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Found 38 papers, 3 papers with code
Fully Differentiable Ray Tracing via Discontinuity Smoothing for Radio Network Optimization
Recently, Differentiable Ray Tracing has been successfully applied in the field of wireless communications for learning radio materials or optimizing the transmitter orientation.
Spintronics for image recognition: performance benchmarking via ultrafast data-driven simulations
We present a demonstration of image classification using an echo-state network (ESN) relying on a single simulated spintronic nanostructure known as the vortex-based spin-torque oscillator (STVO) delayed in time.
Grid Hopping: Accelerating Direct Estimation Algorithms for Multistatic FMCW Radar
Our grid hopping approach, which relies on interpolation strategies, offers a reduced computation time while its performance stays on par with the direct method.
Signal processing after quadratic random sketching with optical units
In this context, the possibility of performing data processing (such as pattern detection or classification) directly in the sketched domain without accessing the original data was previously achieved for linear random sketching methods and compressive sensing.
Interferometric single-pixel imaging with a multicore fiber
Lensless illumination single-pixel imaging with a multicore fiber (MCF) is a computational imaging technique that enables potential endoscopic observations of biological samples at cellular scale.
Interferometric lensless imaging: rank-one projections of image frequencies with speckle illuminations
Lensless illumination single-pixel imaging with a multicore fiber (MCF) is a computational imaging technique that enables potential endoscopic observations of biological samples at cellular scale.
Learning to Reconstruct Signals From Binary Measurements
Here we explore the extreme case of learning from binary observations and provide necessary and sufficient conditions on the number of measurements required for identifying a set of signals from incomplete binary data.
Min-Path-Tracing: A Diffraction Aware Alternative to Image Method in Ray Tracing
For more than twenty years, Ray Tracing methods have continued to improve on both accuracy and computational time aspects.
Signal processing with optical quadratic random sketches
In this context, the possibility of performing data processing (such as pattern detection or classification) directly in the sketched domain without accessing the original data was previously achieved for linear random sketching methods and compressive sensing.
ROP inception: signal estimation with quadratic random sketching
In a nutshell, the SPE shows that the scalar product of a signal sketch with the "sign" of the sketch of a given pattern approximates the square of the projection of that signal on this pattern.
The Separation Capacity of Random Neural Networks
Neural networks with random weights appear in a variety of machine learning applications, most prominently as the initialization of many deep learning algorithms and as a computationally cheap alternative to fully learned neural networks.
Compressive lensless endoscopy with partial speckle scanning
We develop our approach on two key properties of the LE: (i) the ability to easily generate speckles, and (ii) the memory effect in MCF that allows to use fast scan mirrors to shift light patterns.
Asymmetric compressive learning guarantees with applications to quantized sketches
The compressive learning framework reduces the computational cost of training on large-scale datasets.
Sparse Factorization-based Detection of Off-the-Grid Moving targets using FMCW radars
This algorithm extends existing continuous greedy algorithms to the framework of factorized sparse representations of the signals.
Morphological components analysis for circumstellar disks imaging
However, the faint intensity of the circumstellar disks compared to the brightness of the host star compels astronomers to use tailored observation strategies, in addition to state-of-the-art optical devices.
Instrumentation and Methods for Astrophysics Earth and Planetary Astrophysics Information Theory Information Theory
Going Below and Beyond, Off-the-Grid Velocity Estimation from 1-bit Radar Measurements
In this paper we propose to bridge the gap between using extremely low resolution 1-bit measurements and estimating targets' parameters, such as their velocities, that exist in a continuum, i. e., by performing Off-the-Grid estimation.
When compressive learning fails: blame the decoder or the sketch?
In compressive learning, a mixture model (a set of centroids or a Gaussian mixture) is learned from a sketch vector, that serves as a highly compressed representation of the dataset.
One Bit to Rule Them All : Binarizing the Reconstruction in 1-bit Compressive Sensing
This work focuses on the reconstruction of sparse signals from their 1-bit measurements.
