ImECGnet: Cardiovascular Disease Classification from Image-Based ECG Data Using a Multi-branch Convolutional Neural Network

Reliable Cardiovascular Disease (CVD) classification performed by a smart system can assist medical doctors in recognizing heart illnesses in patients more efficiently and effectively. Electrocardiogram (ECG) signals are an important diagnostic tool as they are already available early in the patients’ health diagnosis process and contain...

Cybernetic Data Augmentation for Neural Network Classification of Control Skills

Mathematical human controller (HC) models are widely used in tuning manual control systems and for understanding human performance. Typically, quasi-linear HC models are used, which can accurately capture the linear portion of HCs' behavior, averaged over a long measurement window. This paper presents a deep learning HC skill-level evaluation...

Sound-recognition using Spiking Neural Networks

The development of the Spiking Neural Network (SNN) offers great potential in combination with new types of event-based sensors, by exploiting the embedded temporal information. When combined with dedicated neuromorphic hardware it enables ultra-low power solutions and local on-chip learning. This work implements and presents a viable...

Privacy preserving detection and classification of playground users

Large cities in the Netherlands, like Rotterdam, have hundreds of playgrounds, but local governments have little information on how children and adults use them. Usage data can help create playgrounds that better fit with the residents' needs by identifying what elements of a playground are the most popular, and which do not get used. The AMS...

An energy efficient time-mode digit classification neural network implementation

This paper presents the design of an ultra-low energy neural network that uses time-mode signal processing). Handwritten digit classification using a single-layer artificial neural network (ANN) with a Softmin-based activation function is described as an implementation example. To realize time-mode operation, the presented design makes use of...

Classification of Damages on Aircraft Inspection Images Using Convolutional Neural Networks: Kick-starting a Deep Learning project with limited data

Aircraft inspections after unexpected incidents, like lightning strikes, currently require a timeconsuming and costly inspection process, due to the small size of the lightning strike damages. Mainblades Inspections is working on an automated, drone-based solution, that scans the aircraft hull with a high-resolution camera. The objective of this...

A learning Dempster-Shafer model for automated building detection

This paper presents a learning Dempster-Shafer model for the detection of buildings in aerial image and range data. The process of evidence assignment in the Dempster-Shafer method is implemented through membership functions in an adaptive network-based fuzzy inference system, where a back propagation learning rule is employed to tune the...