Publications de Golnooshsadat Elhami

Three-Dimensional Cubic Barcodes

Martin Vetterli, Golnooshsadat Elhami, Adam James Scholefield

We consider three-dimensional cubic barcodes, consisting of smaller cubes, each built from one of two possible materials and carry one bit of information. To retrieve the information stored in the barcode, we measure a 2-D projection of the barcode using a ...

2022

Uncertain Sampling with Certain Priors

Golnooshsadat Elhami

Sampling has always been at the heart of signal processing providing a bridge between the analogue world and discrete representations of it, as our ability to process data in continuous space is quite limited. Furthermore, sampling plays a key part in unde ...

EPFL2021

Coordinate Difference Matrices

Martin Vetterli, Gilles Baechler, Golnooshsadat Elhami, Miranda Krekovic

In many problems such as phase retrieval, molecular biology, source localization, and sensor array calibration, one can measure vector differences between pairs of points and attempt to recover the position of these points; this class of problems is called ...

SIAM PUBLICATIONS2020

Shape From Bandwidth: Central Projection Case

Martin Vetterli, Golnooshsadat Elhami, Adam James Scholefield

Consider an unknown surface painted with a band-limited texture. We show that only the knowledge of the bandwidth of the texture is enough to estimate the shape of the surface from a single image taken by a camera. We model the problem as a central project ...

IEEE2020

Combining Range and Direction for Improved Localization

Martin Vetterli, Robin Scheibler, Gilles Baechler, Frederike Dümbgen, Golnooshsadat Elhami, Miranda Krekovic, Adam James Scholefield

Self-localization of nodes in a sensor network is typically achieved using either range or direction measurements; in this paper, we show that a constructive combination of both improves the estimation. We propose two localization algorithms that make use ...

2018

Sampling at unknown locations: Uniqueness and reconstruction under constraints

Martin Vetterli, Golnooshsadat Elhami, Benjamin Bejar Haro, Adam James Scholefield, Michalina Wanda Pacholska

Traditional sampling results assume that the sample locations are known. Motivated by simultaneous localization and mapping (SLAM) and structure from motion (SfM), we investigate sampling at unknown locations. Without further constraints, the problem is of ...

2018

Unlabeled Sensing: Reconstruction Algorithm and Theoretical Guarantees

Martin Vetterli, Golnooshsadat Elhami, Benjamin Bejar Haro, Adam James Scholefield

It often happens that we are interested in reconstructing an unknown signal from partial measurements. Also, it is typically assumed that the location (temporal or spatial) of the samples is known and that the only distortion present in the observations is ...

Ieee2017