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38 articles for “denoising”
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Real-Time Edge Detection Camera Module Using Discrete Taylor Transform and Heat Equation (PDE): An Applied Mathematical Approach
Abstract: In modern digital signal processing, the capability for denoising and smoothing in real time is very important in scientific, engineering, and industrial applications. This paper presents an efficient hybrid framework that merges two mathematically sound methods, namely, DTT and PDE defined as the Heat Equation, to robustly denoise a signal with minimal distortion. The model addresses one of the most challenging tasks in signal restoration, which maintains the fidelity of …
Published in Current Trends in Signal Processing · Vol. 16, Issue 2, 2026 · pp. 9–14 Read article
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Face And Spoofing Detection Via Genetic Algorithm-Based Feature Selection with MTCNN
Abstract: Face detection and liveness detection in various environments such as different lighting effects, occlusion, complex backgrounds, and different poses and angles play an important role in facial recognition or detection purposes. In this research paper, propose an improved algorithm for face detection and liveness, spoofing detection via genetic algorithm for feature selection, and mtcc detecting the edge of the facial image by Canny filter and spotting the face from the …
Published in Journal of Control & Instrumentation · Vol. 16, Issue 1, 2025 · pp. 17–26 Read article
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Analysis of Signal Processing Algorithms for Bio-Medical Applications
Abstract: Epilepsy is a burdensome brain disorder, in which heavy seizures will happen again and again. Sudden explosion of electrical activity during the seizures will create a blackout for patients and it will break the body equilibrium, so patients may fall down. Epilepsy affects a large population around the globe. It may begin at any age and last for generations. It can be controlled by the surgical removal of the brain …
Published in Current Trends in Signal Processing · Vol. 12, Issue 3, 2022 · pp. 24–27 Read article
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DEM Processing Methods
Abstract: Digital elevation model (DEM) is used generically to mean the digital cartographic representation of the earth in any form, rectangular grids or lattices, triangular networks or irregular spot heights, and breaklines. This paper aims to present a review of recent techniques used in satellite image processing, especially with focus on DEM processing. The reviewed approaches are classified according to the various techniques used in processing DEM, various filters used in …
Published in Journal of Remote Sensing & GIS · Vol. 4, Issue 1, 2013 · pp. 13–28 Read article
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Design Simulation and Assessment of Image De-noising through Improved Median Filters Based Sub band Decomposition
Abstract: The quality of an image sequence can degrade due to a number of causes, such as noise in the recording, transmission, or scanning. Additionally to enhancing the visual appeal, filtering offers the advantage of facilitating subsequent image processing tasks like coding, interpretation, or analysis. While attempting to restore a perfect image from a faulty copy, noise reduction is a top concern. Because of this, there isn't a single strategy that …
Published in Journal of Microelectronics and Solid State Devices · Vol. 9, Issue 2, 2022 · pp. 9–21 Read article
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Attribute based Level Adaptive Thresholding Algorithm for Object Extraction
Abstract: Image processing plays a vital role in the computer vision because most of the scenarios require object extraction and recognition. In order to utilize these images for the procedural analysis of the environmental entities in computational means, it must be a noiseless one. However, the images are affected through noises caused by the various acquisition techniques and hence an effective technique for denoising and segmentation is necessary in Computational Tomography. …
Published in Journal of Advancements in Robotics · Vol. 1, Issue 2, 2014 · pp. 13–17 Read article
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Machine Learning in Nuclear Medical Applications: A Review of Research Frontiers
Abstract: Nuclear medicine, encompassing PET, SPECT, and targeted radionuclide therapy, generates high-dimensional, quantitative data uniquely suited for machine learning (ML) analysis. This review synthesizes current research applications of ML across six key domains. Positron emission tomography (PET), single-photon emission computed tomography (SPECT), and targeted radionuclide therapy are examples of nuclear medicine modalities that generate high- dimensional, quantitative datasets that are particularly well-suited for machine learning (ML)-driven analysis. These imaging methods provide …
Published in Journal of Nuclear Engineering & Technology · Vol. 16, Issue 1, 2026 · pp. 19–24 Read article
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Study of ECG Signal Features Extraction using Wavelet Transform: A Review
Abstract: QRS-complex detection is used for analysis of ECG signal. ECG gives the information about electrical activity of the heart Since an ECG is a non-stationary signal and consists of various types of noise such as baseline wander, power line interference and muscle noise. To eliminate such types of noise wavelet transform is used. Mother wavelet is widely used to extract information from these types of signal by denoising. This paper …
Published in Current Trends in Signal Processing · Vol. 4, Issue 2, 2014 · pp. 1–6 Read article
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U-Net Based Approach for Automated Brain Tumor Classification
Abstract: Brain tumor detection and identification play vital roles in diagnostic procedures in the field of medicine, with the conventional analysis of MRI images requiring a lot of time and also subject to variability. The proposed study involves the use of a CNN-U-Net based approach for brain tumor detection and identification automatically. The study uses a database of 3,064 contrast-enhanced T1-weighted MRI images from 233 patients with the tumors of meningioma, …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 14, Issue 2, 2025 Read article
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Advancing Brain Tumor MRI Segmentation
