Research Publications

@article{671,

title = {A high isolated, high gain millimeter wave quad-port MIMO antenna array for wideband 5G new radio application},

author = {P. Tiwari and J. K. Rai and A. K. Dwivedi and V. Gahlaut and P. Ranjan and R. Chowdhury and A. Pandey},

url = {https://www.nature.com/articles/s41598-025-18805-1},

doi = {10.1038/s41598-025-18805-1},

isbn = {20452322 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Scientific Reports},

volume = {15},

publisher = {Nature Research},

abstract = {This paper introduces a high-performance quad-port Multiple-Input Multiple-Output (MIMO) antenna array for wideband 5G New Radio (NR) (n258, n257, n260, and n261) applications in the millimeter-wave (mmWave) spectrum. The target antenna structure employs a 1 × 2 element array design with microstrip line feeding and includes diamond-shaped slots along with defected ground geometry (DGG) to increase bandwidth, gain, and isolation independent of complicated decoupling components. Fabricated on low-loss Rogers RT/Duroid 5880 substrate (εr = 2.2, tanδ = 0.0009), the antenna has an ultrawide operation bandwidth of 24 GHz to 40 GHz with a 50% fractional bandwidth. The measured results show a peak gain of 18.2 dBi and outstanding inter-element isolation of over 44 dB, confirming the potential for interference-free MIMO operation of the antenna. The design also exhibits better diversity performance with an ECC of < 0.0005, diversity gain (DG) of 9.99 dB, and a CCL of < 0.3 bps/Hz, affirming its use in high-capacity, low-latency 5G mmWave systems. In addition, the compact form factor of the antenna (35 × 35 × 0.254 mm3), structural simplicity, and strong performance make it extremely suitable for integration within next-generation IoT platforms, indoor/outdoor wireless systems, and flexible mmWave-enabled communication devices. The present work aims to overcome challenging problems in the realization of wideband operation, high gain, and excellent isolation in compact MIMO configurations and presents a scalable and fabrication-friendly solution for future wireless networks.},

note = {0},

keywords = {ECE},

pubstate = {published},

tppubtype = {article}

}

@article{427,

title = {An efficient model using deep convolutional neural networks for modeling underwater images},

author = {A. C. Jiji and Y. R. A. Bessant and K. M. Sagayam and A. A. A. Jone and L. Dinh and H. Dang},

url = {https://link.springer.com/article/10.1007/s12065-025-01036-8},

doi = {10.1007/s12065-025-01036-8},

isbn = {18645909 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Evolutionary Intelligence},

volume = {18},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Underwater photography suffers from the dispersion of light in water. Blurring and color distortion of an image are difficult aspects of underwater image analysis. Various methods have been investigated for providing solutions for underwater image restoration. Nevertheless, these approaches still exhibit a regression. Herein, we describe a deep convolutional neural network (DCNetUI) for medium transmission estimation. DCNetUI adopts Deep Convolutional Neural Networks (DCNN), whose layers are specially designed to embody the established assumptions/priors in image restoration. Specifically, layers of Maxout units are used to extract almost all haze-relevant features. Parallel convolution with multi scale features are used to remove haze. Max-polling layer is able to preserve resolution of the feature maps. We also propose a novel nonlinear activation function Bilateral Rectified Linear Unit (BReLU), which alleviate the noisy problem. To refine the transmission map gradient filter is used to smooth the image. Finally, the performances of the proposed and existing methods were verified by comparing the experimental results with those of known methods under quality metric settings. Experiments on benchmark images show that DCNetUI achieves superior performance over existing methods, yet keeps efficient and easy to use.The recommended method enhances the color of an image by removing the influence of the aquatic elements. It increased the SSIM by 29%, with a value of 0.967 and PSNR of 47%, with a maximum value of 54.537.},

note = {0},

keywords = {ECE},

pubstate = {published},

tppubtype = {article}

}

@article{458,

title = {EDGE COMPUTING-DRIVEN RESOURCE ALLOCATION FOR LATENCY-SENSITIVE 5G APPLICATIONS},

author = {S. Choudhary and M. V. Kavitha and C. Sujana and R. V. S. Lalitha and W. Patel and N. Martin and K. Lingaraj and J. M. Philip},

url = {https://scibulcom.net/en/article/PT7E951KAF5Txq7vHe0H},

isbn = {13115065 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Journal of Environmental Protection and Ecology},

