21 research outputs found

    Euclidean Distance Matrices: Essential Theory, Algorithms and Applications

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    Euclidean distance matrices (EDM) are matrices of squared distances between points. The definition is deceivingly simple: thanks to their many useful properties they have found applications in psychometrics, crystallography, machine learning, wireless sensor networks, acoustics, and more. Despite the usefulness of EDMs, they seem to be insufficiently known in the signal processing community. Our goal is to rectify this mishap in a concise tutorial. We review the fundamental properties of EDMs, such as rank or (non)definiteness. We show how various EDM properties can be used to design algorithms for completing and denoising distance data. Along the way, we demonstrate applications to microphone position calibration, ultrasound tomography, room reconstruction from echoes and phase retrieval. By spelling out the essential algorithms, we hope to fast-track the readers in applying EDMs to their own problems. Matlab code for all the described algorithms, and to generate the figures in the paper, is available online. Finally, we suggest directions for further research.Comment: - 17 pages, 12 figures, to appear in IEEE Signal Processing Magazine - change of title in the last revisio

    Phase Retrieval for Sparse Signals: Uniqueness Conditions

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    In a variety of fields, in particular those involving imaging and optics, we often measure signals whose phase is missing or has been irremediably distorted. Phase retrieval attempts the recovery of the phase information of a signal from the magnitude of its Fourier transform to enable the reconstruction of the original signal. A fundamental question then is: "Under which conditions can we uniquely recover the signal of interest from its measured magnitudes?" In this paper, we assume the measured signal to be sparse. This is a natural assumption in many applications, such as X-ray crystallography, speckle imaging and blind channel estimation. In this work, we derive a sufficient condition for the uniqueness of the solution of the phase retrieval (PR) problem for both discrete and continuous domains, and for one and multi-dimensional domains. More precisely, we show that there is a strong connection between PR and the turnpike problem, a classic combinatorial problem. We also prove that the existence of collisions in the autocorrelation function of the signal may preclude the uniqueness of the solution of PR. Then, assuming the absence of collisions, we prove that the solution is almost surely unique on 1-dimensional domains. Finally, we extend this result to multi-dimensional signals by solving a set of 1-dimensional problems. We show that the solution of the multi-dimensional problem is unique when the autocorrelation function has no collisions, significantly improving upon a previously known result.Comment: submitted to IEEE TI

    Relax and Unfold: Microphone Localization with Euclidean Distance Matrices

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    Recent methods for localization of microphones in a microphone array exploit sound sources at a priori unknown locations. This is convenient for ad-hoc arrays, as it requires little additional infrastructure. We propose a flexible localization algorithm by first recognizing the problem as an instance of multidimensional unfolding (MDU)—a classical problem in Euclidean geometry and psychometrics—and then solving the MDU as a special case of Euclidean distance matrix (EDM) completion. We solve the EDM completion using a semidefinite relaxation. In contrast to existing methods, the semidefinite formulation allows us to elegantly handle missing pairwise distance information, but also to incorporate various prior information about the distances between the pairs of microphones or sources, bounds on these distances, or ordinal information such as “microphones 1 and 2 are more apart than microphones 1 and 15”. The intuition that this should improve the localization performance is confirmed by numerical experiments

    Assessing printability of a very-large-scale integration design

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    Printability of a very-large-scale integration design is assessed by: during a training phase, generating a training set of very-large-scale integration design shapes representative of a population of very-large-scale integration design shapes, obtaining a set of mathematical representations of respective shapes in the training set, identifying at least two classes of physical events causally linked to the printability for the very-large-scale integration design shapes, each of the classes being associated to a respective level of printability, labeling each mathematical representation of the set according to one of the identified classes, based on a lithography model, and selecting a probabilistic model function maximizing a probability of a class, given the set of mathematical representations; and during a testing phase, providing a very-large-scale integration design shape to be tested, testing the provided very-large-scale integration design shape, and labeling the provided very-large-scale integration design shape according to the identified class

