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Found 12 papers, 1 papers with code
Power-Efficient Sampling
Analog-to-digital converters (ADCs) facilitate the conversion of analog signals into a digital format.
Modulation For Modulo: A Sampling-Efficient High-Dynamic Range ADC
An alternative method using phase modulation (PM) achieves HDR-ADC functionality by modulating the phase of a carrier signal with the analog input.
Unlabelled Sensing with Priors: Algorithm and Bounds
We present an estimator to solve for the unknown vector.
Greedy Selection for Heterogeneous Sensors
A greedy selection (GS) algorithm is widely used to select sensors in homogeneous sensor networks.
A Novel Embedding Architecture and Score Level Fusion Scheme for Occluded Image Acquisition in Ear Biometrics System
The reported performance metrics show the improvement achieved by using our proposed embedding network and fusing both sides of occluded ear images.
A Hardware Prototype of Wideband High-Dynamic Range ADC
The dynamic range of an ADC also plays an important role, and ideally, it should be greater than the signal's; otherwise, the signal will be clipped.
Unrolled Compressed Blind-Deconvolution
To reduce its computational and implementation cost, we propose a compression method that enables blind recovery from much fewer measurements with respect to the full received signal in time.
Modulo Sampling of FRI Signals
In the context of modulo folding for FRI sampling, existing works operate at a very high sampling rate compared to the rate of innovation (RoI) and require a large number of samples compared to the degrees of freedom (DoF) of the FRI signal.
Robust Unlimited Sampling Beyond Modulo
We show that our algorithm has the lowest mean-squared error while recovering the signal for a given sampling rate, noise level, and dynamic range of the compared to existing algorithms.
Residual Recovery Algorithm For Modulo Sampling
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.
Learning to Sample: Data-Driven Sampling and Reconstruction of FRI Signals
Typically, Fourier samples of the FRI signals are used for reconstruction.
Unfolding Neural Networks for Compressive Multichannel Blind Deconvolution
We propose a learned-structured unfolding neural network for the problem of compressive sparse multichannel blind-deconvolution.