volume = {26},

pages = {1137-1147,},

publisher = {Scibulcom Ltd.},

abstract = {In 5G networks, the exponential growth of latency-sensitive applications has improved requirement of effective resource allocation. Traditional models often face struggle to satisfy the high latency constraints, due to the inherent delays imposed by the centralised processing. To overcome these issues, this paper proposed a hybrid method for resource allocation in edge computing at the network’s edge. To optimise the resource allocation while ensuring the low latency and higher energy efficiency, the proposed method incorporates artificial intelligence (AI) with optimisation model. The proposed method consists of a multi-layered architecture that begins with data collection at edge devices, followed by pre-processing and utilises the Long Short-Term Memory (LSTM) model for feature extraction. Real-time resource demand forecasting and task distribution analysis are utilised by the AI Deep reinforcement learning (DRL) and the Special Forces Algorithm (SFA) at edge nodes. This method is employed to adaptively distribute the resources based on network conditions and application requirements. Comprehensive simulation results display that the proposed method increased energy efficiency while lowering end-to-end latency that surpasses the conventional approaches. Additionally, the system significantly enhances its performance by meeting the user demands without losing efficiency. This study elaborates the edge computing potential to overcome the drawbacks of existing cloud-based architectures and provides a reliable solution for 5G applications sensitive to latency.},

note = {0},

keywords = {ECE},

pubstate = {published},

tppubtype = {article}

}

@article{461,

title = {Prediction of Voltage Generation in Triboelectric Nanogenerator Using Machine Learning Algorithms},

author = {A. Deepa and L. Nachimuthu and M. V. Kavitha and D. Jyothi},

url = {https://mail.sciencepubco.com/index.php/IJBAS/article/view/33645},

doi = {10.14419/1r9nsq10},

isbn = {22275053 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {International Journal of Basic and Applied Sciences},

volume = {14},

pages = {145-150,},

publisher = {Science Publishing Corporation Inc.},

abstract = {The rapid evolution of solar panels towards greener energy has paved the way for eco-friendly renewable energy generation. However, the effective management of disposed solar cells is an important factor to consider in reducing adverse environmental and health consequences. Hence, the novel based Triboelectric Nano generators are fabricated from waste solar cells and waste chocolate wrappers. The TENG harnesses frictional energy from the contact between the materials, converting it into useful electrical power. This innovative system promotes the efficient utilization of discarded resources, contributing to both renewable energy generation and waste reduction. As a result, the current work offers a realistic technique for gathering electricity and represents a major step in mitigating the difficulties associated with disposing of solar cell waste. The output voltage generation by the TENG is predicted using various Machine learning algorithms. The predictive model performance is also analyzed through various metrics such as Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE).},

note = {0},

keywords = {ECE},

pubstate = {published},

tppubtype = {article}

}

@article{418,

title = {Active and Reactive Power Control in Three-Phase Grid-Connected Electric Vehicles using Zebra Optimization Algorithm and Multimodal Adaptive Spatio-Temporal Graph Neural Network},

author = {E. S. Prasad and S. V. Sonia and K. N. Suresh and T. G. ShivaPanchakshari},

url = {https://www.sciencedirect.com/science/article/abs/pii/S1755008425000377?via%3Dihub},

doi = {10.1016/j.ref.2025.100715},

isbn = {17550084 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Renewable Energy Focus},

volume = {54},

publisher = {Elsevier Ltd},

abstract = {Three-phase grid-connected Electric Vehicles (EVs) are critical for optimizing energy flow, managing Active Power (AP) for charging and discharging, and controlling Reactive Power (RP) to ensure voltage regulation. These features enhance grid reliability and support the seamless integration of large-scale EVs into power grids. However, the unpredictable frequency of charging sessions creates challenges such as voltage fluctuations and grid imbalances, adversely affecting power quality (PQ) and stability. To address these issues, this study proposes a hybrid approach for AP and RP control in three-phase grid-connected EVs. The novel ZOA-MASTGNN technique integrates the Zebra Optimization Algorithm (ZOA) with the Multimodal Adaptive Spatio-Temporal Graph Neural Network (MASTGNN). The ZOA dynamically optimizes system parameters, improving power management, reducing Total Harmonic Distortion (THD), and enhancing grid stability. Meanwhile, MASTGNN predicts optimal control actions, mitigating harmonics, regulating voltage dynamically, and adapting to changing operational conditions in grid-interactive EV systems. The suggested method was implemented on the MATLAB platform and evaluated with existing approaches, including Resiliency-Guided Physics-Informed Neural Networks (RPINN), Elman Neural Networks (ENN), Multilayer Feed Forward Neural Networks (ML-FFNN), Deep Neural Networks (DNN), and Particle Swarm Optimization-Artificial Neural Networks (PSO-ANN). Results showed significant improvements, achieving 19.36% load current THD and 3.52% source current THD, while outperforming other approaches in efficiency and effectiveness. This framework addresses key challenges in large-scale EV integration, offering scalable and practical solutions for sustainable power grid operations.},

