Soumik Sarkar

TL;DR: This study establishes a template for deploying multitrait phenomic prediction that is easily amendable to any crop species and any breeding objective and illuminated the capability of fusing ML and optimization techniques to identify a suite of in-season phenomic traits that will allow breeding programs to decrease the dependence on resource-intensive end- season phenotyping.

TL;DR: A spatiotemporal pattern network (STPN) strategy built on symbolic dynamic filtering (SDF) is proposed to explore spatiotmporal behaviors in a bridge network and demonstrates increased sensitivity in detecting damages and higher reliability in quantifying the damage level with increase in sensor network density.

TL;DR: This paper proposes a feature extraction and fusion methodology to perform fault detection & classification in distributed physical processes generating heterogeneous data to achieve high reliability via retaining the essential spatiotemporal characteristics of the physical processes.

TL;DR: In this paper, an anomaly detection algorithm for condition monitoring of nuclear power plants, where symbolic feature extraction and the associated pattern classification are optimized by appropriate partitioning of (possibly noise-contaminated) sensor time series.

TL;DR: An endto-end deep convolutional selective autoencoder approach to capture the rich information in hi-speed flame video for instability prognostics to effectively detect subtle instability features as a combustion process makes transition from stable to unstable region.