Engineering, Electrical and Electronic Engineering, Multidisciplinary, Signal Processing
7
Scopus Publications
Scopus Publications
Reliable Data Transmission in Wireless Ad Hoc Networks using Advanced Deep Learning Techniques Selvaganapathi Sennan, G. Elaiyaraja, S. Jeevitha, Radhakrishnan M, Rahul Sharma, Nandhini TJ Proceedings of 3rd International Conference on Sustainable Computing and Data Communication Systems Icscds 2025, 2025 Wireless ad hoc networks face substantial hurdles regarding reliable data delivery because of their dynamic topology structures and mobile network components and fluctuating interference conditions. Recently, there has been emergence of advanced deep learning technologies that offer better solutions that solve the performance related challenges of transmission in WANETs. In optimisation of routing alongside error detection and forwarding data in ad hoc networks, scientists examine applicability of deep learning models through the use of convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The study proves the effectiveness of deep learning processes that are integrated into the conventional network protocols to establish superior network survivability and less data loss as compared to the enhancement of the throughput performance. The primary highlight of this inquiry is on the development of flexible models so as to facilitate a real time prediction of the network status so as to allow automatic adjustments of transmission plans. Both simulation strata and actual experimentation is engaged by researchers who test the product of the techniques to compare them with the standard procedures. Advanced methods that incorporate deep learning techniques show better performance than traditional methods in their better reliability along with lower latency and better power/energy consumption. The application of deep learning in solving complicated WANET issues eliminates major handicaps of the communication systems especially in places that are deep in the land and need stable communications once disasters have hit the regions.
An Optimum Classifier Model with Fuzzy C-Means for Fire Detection Technology Elaiyaraja Gandhi, Kumaratharan Narayanaswamy Pertanika Journal of Science and Technology, 2023 Flames recognition methodology is most important for completely diminishing the flame losses in different fired environmental conditions. However, there is delayed detection and lower accuracy in the various common detection methods. Thus, optimum image/video fire detection technology is proposed in this paper based on a support vector machine (SVM) with the fuzzy c-mean, discrete wavelet transform (DWT), and gray level co-occurrence matrices (GLCM) feature extraction for the detection of fires. This algorithm has been tested on various fire and non-fire images for classification accuracy. A performance evaluation of the proposed classifier algorithm and existing algorithms is compared, showing that the accuracy and other metrics of the proposed classifier algorithm are higher than other algorithms. Furthermore, simulation results show that the proposed classifier model is improved the forecast detection accuracy of fires.
Fast and Efficient Filter Using Wavelet Threshold for Removal of Gaussian Noise from MRI/CT Scanned Medical Images/Color Video Sequence G. Elaiyaraja, N. Kumaratharan, T. Chandra Sekhar Rao IETE Journal of Research, 2022 We proposed an optimum de-noising filter using wavelet threshold for the removal of adaptive white Gaussian noise from degraded medical images/colour video sequence based on system noise calculation. The performance of the proposed Gaussian de-noising technique is compared with various de-noising techniques such as Bayesian least squares-Gaussian scale mixtures, bilateral filter, adaptive bilateral filter, multi-resolution bilateral filter, nonlocal-Means, wavelet threshold based filters, and kernel-based method. The proposed methods have comparable/superior performance and less computational time compared to other techniques.
An enhanced algorithm for removal of noise in CT scan image and 3D abdomen color video sequence through trimmed based filter Biomedical Research India, 2017
Enhancing Medical images by new fuzzy membership function median based noise detection and filtering Technique G. Elaiyaraja, N. Kumaratharan Journal of Electrical Engineering and Technology, 2015 In recent years, medical image diagnosis has growing significant momentous in the medicinal field. Brain and lung image of patient are distorted with salt and pepper noise is caused by moving the head and chest during scanning process of patients. Reconstruction of these images is a most significant field of diagnostic evaluation and is produced clearly through techniques such as linear or non-linear filtering. However, restored images are produced with smaller amount of noise reduction in the presence of huge magnitude of salt and pepper noises. To eliminate the high density of salt and pepper noises from the reproduction of images, a new efficient fuzzy based median filtering algorithm with a moderate elapsed time is proposed in this paper. Reproduction image results show enhanced performance for the proposed algorithm over other available noise reduction filtering techniques in terms of peak signal -to -noise ratio (PSNR), mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), image enhancement factor (IMF) and structural similarity (SSIM) value when tested on different medical images like magnetic resonance imaging (MRI) and computer tomography (CT) scan brain image and CT scan lung image. The introduced algorithm is switching filter that recognize the noise pixels and then corrects them by using median filter with fuzzy two-sided π- membership function for extracting the local information.
Enhanced decision median filter for color video sequences and medical images corrupted by impulse noise G. ELAIYARAJA, N. KUMARATHARAN, C RAMA PRAPAU Biomedical and Pharmacology Journal, 2015 The recent advances in sparse representations of images have achieved outstanding results in terms of denoising and restoration; but removal of real and structured noise in digital video sequences remains a challenging problem. Based on this idea, the problem addressed in this paper proposes to improve the decision median filtering algorithm for denoising of video sequences corrupted with impulse noise. The proposed algorithm processes the extracted frame (from corrupted video sequences) by incorporating robust decisions to selectively operate upon the corrupted pixels. The local statistical parameters (of the spatial kernel) are then used to decide whether to restore the centre pixel with median value or adaptively increment the kernel size. This helps in restoration of structural content with minimal blurring at high noise densities. Experimental results show that the proposed algorithm achieves better performance with minimal computational complexity; yielding higher values of PSNR and SSIM for restored frames.
A new trimmed median-mean based filter for the removal of high density fixed value noise in medical images and videos Arpn Journal of Engineering and Applied Sciences, 2015