Sketching Datasets for Large-Scale Learning (long version)
This article considers "compressive learning," an approach to large-scale machine learning where datasets are massively compressed before learning (e. g., clustering, classification, or regression) is performed.
Factorization over interpolation: A fast continuous orthogonal matching pursuit
We propose a fast greedy algorithm to compute sparse representations of signals from continuous dictionaries that are factorizable, i. e., with atoms that can be separated as a product of sub-atoms.
Breaking the waves: asymmetric random periodic features for low-bitrate kernel machines
Concretely, we introduce the general framework of asymmetric random periodic features, where the two signals of interest are observed through random periodic features: random projections followed by a general periodic map, which is allowed to be different for both signals.
Compressive Learning of Generative Networks
Generative networks implicitly approximate complex densities from their sampling with impressive accuracy.
Compressive Classification (Machine Learning without learning)
Compressive learning is a framework where (so far unsupervised) learning tasks use not the entire dataset but a compressed summary (sketch) of it.
Proceedings of the fourth "international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques" (iTWIST'18)
The iTWIST workshop series aim at fostering collaboration between international scientific teams for developing new theories, applications and generalizations of low-complexity models.
Compressive Single-pixel Fourier Transform Imaging using Structured Illumination
Single Pixel (SP) imaging is now a reality in many applications, e. g., biomedical ultrathin endoscope and fluorescent spectroscopy.
Compressive Sampling Approach for Image Acquisition with Lensless Endoscope
The lensless endoscope is a promising device designed to image tissues in vivo at the cellular scale.
Compressive Hyperspectral Imaging: Fourier Transform Interferometry meets Single Pixel Camera
This paper introduces a single-pixel HyperSpectral (HS) imaging framework based on Fourier Transform Interferometry (FTI).
Quantized Compressive K-Means
The recent framework of compressive statistical learning aims at designing tractable learning algorithms that use only a heavily compressed representation-or sketch-of massive datasets.
Multispectral Compressive Imaging Strategies using Fabry-Pérot Filtered Sensors
This paper introduces two acquisition device architectures for multispectral compressive imaging.
Sparse Support Recovery with Non-smooth Loss Functions
More precisely, we focus in detail on the cases of $\ell_1$ and $\ell_\infty$ losses, and contrast them with the usual $\ell_2$ loss. While these losses are routinely used to account for either sparse ($\ell_1$ loss) or uniform ($\ell_\infty$ loss) noise models, a theoretical analysis of their performance is still lacking.
Blind Deconvolution of PET Images using Anatomical Priors
Images from positron emission tomography (PET) provide metabolic information about the human body.
Multi-resolution Compressive Sensing Reconstruction
We consider the problem of reconstructing an image from compressive measurements using a multi-resolution grid.
Cell segmentation with random ferns and graph-cuts
To isolate individual cells in live imaging data, we introduce an elegant image segmentation framework that effectively extracts cell boundaries, even in the presence of poor edge details.
Post-Reconstruction Deconvolution of PET Images by Total Generalized Variation Regularization
Improving the quality of positron emission tomography (PET) images, affected by low resolution and high level of noise, is a challenging task in nuclear medicine and radiotherapy.
Discriminative and Efficient Label Propagation on Complementary Graphs for Multi-Object Tracking
Given a set of detections, detected at each time instant independently, we investigate how to associate them across time.
Non-parametric PSF estimation from celestial transit solar images using blind deconvolution
Contrarily to most methods presented in the literature, our method does not assume a parametric model of the PSF and can thus be applied to any telescope.
Compressive Imaging and Characterization of Sparse Light Deflection Maps
This compressive characterization is then confirmed with experimental results on simple plano-convex and multifocal intra-ocular lenses studying the evolution of the main deflection as a function of the object point location.
Compressive Optical Deflectometric Tomography: A Constrained Total-Variation Minimization Approach
Optical Deflectometric Tomography (ODT) provides an accurate characterization of transparent materials whose complex surfaces present a real challenge for manufacture and control.
