Discrete Fourier transform (DFT) implementation requires high computational resources and
time; a computational complexity of order O (N 2) for a signal of size N. Fast Fourier …

The discrete Fourier transform (DFT) is a fundamental component of numerous
computational techniques in signal processing and scientific computing. The most popular …

A novel dual-function radar communication (DFRC) system is proposed, that achieves high
communication rate, and can flexibly trade-off rate for improved sensing performance. The …

Sparsity in the Fourier domain is an important property that enables the dense
reconstruction of signals, such as 4D light fields, from a small set of samples. The sparsity of …

We present BigBand, a technology that can capture GHz of spectrum in realtime without
sampling the signal at GS/s-ie, without high speed ADCs. Further, it is simple and can be …

The ability to efficiently load functions on quantum computers with high fidelity is essential
for many quantum algorithms, including those for solving partial differential equations and …

Rapid developments in synthetic aperture (SA) systems, which generate a larger aperture
with greater angular resolution than is inherently possible from the physical dimensions of a …

Current face or object detection methods via convolutional neural network (such as
OverFeat, R-CNN and DenseNet) explicitly extract multi-scale features based on an image …

Abstract Machine learning tasks are an exciting application for quantum computers, as it has
been proven that they can learn certain problems more efficiently than classical ones …

The fast Fourier transform (FFT) algorithm was developed by Cooley and Tukey in 1965. It
could reduce the computational complexity of discrete Fourier transform significantly from O …