Search
31 articles for “data reconstruction”
-
Harnessing Artificial Intelligence for Precision Physics: A Machine Learning Framework for Data Reconstruction in Support of India's Deep-Tech Missions
Abstract: India's emergence as a global leader in deep-tech innovation is driven by ambitious scientific megaprojects, including the Laser Interferometer Gravitational-Wave Observatory (LIGO)-India, the X-ray Polarimeter Satellite (XPoSat), the Aditya-L1 solar observatory, and the National Quantum Mission (NQM). However, the unprecedented scale and complexity of the observational data generated by these missions present severe computational bottlenecks. Traditional analytical frameworks struggle with non-stationary noise transients, diffusion blurring, and the exponential scaling limits …
Published in Research & Reviews : Journal of Physics · Vol. 15, Issue 2, 2026 · pp. 48–55 Read article
-
Fusion of deep learning autoencoders with random forest for wetland classification using Sentinel-2A data: A case study on Sirpur wetland
Abstract: Present study analyses the performance of deep leaning algorithm-autoencoder to reduce data dimension as compared to conventional models. Classification accuracies of Sirpur wetland using Sentinel 2A dataset with different inputs have also been studied. These inputs sets comprise the reconstructed data through compression of original 13 bands into 4 bands using decoder algorithm, first four Principal Components, all spectral bands, and spectral indices. Random Forest classifier (RF) is used to …
Published in Journal of Remote Sensing & GIS · Vol. 17, Issue 1, 2026 · pp. 25–35 Read article
-
Rainwater Measuring Algorithm in O(1) Time Complexity
Abstract: The Rain Terraces Time Complexity Data Structure Algorithm (RTTCDSA) introduces a novel method for managing temporal data efficiently, inspired by the natural flow of rainwater on terraced landscapes. This study presents the conceptual framework and implementation details of RTTCDSA, which leverages principles of temporal dynamics and landscape morphology to organize and query temporal data with optimal time complexity. RTTCDSA employs a hierarchical structure akin to terraced landscapes, facilitating rapid traversal …
Published in International Journal of Data Structure Studies · Vol. 2, Issue 1, 2024 · pp. 26–32 Read article
-
Deep Learning Enhanced Compressive Sensing for Wireless IoT Data Optimization and Weather Monitoring.
Abstract: This research explores the application of deep learning and compressive sensing in order to optimize data traffic in non-orthogonal multiple access (NOMA)-based wireless internet of things (IoT) networks and weather monitoring. Such a framework would be very effective and overcome pilot attacks and reconstruction losses for secure data transmission. In this regard, a strong communication model has been adopted based on power-domain NOMA for simultaneous wireless transmission by multiple IoT …
Published in International Journal of Satellite Remote Sensing · Vol. 2, Issue 2, 2024 · pp. 20–36 Read article
-
Geological and Geophysical Studies of the Ninety East Ridge: A Brief Review
Abstract: The Indian Ocean serves as a unique laboratory for studying the impact of both global and regional stressors on environmental changes over various geological epochs. Its distinct hydrological conditions and geographical location make it an ideal focal point for research into these phenomena. Of particular interest within the Indian Ocean is the Ninety East Ridge (NER), which holds significance as the world's longest linear rise. Researchers have long hypothesized that …
Published in International Journal of Marine Life · Vol. 1, Issue 1, 2024 · pp. 8–19 Read article
-
Matrix Factorization and Tensor Decomposition at Scale: Mathematical Foundations and Computational Approaches
Abstract: Matrix factorization and tensor decomposition techniques have emerged as fundamental tools in machine learning and data science for handling high dimensional data efficiently. This paper presents a comprehensive analysis of scalable matrix factorization and tensor decomposition methods, focusing on their mathematical foundations, computational complexity, and practical applications. We examine key algorithms including Singular Value Decomposition (SVD), Non-negative Matrix Factorization (NMF), CP decomposition, and Tucker decomposition, with particular emphasis on their …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 56–59 Read article
-
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
-
A Review: Electrical Impedance Tomography System and Its Application
Abstract: Nowadays, the objective of medical research is to improve the diagnostic tools and instruments such as to develop new assessment methods, non-invasive and long-term monitoring method. Currently, the non-invasive technique is used for during pregnancy and labor due to its standard clinical approach. Such as ultrasound and MCG, CTG is also used during pregnancy because of its sensitivity to the mother and fetal movement but it is not used during …
Published in Journal of Control & Instrumentation · Vol. 7, Issue 2, 2016 · pp. 14–22 Read article
-
Noise-Resilient QPSK Modem for Reliable Communication for Green Communication
Abstract: The channel noise is the severely degraded the performance of communication system and that also limits the maximum data transmission rate. Hence, it is required to design a demodulator in a receiver which overcomes the effect of noise in the received signal, reconstructs un-corrupted information signal and improves data rate. In QPSK, noise effect the phase of the modulated signal and that causes error in the information signal. This study …
Published in Recent Trends in Electronics Communication Systems · Vol. 12, Issue 1, 2025 · pp. 36–46 Read article
-
The Emerging Roles of the School Librarian in the Tech Era: A Transformative Synergy with NEP 2020 and KVS Initiatives
Abstract: The implementation of the National Education Policy (NEP) 2020 in India mandates a radical paradigm shift in the school education ecosystem, moving from rote-based learning toward a 5+3+3+4 pedagogical structure centered on critical thinking and multidisciplinary exploration. This paper employs a Qualitative Policy-to-Practice Synthesis (QPPS) design to analyze the evolving role of the school librarian as a strategic catalyst within this framework, with a specific focus on the Kendriya Vidyalaya …
Published in International Journal of Trends in Humanities · Vol. 3, Issue 2, 2026 · pp. 15–21 Read article