    DASS: Distributed Adaptive Sparse Sensing

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    Near-optimal thermal monitoring framework for many-core systems on chip

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    Chip designers place on-chip thermal sensors to measure local temperatures, thus preventing thermal runaway situations in many-core processing architectures. However, the quality of the thermal reconstruction is directly dependent on the number of placed sensors, which should be minimized, while guaranteeing full detection of all the worst case temperature gradient. In this paper, we present an entire framework for the thermal management of complex many-core architectures, such that we can precisely recover the thermal distribution from a minimal number of sensors. The proposed sensor placement algo- rithm is guaranteed to reduce the impact of noisy measurements on the reconstructed thermal distribution. We achieve significant improvements compared to the state of the art, in terms of both computational complexity and reconstruction precision. For example, if we consider a 64 cores SoC with 64 noisy sensors (σ^2 = 4), we achieve an average reconstruction error of 1.5C, that is less than the half of what previous state-of-the-art methods achieve. We also study the practical limits of the proposed method and show that we do not need realistic workloads to learn the model and efficiently place the sensors. In fact, we show that the reconstruction error is not significantly increased if we randomly generate the power-traces of the components or if we have just a part of the correct workload

    Super Resolution Phase Retrieval for Sparse Signals

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    In a variety of fields, in particular those involving imaging and optics, we often measure signals whose phase is missing or has been irremediably distorted. Phase retrieval attempts to recover the phase information of a signal from the magnitude of its Fourier transform to enable the reconstruction of the original signal. Solving the phase retrieval problem is equivalent to recovering a signal from its auto-correlation function. In this paper, we assume the original signal to be sparse; this is a natural assumption in many applications, such as X-ray crystallography, speckle imaging and blind channel estimation. We propose an algorithm that resolves the phase retrieval problem in three stages: i) we leverage the finite rate of innovation sampling theory to super-resolve the auto-correlation function from a limited number of samples, ii) we design a greedy algorithm that identifies the locations of a sparse solution given the super-resolved auto-correlation function, iii) we recover the amplitudes of the atoms given their locations and the measured auto-correlation function. Unlike traditional approaches that recover a discrete approximation of the underlying signal, our algorithm estimates the signal on a continuous domain, which makes it the first of its kind. Along with the algorithm, we derive its performance bound with a theoretical analysis and propose a set of enhancements to improve its computational complexity and noise resilience. Finally, we demonstrate the benefits of the proposed method via a comparison against Charge Flipping, a notable algorithm in crystallography

    The Fukushima Inverse Problem

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    Knowing what amount of radioactive material was released from Fukushima in March 2011 and at what time instants is crucial to assess the risk, the pollution, and to understand the scope of the consequences. Moreover, it could be used in forward simulations to obtain accurate maps of deposition. But these data are often not publicly available. We propose to estimate the emission waveforms by solving an inverse problem. Previous approaches have relied on a detailed expert guess of how the releases appeared, and they produce a solution strongly biased by this guess. If we plant a nonexistent peak in the guess, the solution also exhibits a nonexistent peak. We pro- pose a method that solves the Fukushima inverse problem blindly. Using atmospheric dispersion models and worldwide radioactivity measurements together with sparse regularization, the method correctly reconstructs the times of major events during the accident, and gives plausible estimates of the released quantities of Xenon


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    The reconstruction of a diffusion field, such as temperature, from samples collected by a sensor network is a classical inverse problem and it is known to be ill-conditioned. Previous work considered source models, such as sparse sources, to regularize the solution. Here, we consider uniform spatial sampling and reconstruction by classical interpolation techniques for those scenarios where the spatial sparsity of the sources is not realistic. We show that even if the spatial bandwidth of the field is infinite, we can exploit the natural lowpass filter given by the diffusion phenomenon to bound the aliasing error. Index Terms — Diffusion equation, initial inverse problems, spatial sampling, aliasing error, interpolation. 1