Person
Golnooshsadat Elhami
This person is no longer with EPFL
Related publications (9)
Uncertain Sampling with Certain Priors
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 ...
EPFL2021Three-Dimensional Cubic Barcodes
Martin Vetterli, Adam James Scholefield, Golnooshsadat Elhami
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 ...
2021Coordinate Difference Matrices
Martin Vetterli, Gilles Baechler, Miranda Krekovic, Golnooshsadat Elhami
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 PUBLICATIONS2020Audio Feature Extraction with Convolutional Neural Autoencoders with Application to Voice Conversion
Feature extraction is a key step in many machine learning and signal processing applications. For speech signals in particular, it is important to derive features that contain both the vocal characteristics of the speaker and the content of the speech. In ...
20192018 FIFA World Cup: The group stage imbalance. Can we do better?
Gilles Baechler, Frederike Dümbgen, Miranda Krekovic, Golnooshsadat Elhami
Synopsis: Create a fair ranking system for the 2018 FIFA World Cup LCAV20171130 Level: BS, MS Description: Imagine that you are given the scores of all the 32 teams participating in the 2 ...
2018Sampling at unknown locations: Uniqueness and reconstruction under constraints
Martin Vetterli, Benjamin Bejar Haro, Adam James Scholefield, Golnooshsadat Elhami, 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 ...
2018Combining Range and Direction for Improved Localization
Martin Vetterli, Adam James Scholefield, Robin Scheibler, Gilles Baechler, Frederike Dümbgen, Miranda Krekovic, Golnooshsadat Elhami
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 ...
2018Unlabeled Sensing: Reconstruction Algorithm and Theoretical Guarantees
Martin Vetterli, Benjamin Bejar Haro, Adam James Scholefield, Golnooshsadat Elhami
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 ...
Ieee2017Unlabeled Sensing
Like other data sensing problems, in unlabeled sensing, the target is to solve the equation y = Φx by finding vector x given a set of sample values in vector y as well as the matrix Φ. However, the main challenge in unlabeled sensing is that the correct or ...
2015