note = {0},

keywords = {ECE},

pubstate = {published},

tppubtype = {article}

}

@article{469,

title = {Enhanced Histopathological Image Reconstruction and Classification Using Multi-Input Super-Resolution and Lightweight Neural Networks},

author = {R. Kadirappa and Pujari Venkata and D. Subbian},

url = {https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/jemt.70024},

doi = {10.1002/jemt.70024},

isbn = {1059910X (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Microscopy Research and Technique},

publisher = {John Wiley and Sons Inc},

abstract = {Liver cancer is one of the leading causes of mortality in cancer-related diagnoses in previous years. The mortality rate can be reduced if the cancer is identified at an early stage. In the early stages, the images are acquired through radiography imaging. However, in critical cases, histopathological imaging is used. In these cases, extreme care is to be taken to avoid any misclassification. The histopathological images are high-resolution images; however, in cases where image quality is lost, classification accuracy will be degraded. In this paper, a multi-input super-resolution neural network (MISRNN) is proposed to restore high-resolution images from low-resolution images. To carry out the proposed work, the histopathological images of four classes were collected from a private hospital. To simulate the real-world scenario, the low-resolution images of factors ×2, ×4, and ×6 are obtained through the bicubic interpolation technique. To evaluate the performance of the proposed model, MISRNN, the quantitative metrics PSNR and SSIM are used. The proposed MISRNN achieved the PSNR values of 39.12, 33.98, and 31.02 dB and SSIM values of 0.948, 0.868, and 0.807 on the ×2, ×4, and ×6 images, respectively. The reconstructed super-resolution images are used for classification. The performance of the proposed classification model is improved by the reconstructed super-resolution images. The proposed model can classify the reconstructed super-resolution histopathological images with an accuracy value of 96.7%. The proposed methodology, super-resolution followed by classification, can be used as a benchmark for further research.},

note = {0},

keywords = {ECE},

pubstate = {published},

tppubtype = {article}

}

@article{470,

title = {Waterwheel Plant Algorithm and Capsule Attention Convolutional Neural Networks for Optimal Sizing Framework for Photovoltaic-Battery EV Charging Microgrids},

author = {R. Raja and R. Geetha and V. U. P. Lavanya and G. Indumathi},

url = {https://onlinelibrary.wiley.com/doi/10.1002/est2.70222},

doi = {10.1002/est2.70222},

isbn = {25784862 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Energy Storage},

volume = {7},

publisher = {John Wiley and Sons Inc},

abstract = {The increasing use of electric vehicles (EVs) highlights the importance of energy management (EM) and particularly photovoltaic (PV)-battery microgrids (MGs). However, the conventional optimization methodologies are not always capable of striking an optimal balance between cost, energy, and size of the system, considering uncertainties such as the variability of solar resource and the fluctuating demand of charging of EVs. This paper proposes a hybrid method for the optimal sizing framework for PV-battery EV charging MGs. The proposed method is the combined execution of the waterwheel plant algorithm (WWPA) and capsule attention convolutional neural networks (CACNN). Thus, the proposed method is referred to as the WWPA-CACNN approach. The goal of this work is to achieve optimal sizing of PV-battery systems, enhancing energy utilization, cost-efficiency, and grid independence. The WWPA is used to optimize the sizing of PV panels and battery storage to minimize costs and maximize energy utilization in EV charging MGs. The CACNN is used to predict energy generation, storage, and demand, ensuring accurate forecasting and system adaptability. By then, the proposed method is simulated on the MATLAB platform and compared with various existing methods like particle swarm optimization (PSO), artificial neural network (ANN), non-dominated sorting genetic algorithm-II (NSGA-II), modified snake optimization (MSO), and dung beetle optimizer (DBO). The proposed WWPA-CACNN method also has the lowest total lifetime cost of $12 730 and high efficiency of 96%, which underlines its better overall performance to effectively manage PV-battery EV charging MGs at optimal cost.},

note = {0},

keywords = {ECE},

pubstate = {published},

tppubtype = {article}

}

@article{610,

title = {Health and Ecological Risk Assessment-Based Air Quality Prediction Framework Using Ensemble Learning Network with Optimal Weighted Prediction Score},

author = {M. Lakshmipathy and Santhosh Panjagal and M. Prasad and G. Kodandaramaiah},

