7 citations · 14 across the 6 of their papers we have counts for
6 papers
3D Convolutional Selective Autoencoder For Instability Detection in Combustion Systems
Tryambak Gangopadhyay, Vikram Ramanan, Adedotun Akintayo +4
While analytical solutions of critical (phase) transitions in physical systems are abundant for simple nonlinear systems, such analysis remains intractable for real-life dynamical…
Hierarchical Symbolic Dynamic Filtering of Streaming Non-stationary Time Series Data
Adedotun Akintayo, Soumik Sarkar
This paper proposes a hierarchical feature extractor for non-stationary streaming time series based on the concept of switching observable Markov chain models. The slow time-scale…
Energy Prediction using Spatiotemporal Pattern Networks
Zhanhong Jiang, Chao Liu, Adedotun Akintayo +2
This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dy…
A Bayesian Network approach to County-Level Corn Yield Prediction using historical data and expert knowledge
Vikas Chawla, Hsiang Sing Naik, Adedotun Akintayo +4
Crop yield forecasting is the methodology of predicting crop yields prior to harvest. The availability of accurate yield prediction frameworks have enormous implications from multi…
An end-to-end convolutional selective autoencoder approach to Soybean Cyst Nematode eggs detection
Adedotun Akintayo, Nigel Lee, Vikas Chawla +7
This paper proposes a novel selective autoencoder approach within the framework of deep convolutional networks. The crux of the idea is to train a deep convolutional autoencoder to…
LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement
Kin Gwn Lore, Adedotun Akintayo, Soumik Sarkar
In surveillance, monitoring and tactical reconnaissance, gathering the right visual information from a dynamic environment and accurately processing such data are essential ingredi…