Rapid Exact Signal Scanning with Deep Convolutional Neural Networks
A rigorous formulation of the dynamics of a signal processing scheme aimed at dense signal scanning without any loss in accuracy is introduced and analyzed. Related methods proposed in the recent past lack a satisfactory analysis of whether they actually fulfill any exactness constraints. This is improved through an exact characterization of the requirements for a sound sliding window approach. The tools developed in this paper are especially beneficial if Convolutional Neural Networks are employed, but can also be used as a more general framework to validate related approaches to signal scanning. The proposed theory helps to eliminate redundant computations and renders special case treatment unnecessary, resulting in a dramatic boost in efficiency particularly on massively parallel processors. This is demonstrated both theoretically in a computational complexity analysis and empirically on modern parallel processors.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Dyadic Sex Composition and Task Classification Using fNIRS Hyperscanning Data
Hyperscanning with functional near-infrared spectroscopy (fNIRS) is an emerging neuroimaging application that measures the nuanced neural signatures underlying social interactions. Researchers have assessed the effect of…
ClassificationDynamic Time WarpingRapid TAURUS for Relaxation-Based Color Magnetic Particle Imaging
Magnetic particle imaging (MPI) is a rapidly developing medical imaging modality that exploits the non-linear response of magnetic nanoparticles (MNPs). Color MPI widens the functionality of MPI, empowering it with the c…
Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise Classification
We present highly efficient algorithms for performing forward and backward propagation of Convolutional Neural Network (CNN) for pixelwise classification on images. For pixelwise classification tasks, such as image segme…
ClassificationGeneral Classificationimage-classificationImage Classification+4Channel Attention Networks for Robust MR Fingerprinting Matching
Magnetic Resonance Fingerprinting (MRF) enables simultaneous mapping of multiple tissue parameters such as T1 and T2 relaxation times. The working principle of MRF relies on varying acquisition parameters pseudo-randomly…
channel selectionMagnetic Resonance FingerprintingIdentity information based on human magnetocardiography signals
We have developed an individual identification system based on magnetocardiography (MCG) signals captured using optically pumped magnetometers (OPMs). Our system utilizes pattern recognition to analyze the signals obtain…
Management