url = {https://www.worldscientific.com/doi/10.1142/S0219467827500604},

doi = {10.1142/S0219467827500604},

isbn = {0219-4678},

year  = {2025},

date = {2025-01-01},

journal = {International Journal of Image and Graphics},

pages = {2750060+},

publisher = {World Scientific Publishing Co.},

abstract = {Due to the high intensity of air pollutants in urban areas, people are suffering from more breathing-related health issues. These health effects are prominent in both older and younger people. Several methods and techniques are adopted by the government to tackle the high emission of this air pollutant in metropolitan cities. However, to generate an exact model for minimizing the health effects caused by air pollution, the prediction of fine-grained particles in the air is crucial. Due to globalization and industrialization, people tend to move from rural areas to cities. This rising population in the cities is the main reason behind the air pollution in cities. The continuous intake of polluted air may lead to severe health effects on people. Elderly people with heart, lung, and chronic diseases and children are more prone to breathing issues caused by the continuous intake of polluted air. So, it is essential to predict the quality of air in a region in order to prevent people from the harmful health effects of air pollution. Hence, an air quality prediction framework (AQPF) to assess the health effects caused by air pollution is generated in this work with the utilization of deep learning techniques. The deep-learning-based AQPF is developed to determine the concentration of air pollutants in the air in order to predict the health effects caused by them. The real-time data are used to create this model. The gathered real-time data are pre-processed. The pre-processed cleaned data are now considered for feature extraction. The statistical features, temporal features, and spatial features are all extracted from the cleaned data. The extracted features are now provided as the input to an optimal weighted prediction score-based ensemble learning network (OWPS-ELNet). The developed OWPS-ELNet is made of connecting the machine learning approaches like support vector regression (SVR), multi-layer preceptron neural network (MPNN), extreme learning model (ELM), bi-directional long short-term memory (Bi-LSTM), and recurrent neural network (RNN). The final classification scores are obtained from the OWPS-ELNet. The classification scores obtained ensemble models are given to the weighted prediction score fusion process. Here, the weights optimization for the weighted prediction score fusion is carried out with the help of the fitness-adapted reptile search algorithm (FA-RSA). From the final fused weighted prediction score, the quality of the ambient air is determined. From the determined air quality index (AQI), the health effects of the ambient air can be predicted accurately in that region. Extensive experiments are carried out with other previously generated AQPFs in order to prove the accurate prediction results and the efficient performance offered by the generated AQPF.},

keywords = {ECE},

pubstate = {published},

tppubtype = {article}

}

@article{627,

title = {Synthesis, characterization, comparative study, DFT analysis, ADMET prediction and molecular docking study of Thiophen-2-yl and 4-pyridinyl derivatives of bis (4-hydroxy-2H-chromen-2-one)},

author = {S. Baskar and S. C. Palapetta and G. Harichandran and G. Indumathi and L. G. Babu and J. E. J and K. Karunakaran},

url = {https://www.sciencedirect.com/science/article/pii/S2211715625006629?via%3Dihub},

doi = {10.1016/j.rechem.2025.102679},

isbn = {22117156 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Results in Chemistry},

volume = {18},

publisher = {Elsevier B.V.},

abstract = {The primary objective of this research was to design and synthesize novel bis(4-hydroxy-2H-chromen-2-one) derivatives bearing thiophen-2-yl and 4-pyridinyl substituents with potential anti-inflammatory properties, utilizing an environmentally friendly and economically viable synthetic approach. To achieve this, 4-hydroxycoumarin was condensed with thiophene-2-aldehyde and pyridine-4-carboxaldehyde using Amberlite 400 Cl− resin, an effective and reusable heterogeneous catalyst. The resulting compounds were structurally optimized and characterized using Density Functional Theory (DFT) at the B3LYP/6–311 + G(d,p) level, which also enabled theoretical predictions of their UV–Visible spectra and vibrational modes. Computational analysis was performed to identify the potential biological targets of this set of compounds using Swiss ADME, a cutting-edge computational tool that, in place of tests, allows for the examination and prediction of a wide range of physicochemical characteristics, drug-likeness, pharmacokinetics, and medicinal chemistry. Further, ADMET predictions were performed to estimate absorption, distribution, metabolism, excretion, and toxicity characteristics. Additionally, molecular docking simulations were performed using the titled compounds as ligands against various anti-inflammatory target proteins, with AutoDock Vina, and the results were visualized in Discovery Studio. A comprehensive theoretical and computational investigation, encompassing DFT, ADMET, SwissADME, and molecular docking, highlighted the pharmacological relevance of the synthesized compounds. These findings suggest that the titled compounds could serve as promising NLO materials, prominent candidates for the development of new anti-inflammatory agents, which further leave a scope for biological evaluation and in vitro/in vivo studies.},

note = {0},

keywords = {ECE},

pubstate = {published},

tppubtype = {article}

}

@article{630,

title = {Energy-efficient adaptive routing protocol for EH-WSNs based on deep reinforcement learning and fuzzy clustering},

author = {R. Shashikiran and N. K. Nagesh},

url = {https://iopscience.iop.org/article/10.1088/1402-4896/ae04a8},

doi = {10.1088/1402-4896/ae04a8},

isbn = {14024896 (ISSN); 00318949 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Physica Scripta},

