GitHub

Holistically-Nested Edge Detection

We develop a new edge detection algorithm, holistically-nested edge detection (HED), which performs image-to-image prediction by means of a deep learning model that leverages fully convolutional ...
IEEE

Enhancing Fault Detection and Classification in Wind Farm Power Generation Using Convolutional Neural Networks (CNN) by Leveraging LVRT Embedded in Numerical Relays

Abstract: Real-time fault detection and classification are important for power system stability and resilience of the power grid to minimize downtime and prevent cascading failures. Numerical relays ...
Frontiers

Designing anisotropic porous bone scaffolds using a self-learning convolutional neural network model

The design of bionic bone scaffolds to mimic the behaviors of native bone tissue is crucial in clinical application, but such design is very challenging due to the complex behaviors of native bone ...
Scientific Research Publishing

Bearing Fault Diagnosis Based on Wavelet Transform and Convolutional Neural Network ()

Rolling bearings are the most prone to failures in mechanical equipment, and the vibration signal is unstable. Therefore, according to this characteristic of the bearing, the time-frequency ...
IEEE

Comparison of CRC and Convolutional Coding in BLE

Abstract: Bluetooth and Wi-Fi technology are commonly used in many areas, including smart phones, smart wearable devices, and Internet of Things (IoT). These technologies build communication bridges ...
ahajournals.org

Multiclass Arrhythmia Detection and Classification From Photoplethysmography Signals Using a Deep Convolutional Neural Network

Studies have reported the use of photoplethysmography signals to detect atrial fibrillation; however, the use of photoplethysmography signals in classifying multiclass arrhythmias has rarely been ...