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no code implementations • 10 Oct 2021 • Xiaoyong Ni, Guy Revach, Nir Shlezinger, Ruud J. G. van Sloun, Yonina C. Eldar

The smoothing task is the core of many signal processing applications.

no code implementations • 7 Oct 2021 • Eyar Azar, Satish Mulleti, Yonina C. Eldar

Existing recovery algorithms to recover the signal from its modulo samples operate at a high sampling rate and are not robust in the presence of noise.

no code implementations • 28 Sep 2021 • Yair Ben Sahel, John P. Bryan, Brian Cleary, Samouil L. Farhi, Yonina C. Eldar

Deep algorithm unrolling has emerged as a powerful model-based approach to develop deep architectures that combine the interpretability of iterative algorithms with the performance gains of supervised deep learning, especially in cases of sparse optimization.

no code implementations • 13 Sep 2021 • Tianyi Liu, Andreas M. Tillmann, Yang Yang, Yonina C. Eldar, Marius Pesavento

The second algorithm, referred to as SCAphase, uses auxiliary variables and is favorable in the case of highly diverse mixture models.

no code implementations • 17 Aug 2021 • Haiyang Zhang, Nir Shlezinger, Francesco Guidi, Davide Dardari, Mohammadreza F. Imani, Yonina C. Eldar

Radiating wireless power transfer (WPT) brings forth the possibility to cost-efficiently charge wireless devices without requiring a wiring infrastructure.

no code implementations • 16 Aug 2021 • Fan Liu, Yuanhao Cui, Christos Masouros, Jie Xu, Tony Xiao Han, Yonina C. Eldar, Stefano Buzzi

As the standardization of 5G is being solidified, researchers are speculating what 6G will be.

no code implementations • 15 Aug 2021 • Yuelong Li, Or Bar-Shira, Vishal Monga, Yonina C. Eldar

In this chapter, we review biomedical applications and breakthroughs via leveraging algorithm unrolling, an important technique that bridges between traditional iterative algorithms and modern deep learning techniques.

no code implementations • 13 Aug 2021 • Spencer Markowitz, Corey Snyder, Yonina C. Eldar, Minh N. Do

Background foreground separation (BFS) is a popular computer vision problem where dynamic foreground objects are separated from the static background of a scene.

no code implementations • 23 Jul 2021 • Oded Drori, Alon Mamistvalov, Oren Solomon, Yonina C. Eldar

This huge and promising market is constantly driven by new imaging and processing techniques.

no code implementations • 21 Jul 2021 • Guy Revach, Nir Shlezinger, Xiaoyong Ni, Adria Lopez Escoriza, Ruud J. G. van Sloun, Yonina C. Eldar

Real-time state estimation of dynamical systems is a fundamental task in signal processing and control.

no code implementations • 12 Jul 2021 • Or Bar-Shira, Ahuva Grubstein, Yael Rapson, Dror Suhami, Eli Atar, Keren Peri-Hanania, Ronnie Rosen, Yonina C. Eldar

This study demonstrates the feasibility of in vivo human super resolution, based on a clinical scanner, to increase US specificity for different breast lesions and promotes the use of US in the diagnosis of breast pathologies.

no code implementations • 30 Jun 2021 • Masatoshi Nagahama, Koki Yamada, Yuichi Tanaka, Stanley H. Chan, Yonina C. Eldar

Since the proposed restoration methods are based on iterations of a (convex) optimization algorithm, the method is interpretable and keeps the number of parameters small because we only need to tune graph-independent regularization parameters.

no code implementations • 28 Jun 2021 • Satish Mulleti, Haiyang Zhang, Yonina C. Eldar

Typically, Fourier samples of the FRI signals are used for reconstruction.

no code implementations • 28 Jun 2021 • Dingyou Ma, Nir Shlezinger, Tianyao Huang, Yimin Liu, Yonina C. Eldar

The proposed FMCW-based radar-communications system (FRaC) operates at reduced cost and complexity by transmitting with a reduced number of radio frequency modules, combined with narrowband FMCW signalling.

