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Soma Bandyopadhyay

5 accepted papers

2025

Generative Model based Optical Response Prediction for Plasmonic Sensing

ICASSP 2025accepted

In recent times, plasmonic sensing is widely used for detecting tiny particles (micro / nano-scale) and plays a crucial role in diverse application domains such as sustainability, healthcare etc. In current scenario, numerical simulators are used for predicting the optical response of a plasmonic na…

Cited by 0SourceScholar
2018

Effective Noise Removal and Unified Model of Hybrid Feature Space Optimization for Automated Cardiac Anomaly Detection Using Phonocardiogarm Signals

ICASSP 2018accepted

In this paper, we present completely automated cardiac anomaly detection for remote screening of cardio-vascular abnormality using Phonocardiogram (PCG) or heart sound signal. Even though PCG contains significant and vital cardiac health information and cardiac abnormality signature, the presence of…

Cited by 0SourceScholar
2017

Heartmate: automated integrated anomaly analysis for effective remote cardiac health management

ICASSP 2017accepted

Remote cardiac health management is an important healthcare application. We have developed Heartmate that enables basic screening of cardiac health using low cost sensors or smartphone-inbuilt sensors without manual intervention. It consists of robust denoising algorithm along with effective anomaly…

Cited by 0SourceScholar
2016

Heart-trend: An affordable heart condition monitoring system exploiting morphological pattern

ICASSP 2016accepted

In this paper we leverage the power of smartphone to enable proactive in-house heart condition monitoring. We introduce Heart-Trend, a nonparametric model to analyze and detect heart abnormality conditions like arrhythmia from photoplethysmogram (PPG) signal. It does on-demand heart status monitorin…

Cited by 0SourceScholar
2015

Adaptive Sensor Data Compression in IoT systems: Sensor data analytics based approach

ICASSP 2015accepted

Sensor nodes are embodiment of IoT systems in microscopic level. As the volume of sensor data increases exponentially, data compression is essential for storage, transmission and in-network processing. The compression performance to realize significant gain in processing high volume sensor data cann…

Cited by 0SourceScholar