanisamy, Kavitha Pachamuthu, Arun Kumar Ramamoorthy http://creativecommons.org/licenses/by-sa/4.0
ELLMW: an enhanced vision–language model for reliable text extraction from manually composed scripts
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.
Copyright (c) 2026 Dhivya Venkatesh, Brintha Rajakumari Sivaraj http://creativecommons.org/licenses/by-sa/4.0
Design and development of an enhanced U-shaped microstrip antenna for super wideband applications in next-generation wireless systems
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.
Copyright (c) 2026 Mani Periyasamy, Shankar Sharma Karthikeyan Jayalakshmi http://creativecommons.org/licenses/by-sa/4.0
Advanced MRI-based deep learning for brain tumors: a five-year review of oncology–radiology–AI synergy
Rapid advancements in computer vision and machine learning have significantly revolutionized medical imaging one such application is brain tumor detection and classification. Deep learning has emerged as a powerful tool, which offers exceptional capabilities in handling complex medical datasets. However, the current systems still face challenges in achieving optimal accuracy, robustness and clinical interpretability. This study presents a comprehensive survey of brain tumor segmentation, classification and detection techniques using deep learning, metaheuristic and hybrid approaches. The detailed quantitative evaluations of conventional and emerging methods are conducted by examining key performance metrics, dataset characteristics, strengths, and limitations. This review highlights recent breakthroughs by analyzing state-of-the-art techniques from the past five years, research gaps and potential directions for future advancements. These findings provide insights into novel architectures, optimization strategies and clinical applications which ultimately guide researchers towards more robust, interpretable and clinically impactful artificial intelligence (AI)-driven solutions for brain tumor analysis.
Copyright (c) 2026 Shrisha Maddur Ramesh, Chitrapadi Gururaj http://creativecommons.org/licenses/by-sa/4.0
Synaptic shield: fusion of ResNext–50 and long short-term memory for enhanaced deepfake detection
Recent developments in deepfakes have created much anxiety about the authenticity of any digital content and thus, calls for implementing detection mechanisms that will work accordingly. This paper uses Synaptic Shield, a innovative deep learning (DL) framework which is customized to detect alterations by deepfakes with high precision levels. It employs both convolution neural networks (CNNs) as well as modules for time feature extractions to test spatial and motion indicators from video data. High-level preprocessing pipelines in combination with confidence scoring mechanism help make Synaptic Shield adaptive toward manipulation techniques such as FaceSwap and DeepFake. The accuracy of our model surpasses other deepfake detection models with a high accuracy of 98.3%. The above results are based on exhaustive experimentation on standard datasets like FaceForensics++, DeepFake detection challenge (DFDC), and Celeb DeepFake (Celeb-DF). Synaptic Shield is shown to be the best with outstanding results that maintain a higher confidence score equivalent to its precision and reliability. Scalability in having the capacity to accommodate various manipulation techniques and levels of video quality indicates robustness in offering an effective method toward ensuring integrity in digital media. The work is an important move forward in addressing the problems created by DeepFake technologies.
Copyright (c) 2026 Amit Mishra, Prajwal Chinchmalatpure, Govinda B. Sambare, Viomesh Kumar Singh, Atul Gulabrao Pawar, Rahul Prakash Mirajkar, Priyanka K. Takalkar, Kuldeep Vayadande http://creativecommons.org/licenses/by-sa/4.0
HDGC-hybrid task offloading framework using deep reinforcement learning and genetic algorithms for 6G edge cloud
The rapid evolution of 6G networks has brought new challenges in the domain of task offloading (TO), particularly within edge computing environments that are heavily reliant on the internet of things (IoT). Traditional TO methods that based on rule-based heuristics or shallow learning techniques fail to adapt efficiently to the dynamic, unpredictable network conditions, resource heterogeneity, and varying task demands. The proliferation of edge computing, the IoT, and 6G networks has introduced new challenges in TO due to dynamic network conditions, resource heterogeneity, and unpredictable task demands. To address these challenges, this work proposes an innovative TO method that integrates deep reinforcement learning (DRL) with heuristic search methods. The combination of DRL and heuristic algorithms enhances adaptability, convergence speed, and decision-making efficiency, making it well-suited for real-time TO in complex and unpredictable environments This paper proposes a novel hybrid TO framework that integrates DRL with genetic algorithms (GA) to address these challenges. The proposed hybrid optimization technique offer promising solutions by leveraging the strengths of individual approaches to balance competing objectives, such as energy consumption, task completion time, and resource utilization. This method explores optimization strategies to enhance TO efficiency in decentralized environments mainly focusing on optimizing energy use while ensuring performance metrics like latency, throughput, and task deadlines are met.
Copyright (c) 2026 Kaniezhil Radhakrishnan, Mong-Fong Horng, Siva Shankar Subramanian, Chun-Chih Lo http://creativecommons.org/licenses/by-sa/4.0
Design and implementation of a novel approximate carry look ahead adder for low-power FIR filter applications
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.
Copyright (c) 2026 Badiganchela Shiva Kumar, Galiveeti Umamaheswara Reddy http://creativecommons.org/licenses/by-sa/4.0