1 code implementation • 14 Jun 2021 • Theo Diamandis, Yonina C. Eldar, Alireza Fallah, Farzan Farnia, Asuman Ozdaglar

We propose an optimal transport-based framework for MLR problems, Wasserstein Mixed Linear Regression (WMLR), which minimizes the Wasserstein distance between the learned and target mixture regression models.

no code implementations • 27 May 2021 • Haiyang Zhang, Nir Shlezinger, Francesco Guidi, Davide Dardari, Mohammadreza F. Imani, Yonina C. Eldar

As the ability to achieve beam focusing is dictated by the transmit antenna, we study near-field signaling considering different antenna structures, including fully-digital architectures, hybrid phase shifter-based precoders, and the emerging dynamic metasurface antenna (DMA) architecture for massive MIMO arrays.

no code implementations • 5 May 2021 • Sean Thammakhoune, Bariscan Yonel, Eric Mason, Birsen Yazıcı, Yonina C. Eldar

In this paper, we present an approach for ground moving target imaging (GMTI) and velocity recovery using synthetic aperture radar.

no code implementations • 31 Mar 2021 • Tomer Gafni, Nir Shlezinger, Kobi Cohen, Yonina C. Eldar, H. Vincent Poor

Learning in a federated manner differs from conventional centralized machine learning, and poses several core unique challenges and requirements, which are closely related to classical problems studied in the areas of signal processing and communications.

no code implementations • 29 Mar 2021 • Ariel Kroizer, Tirza Routtenberg, Yonina C. Eldar

We show that the proposed sample-GSP estimators outperform the sample-LMMSE estimator for a limited training dataset and that the parametric GSP-LMMSE estimators are more robust to topology changes in the form of adding/removing vertices/edges.

no code implementations • 1 Mar 2021 • Alon Mamistvalov, Yonina C. Eldar

This necessitates sampling rates exceeding the Nyquist rate and the use of a large number of antenna elements to ensure sufficient image quality.

no code implementations • 23 Feb 2021 • Rong Fu, Yimin Liu, Tianyao Huang, Yonina C. Eldar

In this paper, we show that the mutual inhibition matrix of a MHR problem naturally has a Toeplitz structure, which means that the degrees of freedom (DoF) of the matrix can be reduced from a quadratic order to a linear order.

no code implementations • 8 Feb 2021 • Divyansh Jhunjhunwala, Advait Gadhikar, Gauri Joshi, Yonina C. Eldar

Communication of model updates between client nodes and the central aggregating server is a major bottleneck in federated learning, especially in bandwidth-limited settings and high-dimensional models.

1 code implementation • 5 Feb 2021 • Shahin Khobahi, Nir Shlezinger, Mojtaba Soltanalian, Yonina C. Eldar

The need to recover high-dimensional signals from their noisy low-resolution quantized measurements is widely encountered in communications and sensing.

no code implementations • 4 Feb 2021 • Shady Abu-Hussein, Tom Tirer, Se Young Chun, Yonina C. Eldar, Raja Giryes

In the first one, where no explicit prior is used, we show that the proposed approach outperforms other internal learning methods, such as DIP.

no code implementations • 29 Jan 2021 • Fan Liu, Ya-Feng Liu, Ang Li, Christos Masouros, Yonina C. Eldar

We employ the Cram\'er-Rao bound (CRB) as a performance metric of target estimation, under both point and extended target scenarios.

no code implementations • 25 Jan 2021 • Man Luo, Qinghua Guo, Ming Jin, Yonina C. Eldar, Defeng, Huang, Xiangming Meng

Sparse Bayesian learning (SBL) can be implemented with low complexity based on the approximate message passing (AMP) algorithm.

no code implementations • 12 Jan 2021 • Nir Shlezinger, Nariman Farsad, Yonina C. Eldar, Andrea J. Goldsmith

We present an introduction to model-based machine learning for communication systems.

no code implementations • 31 Dec 2020 • Seunghoon Lee, Chanho Park, Song-Nam Hong, Yonina C. Eldar, Namyoon Lee

This paper proposes a Bayesian federated learning (BFL) algorithm to aggregate the heterogeneous quantized gradient information optimally in the sense of minimizing the mean-squared error (MSE).

no code implementations • 30 Dec 2020 • Yuhan Li, Tianyao Huang, Xingyu Xu, Yimin Liu, Yonina C. Eldar

FAR has improved anti-jamming performance over traditional pulse-Doppler radars under complex electromagnetic circumstances.

no code implementations • 22 Dec 2020 • Xiang Liu, Deborah Cohen, Tianyao Huang, Yimin Liu, Yonina C. Eldar

Our method encodes each pulse with a random phase, varying from pulse to pulse, and then processes the received samples jointly to resolve the range ambiguity.

no code implementations • 15 Dec 2020 • Nir Shlezinger, Jay Whang, Yonina C. Eldar, Alexandros G. Dimakis

We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches.