Abstract: Segmentation of brain tumors in MRI scans is an integral part of neuroimaging carried out for diagnostic and therapeutic interventions. Given that manual segmentation is cumbersome and highly variable, there arises a need for automated, more precise segmentation solutions. This project, ‘Machine Learning and Deep Neural Networks to Advance Brain Tumor MRI Segmentation’ will develop a better, efficient, and accurate segmentation model to help clinicians identify brain tumors with greater …
Published in Recent Trends in Electronics Communication Systems · Vol. 12, Issue 2, 2025 · pp. 28–33 Read article
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Automated Blood Cell Counting and Disease Identification Using Image Processing: Implications for Polymer Composite- Based Biomedical Diagnostic Devices
Abstract: Accurate quantification of blood cells is central to clinical decision-making and to the performance of emerging polymer composite–based diagnostic platforms. This work presents a cost-effective, image-processing pipeline for automated counting of red blood cells (including overlapping cells), white blood cells, and platelets from Leishman-stained peripheral blood smears, and articulates its relevance to polymer composite microfluidic and biosensor devices. Implemented in Python with OpenCV, the workflow performs grayscale conversion, median/Gaussian denoising, …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 262–270 Read article
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Harnessing Deep Learning to Explore Microbial Community Structure and Carbon Storage Capacity in Mangrove Ecosystems: A Framework for Computationally
Abstract: Mangrove ecosystems represent one of the most efficient natural carbon sinks on Earth, functioning as critical blue carbon habitats that sustain diverse microbial communities responsible for biogeochemical cycling and long-term carbon storage. Despite their global ecological significance, accurately quantifying and predicting carbon sequestration in mangrove systems remains challenging due to the complex interactions between microbial diversity, sediment chemistry, and environmental drivers. This study presents a comprehensive and sustainable artificial intelligence …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 1, 2026 · pp. 41–49 Read article
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Diffusion-Based Enhancement of Low-SNR Time- Frequency Signals
Abstract: Traditional enhancing techniques are useless in low signal-to-noise ratio (LSNR) situations because noise drastically interferes with communication signals. Based on an enhanced DiffBIR model, this paper suggests a dual-stage signal improvement approach that combines diffusion with deep learning. By combining the Inception module for multi-scale feature extraction with the Pixel Fusion Attention (PFA) module for significant region highlighting, the model improves signal recovery in the time- frequency domain. Experiments show …
Published in Current Trends in Signal Processing · Vol. 16, Issue 2, 2026 · pp. 15–27 Read article
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Generative AI-Based Inverse Design of Sustainable Biodegradable Polymers with Target Mechanical and Thermal Properties
Abstract: The escalating global plastic pollution crisis has intensified the urgent need for sustainable biodegradable polymer alternatives that can match or exceed the performance of conventional petroleum-based plastics while minimizing environmental impact. However, traditional polymer discovery approaches are severely constrained by high experimental costs, protracted development cycles spanning years, and fundamental inability to simultaneously optimize multiple conflicting material properties such as mechanical strength, thermal stability, and degradation kinetics. This study presents …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1285–1295 Read article
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Graphene-Based Electronic Skin for Wearable Health Monitoring and Human–Machine Interaction, Materials, Structures, and AI Integration
Abstract: Graphene-based electronic skin (e-skin) has emerged as a transformative technology for next-generation wearable health monitoring and advanced human–machine interaction (HMI). Owing to its outstanding electrical conductivity, mechanical flexibility, atomic-scale thickness, and biocompatibility, graphene enables the fabrication of ultrathin, conformal, and multifunctional sensors capable of mimicking the sensory functions of natural human skin. Over the past decade, research in this domain has progressed rapidly across four interconnected fronts: material synthesis and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 · pp. 175–180 Read article
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Machine Learning-Based Channel Estimation in 5G, Beyond-5G, and 6G Networks: Recent Advances and Future Directions
Abstract: Accurate channel estimation is one of the most fundamental challenges in modern wireless communication systems. In fifth- generation (5G) New Radio (NR) and emerging sixth-generation (6G) networks, precise knowledge of the wireless channel is essential for achieving reliable data transmission, high spectral efficiency, and low Bit Error Rate (BER). Conventional estimation techniques such as Least Squares (LS) and Minimum Mean Square Error (MMSE) rely on mathematical channel models and predefined …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 13, Issue 2, 2026 Read article
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Reduction of Ocular Artifacts in Single Channel EEG by EEMD-IMF Thresholding
Abstract: AbstractElectroencephalogram (EEG) is a widely used signal for analyzing the activities of the brain and usually contaminated with artifacts due to the movements of eye, heart, muscles and power line interference. Among these ocular activities creates significant artifacts and makes the analysis difficult. In this paper, ensemble empirical mode decomposition (EEMD) inspired by wavelet thresholding is used for the correction of ocular artifacts (OA) in EEG signals. Unlike the conventional …
Published in Recent Trends in Electronics Communication Systems · Vol. 5, Issue 1, 2018 · pp. 17–25 Read article
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A Technique for Haze Removal Using Contrast Variation and DCP
Abstract: Image enhancement approach is one of the extensively used approaches for image quality enhancement through enhancing the image’s visibility. In this paper, an underwater image for enhancing the underwater image quality has been done and there a simple technique used for haze removal with the help of DCP. In this paper, we have shown two flow diagrams, one is used for base methodology and another for the proposed methodology. In …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 5, Issue 2, 2017 · pp. 23–38 Read article