volume = {100},

publisher = {Institute of Physics},

abstract = {In energy-harvesting wireless sensor networks (EH-WSNs), efficient energy management and reliable data transmission are crucial for prolonging network lifetime and improving performance. This paper presents an Adaptive Energy-Efficient Routing Protocol that integrates Deep Reinforcement Learning (DRL) and Fuzzy Clustering to address key challenges, such as energy consumption, network reliability, and data throughput. The proposed protocol leverages fuzzy logic to optimally select cluster heads based on energy levels, distance, and network density, thereby ensuring balanced energy usage among nodes. Additionally, DRL is employed to dynamically determine the best routing paths that minimize energy expenditure while maintaining reliable communication. Simulation results demonstrate that the proposed protocol outperforms existing approaches, such as Low Energy Adaptive Clustering Hierarchy (LEACH) and Efficient Routing Awareness Scheduling (ERAS), in terms of network lifetime, packet delivery ratio (PDR), throughput, and total energy consumption. Specifically, the proposed protocol achieves up to 25% longer network lifetime, 20%-25% higher throughput, and 15%-20% lower energy consumption compared to the benchmark protocols. These results highlight the effectiveness of combining fuzzy logic and DRL for adaptive routing in EH-WSNs, making the proposed solution highly suitable for real-world energy-constrained wireless sensor network applications.},

note = {0},

keywords = {ECE},

pubstate = {published},

tppubtype = {article}

}

@article{633,

title = {DWFUIR: deep weighted least square filter for underwater image restoration},

author = {Chrispin Jiji and I. Fatima and A. K. S},

url = {https://link.springer.com/article/10.1007/s12596-025-02926-x},

doi = {10.1007/s12596-025-02926-x},

isbn = {09746900 (ISSN); 09728821 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Journal of Optics (India)},

publisher = {Springer},

abstract = {Underwater image processing has been a major field of study in ocean exploration, and numerous convolutional neural network based techniques for underwater picture improvement and restoration have been developed over time. Real-world underwater photos typically have a variety of quality issues, including colour casts, poor contrast, decreased visibility, and so on, because of attenuation and scattering of beam in water medium. Thus, in real-world applications, these quality flaws have a negative impact on underwater photographs. To solve these difficulties, in this paper, we suggest an efficient Deep Weighted Least Square Filter for underwater image restoration (DWFUIR). A novel building block for Deep Convolutional Neural Network (DCNN) acquires an end-to-end mapping among small resolution inputs and produce better quality outputs. Such a layer contains learnable parameters, which can be integrated with Weighted Least Square Filter and jointly optimized through training. By integrating suggested layer with DCNNs, DWFUIR can generate better restored, edge-preserving outputs. Experimental evaluations demonstrate that DWFUIR performs better than current restoration techniques, obtaining higher scores in quantitative and qualitative evaluations while successfully maintaining edges. These findings show that the suggested method offers a reliable and effective way to restore underwater images, with a great deal of promise for real-world uses in marine exploration and research.},

note = {0},

keywords = {ECE},

pubstate = {published},

tppubtype = {article}

}

@article{674,

title = {REOUN: restoration and enhancement of optical imaging underwater based on non-local prior},

author = {Chrispin Jiji and M. S. Sujitha and Annie Bessant and G. Indumathi},

url = {https://link.springer.com/article/10.1007/s12596-024-02097-1},

doi = {10.1007/s12596-024-02097-1},

isbn = {09746900 (ISSN); 09728821 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Journal of Optics (India)},

volume = {54},

pages = {1837-1850,},

publisher = {Springer},

abstract = {The complex aquatic environment of underwater imaging sometimes leads to significant image distortion. Due to light absorption and dispersion in aqueous medium, underwater photographs frequently experience serious quality loss, including poor visibility, contrast reduction, and colour divergence. Reducing colour cast, increasing contrast, and improving visibility in these photographs is a difficult task. To increase the quality of underwater photos, restoration and enhancement based on Non-Local previous technique has been developed. However, the utilisation of several undersea image restoration and enhancement techniques is hampered by the over- or under-enhancement they yield. Initially, the use of Non local prior averages pixel intensities of various locations spread across the hazy image plane have a quasi-linear association with those over the equivalent haze free images. Secondly, a dual optimization function is employed to reduce the size of the solution space to remove blur. Furthermore, by estimating the imaging parameters using the information present in the full image, this unique dual optimisation technique produces a more dependable restoration. Thirdly, the negative interference brought about by the region segmentation is removed by the use of a gradient filter. Finally, an improved weighted grey edge method is adopted to enhance image brightness and visibility. A comparison is made between the REOUN and an existing methods in terms of both objective and subjective visual impact. We compared the edge information of the restored results since texture and details are crucial to images and serve as evaluation criteria to gauge an image’s performance. Comparison results show that the suggested REOUN retains the edges best over other techniques.},