1 code implementation • CVPR 2021 • Yair Kittenplon, Yonina C. Eldar, Dan Raviv

Estimating the 3D motion of points in a scene, known as scene flow, is a core problem in computer vision.

1 code implementation • 18 Nov 2020 • Sandipan Das, Prakash B. Gohain, Alireza M. Javid, Yonina C. Eldar, Saikat Chatterjee

Using a statistical model-based data generation, we develop an experimental setup for the evaluation of neural networks (NNs).

no code implementations • 17 Nov 2020 • Georgios K. Papageorgiou, Mathini Sellathurai, Yonina C. Eldar

The proposed architecture demonstrates enhanced robustness in the presence of noise, and resilience to a small number of snapshots.

1 code implementation • 14 Nov 2020 • Mahdi Boloursaz Mashhadi, Nir Shlezinger, Yonina C. Eldar, Deniz Gunduz

Wireless communications is often subject to channel fading.

no code implementations • 28 Oct 2020 • Daniel Yaron, Daphna Keidar, Elisha Goldstein, Yair Shachar, Ayelet Blass, Oz Frank, Nir Schipper, Nogah Shabshin, Ahuva Grubstein, Dror Suhami, Naama R. Bogot, Eyal Sela, Amiel A. Dror, Mordehay Vaturi, Federico Mento, Elena Torri, Riccardo Inchingolo, Andrea Smargiassi, Gino Soldati, Tiziano Perrone, Libertario Demi, Meirav Galun, Shai Bagon, Yishai M. Elyada, Yonina C. Eldar

Collaborating with several hospitals in Israel we collect a large dataset of CXRs and use this dataset to train a neural network obtaining above 90% detection rate for COVID-19.

no code implementations • 25 Oct 2020 • Alon Mamistvalov, Yonina C. Eldar

Our results pave the way towards wireless US and demonstrate that high resolution US images can be produced using sub-Nyquist sampling and a small number of receiving channels.

Signal Processing Image and Video Processing

no code implementations • 22 Oct 2020 • Bahareh Tolooshams, Satish Mulleti, Demba Ba, Yonina C. Eldar

We propose a learned-structured unfolding neural network for the problem of compressive sparse multichannel blind-deconvolution.

1 code implementation • 21 Oct 2020 • Huy Vu, Gene Cheung, Yonina C. Eldar

While deep learning (DL) architectures like convolutional neural networks (CNNs) have enabled effective solutions in image denoising, in general their implementations overly rely on training data, lack interpretability, and require tuning of a large parameter set.

no code implementations • 14 Oct 2020 • Nicolae-Cătălin Ristea, Andrei Anghel, Radu Tudor Ionescu, Yonina C. Eldar

In autonomous driving, radar systems play an important role in detecting targets such as other vehicles on the road.

no code implementations • 2 Oct 2020 • Huynh Van Luong, Boris Joukovsky, Yonina C. Eldar, Nikos Deligiannis

This paper proposes a new deep-unfolding-based network design for the problem of Robust Principal Component Analysis (RPCA) with application to video foreground-background separation.

no code implementations • 1 Oct 2020 • Feng Xi, Nir Shlezinger, Yonina C. Eldar

One of the reasons for this difficulty stems from the increased cost and power consumption required by analog-to-digital convertors (ADCs) in acquiring the multiple waveforms at the radar receiver.

1 code implementation • 27 Sep 2020 • Tomer Sery, Nir Shlezinger, Kobi Cohen, Yonina C. Eldar

Our analysis reveals the ability of COTAF to achieve a convergence rate similar to that achievable over error-free channels.

no code implementations • 16 Jul 2020 • Neha Jain, Nir Shlezinger, Bhawna Tiwari, Yonina C. Eldar, Anubha Gupta, Vivek Ashok Bohara, Pydi Ganga Bahubalindruni

We analyze the tradeoff between the ability to recover the sampled signal and the energy harvested, and provide guidelines for setting the sampling rate in the light of accuracy and energy constraints.

no code implementations • 1 Jul 2020 • Dawei Gao, Qinghua Guo, Yonina C. Eldar

This work shows that a massive multiple-input multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs) forms a natural extreme learning machine (ELM).

no code implementations • 14 Jun 2020 • Nir Shlezinger, George C. Alexandropoulos, Mohammadreza F. Imani, Yonina C. Eldar, David R. Smith

Next generation wireless base stations and access points will transmit and receive using extremely massive numbers of antennas.

