Search Results for author: Chinthaka Dinesh

Found 5 papers, 0 papers with code

Complex Graph Laplacian Regularizer for Inferencing Grid States

no code implementations4 Jul 2023 Chinthaka Dinesh, Junfei Wang, Gene Cheung, Pirathayini Srikantha

In order to maintain stable grid operations, system monitoring and control processes require the computation of grid states (e. g. voltage magnitude and angles) at high granularity.

No-Box Attacks on 3D Point Cloud Classification

no code implementations19 Oct 2022 Hanieh Naderi, Chinthaka Dinesh, Ivan V. Bajic, Shohreh Kasaei

To this end, we define 14 point cloud features and use multiple linear regression to examine whether these features can be used for adversarial point prediction, and which combination of features is best suited for this purpose.

3D Point Cloud Classification Classification +2

Efficient Signed Graph Sampling via Balancing & Gershgorin Disc Perfect Alignment

no code implementations18 Aug 2022 Chinthaka Dinesh, Gene Cheung, Saghar Bagheri, Ivan V. Bajic

Experimental results show that our signed graph sampling method outperformed existing fast sampling schemes noticeably on various datasets.

Graph Sampling

Unsupervised Graph Spectral Feature Denoising for Crop Yield Prediction

no code implementations4 Aug 2022 Saghar Bagheri, Chinthaka Dinesh, Gene Cheung, Timothy Eadie

Prediction of annual crop yields at a county granularity is important for national food production and price stability.

Crop Yield Prediction Denoising +1

Point Cloud Sampling via Graph Balancing and Gershgorin Disc Alignment

no code implementations10 Mar 2021 Chinthaka Dinesh, Gene Cheung, Ivan Bajic

Specifically, to articulate a sampling objective, we first assume a super-resolution (SR) method based on feature graph Laplacian regularization (FGLR) that reconstructs the original high-resolution PC, given 3D points chosen by a sampling matrix $\H$.

Graph Sampling Object Recognition +1

Cannot find the paper you are looking for? You can Submit a new open access paper.