note = {0},

keywords = {ECE},

pubstate = {published},

tppubtype = {article}

}

@article{677,

title = {Brain Tumor Detection from MRI Images Using Window-Aware Hierarchical Auto-Associative Polynomial Network with Great Wall Construction Algorithm},

author = {C. Selvan and B. Adusumalli and P. S. Geetha and Chrispin Jiji},

url = {https://link.springer.com/article/10.1007/s44174-025-00558-0},

doi = {10.1007/s44174-025-00558-0},

isbn = {27314820 (ISSN); 27314812 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Biomedical Materials and Devices},

publisher = {Springer Nature},

abstract = {Early brain tumor (BT) diagnosis is plagued by a small number of samples, limited generalizability to other groups, and a lack of ability to detect subtle variation in dense brain tissue. A new technique called Window-Aware Hierarchical Auto-Associative Polynomial Network with the Great Wall Construction Algorithm (WA-HAAPNet-GWCA) has been developed to overcome these problems. This method uses Magnetic Resonance Imaging (MRI) images from the CE-MRI (Contrast-Enhanced Magnetic Resonance Imaging) and Figshare datasets for assessment. Window-Aware Guide Filtering (WAGF) efficiently lowers noise and artifacts during preprocessing, improving image quality. R2U + + with Tuning Attention (R2U-TA) is used to locate tumors precisely. While the Scale-Aware Hierarchical Auto-Associative Polynomial Network (SA-HAAPNet) is used for feature extraction and classification, the Great Wall Construction Algorithm (GWCA) enhances model performance and reliability. The WA-HAAPNet-GWCA approach is able to use the CE-MRI and Figshare datasets and obtains a recall of 99.8% and accuracy of 99.9%, highlighting a very high diagnostic accuracy. The approach is efficacious and shows the feasibility of clinical use by providing solid answers to problems that have eluded diagnosis. This strategy, with elements drawn from the cutting edge, ensures cutting-edge performance in uncovering BTs at an early stage, which significantly enhances patient outcomes and contributes to the development of precision medicine.},

note = {0},

keywords = {ECE},

pubstate = {published},

tppubtype = {article}

}

@article{681,

title = {Dual-port circularly polarized optically transparent MIMO antenna for N77/N78 5G applications},

author = {D. Smitha and B. R. Nayana and M. Pramukhya and M. Brunda and A. K. Dwivedi and V. Singh},

url = {https://www.cambridge.org/core/journals/international-journal-of-microwave-and-wireless-technologies/article/abs/dualport-circularly-polarized-optically-transparent-mimo-antenna-for-n77n78-5g-applications/C5D237C4A70825F9EB8AA7D86C1C316B},

doi = {10.1017/S1759078725102353},

isbn = {17590787 (ISSN); 17590795 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {International Journal of Microwave and Wireless Technologies},

publisher = {Cambridge University Press},

abstract = {A work of compact dual-port transparent multiple-input multiple-output antenna optimized for fifth-generation (5G) N77 (3.3–4.2 GHz) and N78 (3.3–3.8 GHz) bandwidth has been simulated, investigated, and optimized for robust performance in high-speed wireless communication. It features an impedance bandwidth of 3–4.3 GHz with a minimum simulated return loss of −28 dB, with 100% 3-dB axial ratio bandwidth and a simulated gain of 3.5 dB. The conducting plane material is indium tin oxide (ITO), chosen for its high optical transparency and sufficient electrical conductivity to seamlessly integrate into visually demanding applications. The substrate is glass, chosen for its lightweight and durable properties, which enhance both the mechanical durability of the antenna and its electromagnetic performance. To validate the ITO-based simulated design, the prototype with the same geometrical specification has been fabricated with the conducting portion replaced with copper and substrate as glass material due to a lack of facilities for transparent antenna fabrication. The comparative investigation study between the proposed ITO-based transparent antenna and with copper-based prototype (simulated/measured) both on a glass substrate, has been discussed, which supports the findings.},

note = {0},

keywords = {ECE},

pubstate = {published},

tppubtype = {article}

}

@article{688,

title = {Design and experimental validation of a compact inverted l-based quad-port MIMO antenna for 5G NR mm wave systems},

author = {A. K. Singh and A. K. Dwivedi and V. Singh and A. Pandey},

url = {https://www.nature.com/articles/s41598-025-25836-1},

doi = {10.1038/s41598-025-25836-1},

isbn = {20452322 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Scientific Reports},