1 code implementation • 5 Jun 2020 • Nir Shlezinger, Mingzhe Chen, Yonina C. Eldar, H. Vincent Poor, Shuguang Cui

We show that combining universal vector quantization methods with FL yields a decentralized training system in which the compression of the trained models induces only a minimum distortion.

no code implementations • 5 Jun 2020 • Nir Shlezinger, Nariman Farsad, Yonina C. Eldar, Andrea J. Goldsmith

Learned factor graph can be realized using compact neural networks that are trainable using small training sets, or alternatively, be used to improve upon existing deep inference systems.

no code implementations • 1 Jun 2020 • Siheng Chen, Yonina C. Eldar, Lingxiao Zhao

We unroll an iterative denoising algorithm by mapping each iteration into a single network layer where the feed-forward process is equivalent to iteratively denoising graph signals.

1 code implementation • 10 May 2020 • Sharan Ramjee, Shengtai Ju, Diyu Yang, Xiaoyu Liu, Aly El Gamal, Yonina C. Eldar

Subsampling of received wireless signals is important for relaxing hardware requirements as well as the computational cost of signal processing algorithms that rely on the output samples.

1 code implementation • 20 Apr 2020 • Gili Dardikman-Yoffe, Yonina C. Eldar

The use of photo-activated fluorescent molecules to create long sequences of low emitter-density diffraction-limited images enables high-precision emitter localization, but at the cost of low temporal resolution.

Image and Video Processing

no code implementations • 23 Mar 2020 • Dingyou Ma, Nir Shlezinger, Tianyao Huang, Yariv Shavit, Moshe Namer, Yimin Liu, Yonina C. Eldar

For the radar subsystem, our experiments show that the spatial agility induced by the GSM transmission improves the angular resolution and reduces the sidelobe level in the transmit beam pattern compared to using fixed antenna allocations.

no code implementations • 9 Mar 2020 • Yuichi Tanaka, Yonina C. Eldar, Antonio Ortega, Gene Cheung

In this article, we review current progress on sampling over graphs focusing on theory and potential applications.

no code implementations • 14 Feb 2020 • Nariman Farsad, Nir Shlezinger, Andrea J. Goldsmith, Yonina C. Eldar

The design of symbol detectors in digital communication systems has traditionally relied on statistical channel models that describe the relation between the transmitted symbols and the observed signal at the receiver.

1 code implementation • 8 Feb 2020 • Nir Shlezinger, Rong Fu, Yonina C. Eldar

In multiuser multiple-input multiple-output (MIMO) setups, where multiple symbols are simultaneously transmitted, accurate symbol detection is challenging.

no code implementations • 31 Jan 2020 • Nir Shlezinger, Nariman Farsad, Yonina C. Eldar, Andrea J. Goldsmith

In particular, we propose to use machine learning (ML) tools to learn the factor graph, instead of the overall system task, which in turn is used for inference by message passing over the learned graph.

no code implementations • 5 Jan 2020 • Regev Cohen, Yonina C. Eldar

To that end, we introduce a fractal array design in which a generator array is recursively expanded according to its difference coarray.

no code implementations • 22 Dec 2019 • Vishal Monga, Yuelong Li, Yonina C. Eldar

In this article, we review algorithm unrolling for signal and image processing.

no code implementations • 1 Aug 2019 • Nir Shlezinger, Yonina C. Eldar

In this work we design data-driven task-oriented quantization systems with scalar ADCs, which determine how to map an analog signal into its digital representation using deep learning tools.

no code implementations • 25 Jul 2019 • Yun Liao, Nariman Farsad, Nir Shlezinger, Yonina C. Eldar, Andrea J. Goldsmith

This paper proposes to use a deep neural network (DNN)-based symbol detector for mmWave systems such that CSI acquisition can be bypassed.

no code implementations • 5 Jul 2019 • Ruud JG van Sloun, Regev Cohen, Yonina C. Eldar

We consider deep learning strategies in ultrasound systems, from the front-end to advanced applications.

no code implementations • 26 May 2019 • Nir Shlezinger, Nariman Farsad, Yonina C. Eldar, Andrea J. Goldsmith

Our numerical evaluations demonstrate that the performance of ViterbiNet, which is ignorant of the CSI, approaches that of the CSI-based Viterbi algorithm, and is capable of tracking time-varying channels without needing instantaneous CSI or additional training data.

