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Creators/Authors contains: "Pallickara, Sangmi Lee"

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  1. ; ; ; ; (, IEEE)
    Free, publicly-accessible full text available May 5, 2026
  2. ; ; ; ; ; ; (, IEEE)
    Free, publicly-accessible full text available May 19, 2026
  3. ; ; ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    Masked autoencoders employ random masking to effectively reconstruct input images using self-supervised techniques, which allows for efficient training on large datasets. However, the random masking strategy does not adequately tap into information encapsulated within high-dimensional hyperspectral satellite imagery that is used in several domains. We propose a novel masking strategy, HOGMAE, based on the Histogram of Oriented Gradients that incorporates rich information inherent within satellite images during the mask creation step. Our experiments, over a hyperspectral satellite dataset, demonstrate the effectiveness of our methodology. 
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    Free, publicly-accessible full text available April 11, 2026
  4. ; ; (, IEEE)
    Free, publicly-accessible full text available December 16, 2025
  5. ; ; ; (, IEEE)
    Free, publicly-accessible full text available December 16, 2025
  6. ; ; ; ; (, IEEE)
    Free, publicly-accessible full text available December 15, 2025
  7. ; ; (, ACM)
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  8. ; ; ; (, IEEE)
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  9. ; ; ; (, IEEE)
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  10. ; ; ; (, 2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid))
    Gridded spatial datasets arise naturally in environmental, climatic, meteorological, and ecological settings. Each grid point encapsulates a vector of variables representing different measures of interest. Gridded datasets tend to be voluminous since they encapsulate observations for long timescales. Visualizing such datasets poses significant challenges stemming from the need to preserve interactivity, manage I/O overheads, and cope with data volumes. Here we present our methodology to significantly alleviate I/O requirements by leveraging deep neural network-based models and a distributed, in-memory cache to facilitate interactive visualizations. Our benchmarks demonstrate that deploying our lightweight models coupled with back-end caching and prefetching schemes can reduce the client's query response time by 92.3% while maintaining a high perceptual quality with a PSNR (peak signal-to-noise ratio) of 38.7 dB. 
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