Get our free extension to see links to code for papers anywhere online!

Chrome logo  Add to Chrome

Firefox logo Add to Firefox
Picture for John Yearwood

John Yearwood
Alert button

Optimising complexity of CNN models for resource constrained devices: QRS detection case study


Jan 23, 2023
Ahsan Habib, Chandan Karmakar, John Yearwood

Add code
Alert button

Figure 1 for Optimising complexity of CNN models for resource constrained devices: QRS detection case study
Figure 2 for Optimising complexity of CNN models for resource constrained devices: QRS detection case study
Figure 3 for Optimising complexity of CNN models for resource constrained devices: QRS detection case study
Figure 4 for Optimising complexity of CNN models for resource constrained devices: QRS detection case study

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Learning post-processing for QRS detection using Recurrent Neural Network


Oct 07, 2021
Ahsan Habib, Chandan Karmakar, John Yearwood

Add code
Alert button

Figure 1 for Learning post-processing for QRS detection using Recurrent Neural Network
Figure 2 for Learning post-processing for QRS detection using Recurrent Neural Network
Figure 3 for Learning post-processing for QRS detection using Recurrent Neural Network
Figure 4 for Learning post-processing for QRS detection using Recurrent Neural Network

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Knowledge engineering mixed-integer linear programming: constraint typology


Feb 20, 2021
Vicky Mak-Hau, John Yearwood, William Moran

Add code
Alert button

Figure 1 for Knowledge engineering mixed-integer linear programming: constraint typology
Figure 2 for Knowledge engineering mixed-integer linear programming: constraint typology
Figure 3 for Knowledge engineering mixed-integer linear programming: constraint typology
* 6 pages, 3 figures. arXiv admin note: substantial text overlap with arXiv:2011.06300 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

A Knowledge Representation Approach to Automated Mathematical Modelling


Nov 12, 2020
Bahadorreza Ofoghi, Vicky Mak, John Yearwood

Add code
Alert button

Figure 1 for A Knowledge Representation Approach to Automated Mathematical Modelling
Figure 2 for A Knowledge Representation Approach to Automated Mathematical Modelling
* 10 pages, 2 figures 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Choosing a sampling frequency for ECG QRS detection using convolutional networks


Jul 04, 2020
Ahsan Habib, Chandan Karmakar, John Yearwood

Add code
Alert button

Figure 1 for Choosing a sampling frequency for ECG QRS detection using convolutional networks
Figure 2 for Choosing a sampling frequency for ECG QRS detection using convolutional networks
Figure 3 for Choosing a sampling frequency for ECG QRS detection using convolutional networks
Figure 4 for Choosing a sampling frequency for ECG QRS detection using convolutional networks

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Robust artificial neural networks and outlier detection. Technical report


Oct 02, 2011
Gleb Beliakov, Andrei Kelarev, John Yearwood

Add code
Alert button

Figure 1 for Robust artificial neural networks and outlier detection. Technical report
Figure 2 for Robust artificial neural networks and outlier detection. Technical report
Figure 3 for Robust artificial neural networks and outlier detection. Technical report
Figure 4 for Robust artificial neural networks and outlier detection. Technical report

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email