IEEE

FPGA Implementation of High-Speed Accelerators for CNN Architecture Using 3D Systolic Array Architecture

Abstract: Convolution Neural Networks (CNNs) are pivotal in image processing. Prominent CNN models include SqueezeNet, MobileNet, Xception, VGG-16, and AlexNet. These models transform 2D data to 1D, ...
C&EN

Identifying Balls Feature in a Large-Scale Laser Point Cloud of a Coal Mining Environment by a Multiscale Dynamic Graph Convolution Neural Network

College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China ...
C&EN

Coal Wall and Roof Segmentation in the Coal Mine Working Face Based on Dynamic Graph Convolution Neural Networks

College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China ...
Princeton University

ELE 201: Information Signals

Signals that carry information play a central role in technology and engineering — signals ranging from sound and images to sensors, radar, communication, MRI, ultrasound, touch-screens, GPS, and ...
GitHub

hCNN, Hybrid Neural Network Toolbox

During the recent decade, deep learning technology, particularly deep neural network (DNN), has gained tremendous popularity in various fields including signal processing (SP). As a data-driven ...
Frontiers

Higher Sensitivity and Reproducibility of Wavelet-Based Amplitude of Resting-State fMRI

The fast Fourier transform (FFT) is a widely used algorithm used to depict the amplitude of low-frequency fluctuation (ALFF) of resting-state functional magnetic resonance imaging (RS-fMRI). Wavelet ...
EDN

Use LTspice to simulate mixed continuous and sampled systems

Did you know that you can use LTspice to do Digital Signal Processing (DSP)? Actually, I should say it is useful for validating the operation of a signal-processing algorithm under development. This ...
Frontiers

Brain Network Analysis and Classification Based on Convolutional Neural Network

Background: Convolution neural networks (CNN) is increasingly used in computer science and finds more and more applications in different fields. However, analyzing brain network with CNN is not ...
PNAS

Convolutional networks for fast, energy-efficient neuromorphic computing

Brain-inspired computing seeks to develop new technologies that solve real-world problems while remaining grounded in the physical requirements of energy, speed, and size. Meeting these challenges ...
IEEE

FDTD-Based Metamaterial (MTM) Modeling and Simulation [Testing Ourselves]

Abstract: Metamaterial (MTM) modeling and simulation using the Finite-Difference Time-Domain (FDTD) method is discussed. The frequency dependence of the permittivity and permeability of the ...