Don't take it lightly: Phasing optical random projections with unknown operators
Authors: Sidharth Gupta, Remi Gribonval, Laurent Daudet, Ivan Dokmanić
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test the proposed MPR algorithm via simulations and experiments on a real OPU. |
| Researcher Affiliation | Collaboration | Sidharth Gupta University of Illinois at Urbana-Champaign gupta67@illinois.edu Rémi Gribonval Univ Rennes, Inria, CNRS, IRISA remi.gribonval@inria.fr Laurent Daudet Light On, Paris laurent@lighton.ai Ivan Dokmani c University of Illinois at Urbana-Champaign dokmanic@illinois.edu |
| Pseudocode | Yes | Algorithm 1 MPR algorithm for S frames. |
| Open Source Code | Yes | Reproducible code available at https://github.com/swing-research/opu_phase under the MIT License. |
| Open Datasets | Yes | Next, we take 500 28 x 28 samples from the MNIST dataset [13], threshold them to be binary, vectorize them, and stack them into a matrix B ∈ R500x282. |
| Dataset Splits | No | The paper does not provide specific training/validation/test dataset splits for the MNIST dataset used. |
| Hardware Specification | No | The paper mentions using a 'publicly available cloud-based OPU' and describes components like DMD and cameras, but it does not specify concrete hardware details such as CPU/GPU models, memory, or processor types used for computation. |
| Software Dependencies | No | The paper mentions 'scikit-learn interface' and 'Python' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | In all simulations, intensity measurements are quantized to 8 bits and all signals and references are iid standard (complex) Gaussian random vectors. We use 5 anchors in all RSVD experiments. The empirically determined sensitivity threshold of the camera is τ = 6. |