1 code implementation • 16 May 2019 • Xiwen Zhang, Tolunay Seyfi, Shengtai Ju, Sharan Ramjee, Aly El Gamal, Yonina C. Eldar

We study the problem of interference source identification, through the lens of recognizing one of 15 different channels that belong to 3 different wireless technologies: Bluetooth, Zigbee, and WiFi.

no code implementations • 14 May 2019 • Yonina C. Eldar, Jerry Li, Cameron Musco, Christopher Musco

In addition to results that hold for any Toeplitz $T$, we further study the important setting when $T$ is close to low-rank, which is often the case in practice.

no code implementations • 16 Apr 2019 • Satoshi Takabe, Tadashi Wadayama, Yonina C. Eldar

Complex-field signal recovery problems from noisy linear/nonlinear measurements appear in many areas of signal processing and wireless communications.

no code implementations • 9 Feb 2019 • Yuelong Li, Mohammad Tofighi, Junyi Geng, Vishal Monga, Yonina C. Eldar

We then unroll the algorithm to construct a neural network for image deblurring which we refer to as Deep Unrolling for Blind Deblurring (DUBLID).

no code implementations • 9 Feb 2019 • Yuelong Li, Mohammad Tofighi, Vishal Monga, Yonina C. Eldar

We first present an iterative algorithm that may be considered a generalization of the traditional total-variation regularization method on the gradient domain, and subsequently unroll the half-quadratic splitting algorithm to construct a neural network.

no code implementations • 28 Jan 2019 • Alexander Jung, Alfred O. Hero III, Alexandru Mara, Saeed Jahromi, Ayelet Heimowitz, Yonina C. Eldar

This lends naturally to learning the labels by total variation (TV) minimization, which we solve by applying a recently proposed primal-dual method for non-smooth convex optimization.

1 code implementation • 16 Jan 2019 • Sharan Ramjee, Shengtai Ju, Diyu Yang, Xiaoyu Liu, Aly El Gamal, Yonina C. Eldar

We then study algorithms to reduce the training time by minimizing the size of the training data set, while incurring a minimal loss in classification accuracy.

no code implementations • 30 Nov 2018 • Shahin Khobahi, Naveed Naimipour, Mojtaba Soltanalian, Yonina C. Eldar

Machine learning, and more specifically deep learning, have shown remarkable performance in sensing, communications, and inference.

no code implementations • 20 Nov 2018 • Oren Solomon, Regev Cohen, Yi Zhang, Yi Yang, He Qiong, Jianwen Luo, Ruud J. G. van Sloun, Yonina C. Eldar

We compare the performance of the suggested deep network on both simulations and in-vivo rat brain scans, with a commonly practiced deep-network architecture and the fast iterative shrinkage algorithm, and show that our architecture exhibits better image quality and contrast.

no code implementations • 1 Nov 2018 • Luiz. F. O. Chamon, Yonina C. Eldar, Alejandro Ribeiro

Even if they are, recovering sparse solutions using convex relaxations requires assumptions that may be hard to meet in practice.

1 code implementation • 26 Jun 2018 • Pingfan Song, Lior Weizman, Joao F. C. Mota, Yonina C. Eldar, Miguel R. D. Rodrigues

In this paper, we propose a Coupled Dictionary Learning based multi-contrast MRI reconstruction (CDLMRI) approach to leverage an available guidance contrast to restore the target contrast.

no code implementations • 24 May 2018 • Fabio Giovanneschi, Kumar Vijay Mishra, Maria Antonia Gonzalez-Huici, Yonina C. Eldar, Joachim H. G. Ender

For the case of abandoned anti-personnel landmines classification, we compare the performance of K-SVD with three online algorithms: classical Online Dictionary Learning, its correlation-based variant, and DOMINODL.