-
A Comprehensive Review of Deep Compressive Sensing for Efficient IoT Data Management
Abstract: The Internet of Things has revolutionized data-driven ecosystems and offers advanced services, such as live monitoring and automation in various domains: smart cities, healthcare, and industrial automation. However, with the exponential growth of IoT devices, comes a large amount of data generation, which poses considerable problems like network congestion, latency, and energy inefficiency. Compressive sensing (CS), one of the newest signal processing methodologies, has emerged as an enabler to meet …
Published in Trends in Electrical Engineering · Vol. 14, Issue 3, 2024 Read article
-
Fractal-Entropy Guided Adaptive Signal Reconstruction for Non-Stationary Biomedical and Communication Systems
Abstract: This paper presents a novel Fractal-Entropy Guided Adaptive Signal Reconstruction (FEG- ASR) framework designed for accurate processing of non-stationary signals in biomedical and communication systems. The proposed approach integrates fractal dimension analysis with entropy- based feature evaluation to capture the intrinsic complexity and irregularity of time-varying signals. By dynamically adapting reconstruction parameters based on fractal-entropy measures, the method effectively separates noise from meaningful signal components while preserving critical information. The …
Published in Current Trends in Signal Processing · Vol. 16, Issue 1, 2026 · pp. 22–33 Read article
-
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
-
From High-Resolution Optics to High-Bandwidth Telemetry: The Evolution of Deep-Space Imaging Satellites
Abstract: The development of deep space imaging satellites has significantly enhanced satellite communication and space exploration capabilities. Earlier satellite systems relied on low-resolution imaging and conventional radio-frequency (RF) communication, which limited data transmission rates and image quality. With advancements in technology, modern satellites are equipped with high-resolution optical systems capable of capturing detailed images of celestial bodies. Furthermore, the introduction of high-bandwidth telemetry systems, such as Ka- band and optical (laser) …
Published in Trends in Opto-electro & Optical Communication · Vol. 16, Issue 1, 2026 · pp. 34–40 Read article
-
Computation of Remaining Serviceable Life (RSL) of Bituminous Concrete (BC) Surfaced Road Sections using Hdm-4 Model
Abstract: The road network is the basic component that facilitates free movement of traffic from one area to another. The road sections are constructed to facilitate traffic for their entire design life. Due to the insufficient maintenance and rehabilitation activities, road section deteriorates rapidly and rapid deterioration results in entire road section damage prior to its design life. This paper presents the methodology for computing remaining serviceable life (RSL) in years …
Published in Trends in Transport Engineering and Applications · Vol. 4, Issue 2, 2017 · pp. 8–15 Read article
-
Short-Term Load Demand Forecasting using Chaos Theory and ANFIS
Abstract: In the electrical power sector, forecasting of load demand is an important process for effective planning of future expansion and periodical operations including unit commitments, fuel scheduling, short-term maintenance, security assessments, reducing spinning reserve, reliability analysis etc. Accurate load predictions are also necessary to utilize the electrical energy efficiently and to minimize the conflicts between the demand and supply of electricity. As electric load pattern of a region is very …
Published in Trends in Electrical Engineering · Vol. 6, Issue 2, 2016 · pp. 50–57 Read article
-
A Review on Fundamental Premises and Algorithmic Approaches in Compressive Sensing
Abstract: This paper addresses the classical approach of acquiring signals by following the well celebrated Shannon sampling theorem that underlies the majority devices of current technology, viz. analog-to-digital conversion, medical imaging, or audio and video electronics. In medical imaging, there are problems related to acquisition time and compression. Compressive sensing (CS) paradigm addresses the shortcomings of traditional data acquisition by sampling signals much more efficiently. CS is a novel kind of …
Published in Current Trends in Signal Processing · Vol. 6, Issue 1, 2016 · pp. 18–24 Read article
-
Early Alzheimer’s Disease Prediction Using Vision Transformers and Attention-Guided MRI Analysis
Abstract: Alzheimer’s Disease (AD) continues to be a major global health concern, with early detection being crucial for effective intervention. While conventional machine learning and convolutional neural network (CNN) approaches have made notable progress in automated AD diagnosis using MRI data, they often struggle with capturing long-range dependencies and maintaining spatial contextual awareness. In this research, we propose a novel framework using Vision Transformers (ViTs) for early Alzheimer’s prediction from 3D …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 16, Issue 1, 2026 · pp. 30–40 Read article
-
Estimation Techniques in Image Restoration - A Survey Approach
Abstract: In the present paper a comparative study of various estimation techniques based on neural network, MATLAB, partial differential equation (PDE) and other proposed models for image restoration are been discussed. An image may be distorted, noisy or blurred and not suitable for extracting desired information or data, so it needs to be restored for desired application. Image restoration techniques are oriented towards modeling the degradation, blur and noise and applying …
Published in Current Trends in Signal Processing · Vol. 4, Issue 1, 2014 · pp. 11–16 Read article
-
Advances in Deep Learning for Medical Image Analysis in the Era of Precision Medicine
Abstract: Medical imaging is fundamental to modern healthcare but analyzing the high-dimensional data requires advanced techniques. Manual image interpretation is time-consuming, subjective and limited in detecting complex patterns and minute details. Recent breakthroughs in Deep Learning offer transformative advances for unlocking clinically relevant information from medical images. This paper provides a comprehensive 6000+ word review of the current state-of-the-art Deep Learning techniques for medical image analysis including detailed coverage of key …
Published in Research and Reviews : Journal of Computational Biology · Vol. 12, Issue 2, 2023 · pp. 10–23 Read article