arXiv subjects

Sagnik Ghosh

Publications and source records attributed to Sagnik Ghosh.

6 recordsLinked to original sources

arXiv2026

Predicting Lethal Outcome (Cause) And Understanding Key Biomarkers Linked With Acute Myocardial Infarction Using Deep Artificial Neural Network And Ensemble Of Machine Learning Methodologies

Cardiovascular disease is still one of the main causes of death around the world. Acute myocardial infarction (MI), or heart attack, claims millions of lives each year. MI happens when blood flow to the coronary arteries is blocked or reduced, which causes permanent damage to the heart muscle. Without treatment, this can lead to cardiac arrest, where the heart stops pumping blood to the organs, resulting in organ failure and death. Even survivors often face serious problems like heart failure, pulmonary edema, and asystole. Research shows that 5 to 10 percent of survivors die within the first year after an MI, and nearly half need to be hospitalized again. Early thrombolytic treatment leads to better outcomes, so there is a clear need for faster and more accurate ways to diagnose MI. Right now, doctors usually review patient history and use their own experience to find the causes of MI. This process takes a lot of time and can be inconsistent. Detecting MI accurately and quickly can help patients take better care of themselves and prevent fatal events. In this study, we introduce an automated model to predict deadly outcomes of MI and help doctors understand important biomarkers linked to its complications. This approach aims to make diagnosis clearer, faster, and more affordable. The process includes preparing the data, filling in missing values, and handling imbalanced data using SVMSMOTE, ADASYN, and class-weighted methods. We use wrapper and embedded feature selection to find the most important variables, then scale the features for consistency. The model combines Logistic Regression, Random Forest, Light-GBM, and Bagging SVM, and is further improved with an artificial neural network to increase accuracy. We evaluate all models using precision, recall, and other key measures to find the best option for clinical use.

eess.IV↗

arXiv2025

VibraWave: Sensing the Pulse of Polluted Waters

Conventional methods for water pollutant detection, such as chemical assays and optical spectroscopy, are often invasive, expensive, and unsuitable for real-time, portable monitoring. In this paper, we introduce VibraWave, a novel non-invasive sensing framework that combines mmWave radar with controlled acoustic excitation, tensor decomposition, and deep learning to detect and quantify a wide range of water pollutants. By capturing radar reflections as a three-dimensional tensor encoding phase dynamics, range bin power, and angle-of-arrival (AoA), we apply PARAFAC decomposition with non-negative constraints to extract compact, interpretable pollutant fingerprints. These are used to train a lightweight student neural network via knowledge distillation, enabling joint classification and quantification of heavy metals (Cu, Fe, Mg), oil emulsions, and sediments. Extensive experiments show that VibraWave achieves high accuracy and low RMSE across pure, binary, and tertiary mixtures, while remaining robust and computationally efficient, making it well-suited for scalable, real-time water quality monitoring.

eess.SY↗

arXiv2023

Single-stage few-cycle pulse amplification

Kerr instability can be exploited to amplify visible, near-, and mid-infrared ultrashort pulses. We use the results of Kerr instability amplification theory to inform our simulations amplifying few-cycle pulses. We show that the amplification angle-dependence is simplified to the phase matching condition of four-wave mixing when the intense pump is considered. Seeding with few-cycle pulses near the pump leads to broadband amplification without spatial chirp, while longer pulses undergo compression through amplification. Pumping in the mid-IR leads to multi-octave spanning amplified pulses with single-cycle duration not previously predicted. We discuss limitations of the amplification process and optimizing pump and seed parameters to maximize amplification.

physics.optics↗

arXiv2023

Supercontinuum amplification by Kerr instability

The versatility of optical parametric amplifiers make them excellent sources for next-generation ultrashort strong-field physics experiments, however phase matching considerations limit the available bandwidth. We demonstrate supercontinuum amplification in a four-wave optical parametric amplifier by Kerr instability. Experimentally, we amplify spectra that span nearly an octave without spatial chirp, with two-cycle transform limited pulse duration. We also theoretically explore regimes that demonstrate single-cycle amplification in the near infrared.

physics.optics↗

arXiv2021

Position-Dependent Diffusion induced Non-monotonic decay of Certain Non-Equilibrium Phenomena in Condensed Phase

The dynamics of various optically controlled non-equilibrium phenomena in the condensed phase are studied using the Liouville equation. We study a projection of the same in a slow moving coordinate, identified as the Reaction Coordinate approach, with a position dependent diffusion coefficient. Introduction of position dependence is shown to induce non-monotonicity in relaxations of certain Non-equilibrium correlation functions, previously unexplored in the theoretical as well as experimental studies. This is in contrast to the exponential relaxation of its position independent analogue, irrespective of initial conditions. We characterize the dependence of this non-monotonicity on the strength of spatial inhomogeneity of diffusion and on the strength of the restoring forces and also indicate ranges of combinations where this feature is exhibited to pave the way for its experimental detection.

quant-ph↗

arXiv2021

A Path Integral approach to Quantum Fluid Dynamics

In this work we develop an alternative approach for solution of Quantum Trajectories using the Path Integral method. The state-of-the-art technique in the field is to solve a set of non-linear, coupled partial differential equations (PDEs) simultaneously. We opt for a fundamentally different route. We first derive a general closed form expression for the Path Integral propagator valid for any general potential as a functional of the corresponding classical path. The method is exact and is applicable in many dimensions as well as multi-particle cases. This, then, is used to compute the Quantum Potential (QP), which, in turn, can generate the Quantum Trajectories. For cases, where closed form solution is not possible, the problem is formally boiled down to solving the classical path as a boundary value problem. The work formally bridges the Path Integral approach with Quantum Fluid Dynamics. As a model application to illustrate the method, we work out a toy model viz. the double-well potential, where the boundary value problem for the classical path has been computed perturbatively, but the Quantum part is left exact. Using this we delve into seeking insight in one of the long standing debates with regard to Quantum Tunneling.

quant-ph↗