no code implementations • 17 May 2018 • Chuang Wang, Yonina C. Eldar, Yue M. Lu

In addition to providing asymptotically exact predictions of the dynamic performance of the algorithms, our high-dimensional analysis yields several insights, including an asymptotic equivalence between Oja's method and GROUSE, and a precise scaling relationship linking the amount of missing data to the signal-to-noise ratio.

no code implementations • 13 May 2018 • Zhihui Zhu, Daniel Soudry, Yonina C. Eldar, Michael B. Wakin

We examine the squared error loss landscape of shallow linear neural networks.

no code implementations • 20 Apr 2018 • Ruud J. G. van Sloun, Oren Solomon, Matthew Bruce, Zin Z. Khaing, Hessel Wijkstra, Yonina C. Eldar, Massimo Mischi

This method, which we term Deep Ultrasound Localization Microscopy (Deep-ULM), exploits modern deep learning strategies and employs a convolutional neural network to perform localization microscopy in dense scenarios.

no code implementations • 27 Feb 2018 • Ahmet M. Elbir, Kumar Vijay Mishra, Yonina C. Eldar

Direction of arrival (DoA) estimation of targets improves with the number of elements employed by a phased array radar antenna.

no code implementations • 3 Dec 2017 • Qing Qu, Yuqian Zhang, Yonina C. Eldar, John Wright

We study the convolutional phase retrieval problem, of recovering an unknown signal $\mathbf x \in \mathbb C^n $ from $m$ measurements consisting of the magnitude of its cyclic convolution with a given kernel $\mathbf a \in \mathbb C^m $.

no code implementations • 29 Aug 2017 • Shahar Tsiper, Or Dicker, Idan Kaizerman, Zeev Zohar, Mordechai Segev, Yonina C. Eldar

The scanning electron microscope (SEM) produces an image of a sample by scanning it with a focused beam of electrons.

no code implementations • 30 May 2016 • Raja Giryes, Yonina C. Eldar, Alex M. Bronstein, Guillermo Sapiro

Solving inverse problems with iterative algorithms is popular, especially for large data.

no code implementations • 26 May 2016 • Gang Wang, Georgios B. Giannakis, Yonina C. Eldar

This paper presents a new algorithm, termed \emph{truncated amplitude flow} (TAF), to recover an unknown vector $\bm{x}$ from a system of quadratic equations of the form $y_i=|\langle\bm{a}_i,\bm{x}\rangle|^2$, where $\bm{a}_i$'s are given random measurement vectors.

no code implementations • 6 Feb 2016 • Andreas M. Tillmann, Yonina C. Eldar, Julien Mairal

We propose a new algorithm to learn a dictionary for reconstructing and sparsely encoding signals from measurements without phase.

no code implementations • 14 Nov 2015 • Zhuoran Yang, Zhaoran Wang, Han Liu, Yonina C. Eldar, Tong Zhang

To recover $\beta^*$, we propose an $\ell_1$-regularized least-squares estimator.

no code implementations • 20 Jul 2015 • Alexander Jung, Yonina C. Eldar, Norbert Görtz

The main conceptual contribution of this paper is the adaption of the information-theoretic approach to minimax estimation for the DL problem in order to derive lower bounds on the worst case MSE of any DL scheme.

no code implementations • 10 Jul 2014 • Lior Weizman, Yonina C. Eldar, Dafna Ben Bashat

Methods: The proposed approach utilizes the possible similarity of the repeated scans in longitudinal MRI studies.

no code implementations • 17 Feb 2014 • Alexander Jung, Yonina C. Eldar, Norbert Görtz

We consider the problem of dictionary learning under the assumption that the observed signals can be represented as sparse linear combinations of the columns of a single large dictionary matrix.

no code implementations • 20 Mar 2013 • Henrik Ohlsson, Yonina C. Eldar, Allen Y. Yang, S. Shankar Sastry

The problem is of great importance in many applications and is typically solved by maximizing the cross-correlation between the two signals.

no code implementations • 25 Aug 2010 • Yonina C. Eldar, Deanna Needell

The Kaczmarz method is an algorithm for finding the solution to an overdetermined consistent system of linear equations Ax=b by iteratively projecting onto the solution spaces.

Numerical Analysis

no code implementations • 31 May 2000 • Yonina C. Eldar, G. David Forney Jr

In this paper we consider the problem of constructing measurements optimized to distinguish between a collection of possibly non-orthogonal quantum states.

Quantum Physics

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