A New Way To See Life’s Hidden Chemistry: $10 Spectrometer Could Turn Wearables Into Personal Health Labs

Researchers have developed a compact, low-cost convolutional spectrometer that delivers lab-grade precision for applications ...
eeworldonline

How to use convolution to implement filters: part 4

A windowed sinc function can implement a low-pass filter, and a two-dimensional convolutional filter can blur or sharpen images. In part 3 of this series, we introduced a low-pass filter based on the ...
IEEE

Dynamic Strip Convolution and Adaptive Morphology Perception Plugin for Medical Anatomy Segmentation

Abstract: Medical anatomy segmentation is essential for computer-aided diagnosis and lesion localization in medical images. For example, segmenting individual ribs benefits localizing the lung lesions ...
Embedded

Understanding convolutional neural networks

The world of artificial intelligence (AI) is rapidly evolving, and AI is increasingly enabling applications that were previously unattainable or very difficult to implement. A subsequent article, ...
Frontiers

Accurate Tumor Segmentation via Octave Convolution Neural Network

Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many ...
Frontiers

Motor Imagery EEG Signal Recognition Using Deep Convolution Neural Network

Brain computer interaction (BCI) based on EEG can help patients with limb dyskinesia to carry out daily life and rehabilitation training. However, due to the low signal-to-noise ratio and large ...
IEEE

Nonstationary Discrete Convolution Kernel for Multimodal Process Monitoring

Abstract: Data-driven process monitoring has benefited from the development and application of kernel transformations, especially when various types of nonlinearity exist in the data. However, when ...
syncedreview

A Guide to Receptive Field Arithmetic for Convolutional Neural Networks

The receptive field is defined as the region in the input space that a particular CNN’s feature is looking at (i.e. be affected by). For convolutional neural network, the number of output features in ...