volume = {15},

publisher = {Nature Research},

abstract = {This paper presents a miniaturized (13x13x0.8 mm3) quad-port MIMO antenna for 5G NR n257, n258, & n261 for mm-wave usage. The recommended antenna designed at 29 GHz chose an optimal design (Stage 3) after a careful examination of performance at numerous stages. The single radiating plane consists of an inverted L-shaped design with identical hexagonal slotted loaded DGS. The quad components of the MIMO antenna are set orthogonally for best inter-element mutual coupling. The key feature of the recommended design is (a) impedance bandwidth of 25 -29.5 GHz (Simulated) and 25 to 27.5 GHz or 28 to 29 (measured), along with outstanding mutual coupling of > 25 dB suitable for new radio frequency (RF) bands n257, n258, & n261 (b) an impressive peak gain of 9.5 dB at a full band. Diversity parameters, such as the envelope correlation coefficient (ECC), diversity gain (DG), TARC and channel capacity loss (CCL) are analyzed and calculated the performance characteristics of the proposed MIMO antenna. The suggested antenna’s findings have been confirmed by experimental data and have been shown to be in close proximity to the simulated results.},

note = {0},

keywords = {ECE},

pubstate = {published},

tppubtype = {article}

}

@article{702,

title = {Design and implementation of discontinuities in Microwave filter design},

author = {C Lakshmi and K. P and D. Kavitha and D. Kannadassan},

url = {https://www.tandfonline.com/doi/full/10.1080/1448837X.2025.2583477},

doi = {10.1080/1448837X.2025.2583477},

isbn = {1448837X (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Australian Journal of Electrical and Electronics Engineering},

publisher = {Taylor and Francis Ltd.},

abstract = {The systematic design and implementation of microwave circuits play a crucial role in the microwave industry. The traditional method of design is based on trial-and-error methods, leading to approximation-based structures. This research work primarily focused on the analysis, design, and implementation of discontinuities in slotted resonator structures, achieving empirical modelling of the coupled resonator structure for the implementation of bandstop filter design. This work leads to a generic systematic design of a microwave structure, like a filter, based on the developer specification. The systematic design and implementation of a band-stop filter using coupled resonator theory for a fractional bandwidth of less than 20% is discussed. Also, a study on the modelling of discontinuity-based structures with elliptical modelling techniques is performed.},

note = {0},

keywords = {ECE},

pubstate = {published},

tppubtype = {article}

}

@article{705,

title = {Rat Race Coupler Design in Fixed Width SIW/Microstrip: Methodology and Validation},

author = {B. K. Bharti and A. N. Yadav and A. K. Dwivedi},

url = {https://ieeexplore.ieee.org/document/11219259},

doi = {10.1109/LMWT.2025.3623905},

isbn = {2771957X (ISSN); 27719588 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Microwave and Wireless Technology Letters},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {This letter introduces a simple and practical solution for designing a compact rat race coupler (RRC) in fixed-width microstrip and substrate-integrated waveguide (SIW) technologies. Unlike the traditional method that requires multiple impedance lines, the proposed approach maintains a uniform impedance and constant cutoff frequency, making it directly applicable to SIW. A significant 26.66% reduction in the coupler’s ring size is achieved. Theoretical design equations are validated through the design of couplers at 5 and 18 GHz in microstrip and SIW, and experimental findings confirm the simulation results.},

note = {0},

keywords = {ECE},

pubstate = {published},

tppubtype = {article}

}

@article{714,

title = {Dispersion compensation in single and multi-channel DWDM using chirped apodized fiber Bragg gratings},

author = {K. K. Balakrishna and G. M. C. Ramappa and K. Palani},

url = {https://beei.org/index.php/EEI/article/view/10599},

doi = {10.11591/eei.v14i6.10599},

isbn = {20893191 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Bulletin of Electrical Engineering and Informatics},

volume = {14},

pages = {4548-4564,},

publisher = {Institute of Advanced Engineering and Science},

abstract = {Chromatic dispersion (CD) is a key limiting factor in long-haul optical fiber communication, particularly in multi-channel dense wavelength division multiplexing (DWDM) systems, where it introduces signal distortion and inter-symbol interference (ISI). This paper proposes a low-dispersion-offset compensation (LDOC) scheme employing Gaussian-apodized linear chirped fiber Bragg gratings (CFBGs) to enhance dispersion management in single and multi-channel DWDM optical fiber communication systems. Simulations were performed in OptiSystem 7.0 for 10 Gbps single-channel transmission over standard single-mode fiber (SSMF) spanning 110–210 km, and were extended to 4- and 8-channel DWDM systems with a 0.8 nm channel spacing. System performance was evaluated in terms of quality factor (Q-factor), bit error rate (BER), and eye height under varying fiber lengths, input powers, and chirp coefficients. The LDOC-enhanced CFBG achieved a Q-factor of 7.04 with a BER of 9.82×10-13 for single-channel transmission at 180 km, 13.83 with a BER of 5.57×10-41 for a 4-channel system at 150 km, and 7.56 with a BER of 7.76×10-11 for an 8-channel system at 150 km. These results confirm significant improvements compared to conventional CFBGs, demonstrating that the proposed LDOC-based approach is a compact and effective solution for next-generation metro-core, long-haul, DWDM, and 5G/6G optical networks.},

note = {0},

keywords = {ECE},

pubstate = {published},

tppubtype = {article}

}

@article{715,

title = {Metasurface-integrated Al₂O₃ ceramic dielectric resonator for enhanced gain and polarization performance in mm-wave MIMO systems},

author = {S. K. Yadav and A. K. Dwivedi and D. Sigroha and S. Tripathi and A. Sharma and S. Pandey},

url = {https://www.nature.com/articles/s41598-025-27278-1},

doi = {10.1038/s41598-025-27278-1},

year  = {2025},

date = {2025-01-01},

journal = {Scientific Reports},

volume = {15},

publisher = {Nature Research},

abstract = {This paper outlines the design and characterization of a dual-port dielectric resonator antenna made from alumina (Al₂O₃) and coupled with a metasurface superstrate for millimeter-wave applications. Alumina ceramic with high permittivity (εr = 9.9, tanδ = 0.0019) was employed to excite the lower-order HEM<inf>11δ</inf> mode through aperture coupling to enable efficient radiation between 27.65 and 28.75 GHz. A dual-stub C-shaped slot was carefully engineered on the substrate to generate orthogonal modes, thereby realizing circular polarization throughout the bandwidth of 27.8–28.45 GHz. To access better radiation properties, a double-negative (DNG) metasurface lens made on an RT Duroid substrate was coupled with a resultant increase in realized gain to about 11 dBi, with preservation of impedance and polarization properties. Experimental characterization confirmed steady broadside radiation patterns with low mutual coupling (<– 25 dB), together with exemplary diversity parameters (ECC < 0.02, DG ≈ 10 dB). The integration of both dielectric ceramic and metasurface building materials demonstrates a synergistic building–structure approach to realizing high-gain, circularly polarized, volume-reduced radiators with millimeter-wave applications. These outcomes highlight engineered dielectric–metasurface architectures as a prospective pathway for licensed 5G FR2 frequency band.},

note = {0},

keywords = {ECE},

pubstate = {published},

tppubtype = {article}

}

@article{372,

title = {Automated liver image segmentation using entropy-based thresholding and median filtering},

author = {S. K. Siri and M. S. Sudha and H. T. Baby and Pramod Kumar and S. C. Pradeepa and N. Abhijith},

doi = {10.19101/IJATEE.2024.111100140},

isbn = {23945443 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {International Journal of Advanced Technology and Engineering Exploration},

volume = {11},

pages = {1699-1713,},

publisher = {Accent Social and Welfare Society},

abstract = {The separation of liver images from abdominal scans has emerged as a critical focus in biomedical image processing, serving as a foundational step in automated techniques for liver disease diagnosis, treatment planning, and follow-up assessment. Current medical research and case studies underscore the challenges of liver segmentation, primarily due to the low contrast between the liver and surrounding tissues in computed tomography (CT) images. Furthermore, the liver's edges are often indistinct, and its texture, shape, color, and size exhibit significant variability. With advancements in medical imaging technology, the volume of data requiring processing has grown substantially, highlighting the need for automated methods to replace time-intensive manual segmentation procedures. In response to these challenges, a novel threshold-based segmentation technique has been introduced, utilizing liver image entropy as a measure of information content. The process involves denoising with a median filter, followed by cropping a random section of the liver image to determine its entropy distribution. This distribution establishes upper and lower bounds, facilitating precise separation of the liver from its background. The proposed method was evaluated on CT scan images from 60 patients, addressing diverse and complex segmentation scenarios. Key performance metrics, including maximum edge distance (MED), relative volume difference (RVD), accuracy, and dice similarity factor (DSF), were employed to benchmark the model against expert-traced reference images. The results indicate an average MED of 12.5 mm, an average RVD of 4.2%, an average accuracy of 91.70%, and an average DSF of 90.95%. These results demonstrate the effectiveness of the proposed model as a robust tool for computer-aided decision support systems, significantly advancing the accuracy and reliability of clinical diagnosis. © 2024 Sangeeta K Siri et al.},

note = {0},

keywords = {ECE},

pubstate = {published},

tppubtype = {article}

}