Finding Small-Bowel Lesions: Challenges in Endoscopy-Image-Based Learning Systems

Capsule endoscopy identifies damaged areas in a patient’s small intestine but often outputs poor-quality images or misses lesions, leading to either misdiagnosis or repetition of the lengthy procedure. The authors propose applying deep-learning models to automatically process the captured images and identify lesions in real time, enabling the capsule to…

The Deep (Learning) Transformation of Mobile and Embedded Computing

Mobile and embedded devices increasingly rely on deep neural networks to understand the world—a feat that would have overwhelmed their system resources only a few years ago. Further integration of machine learning and embedded/mobile systems will require additional breakthroughs of efficient learning algorithms that can function under fluctuating resource constraints,…

Cyberthreats under the Bed

Internet-connected toys provide an often-overlooked avenue for breaching personal data, especially of those most vulnerable. Government and private measures can minimize the risks, but responsibility for monitoring smart toy usage ultimately lies with parents. Source: New feed

Breathing-Based Authentication on Resource-Constrained IoT Devices using Recurrent Neural Networks

Recurrent neural networks (RNNs) have shown promising results in audio and speech-processing applications. The increasing popularity of Internet of Things (IoT) devices makes a strong case for implementing RNN-based inferences for applications such as acoustics-based authentication and voice commands for smart homes. However, the feasibility and performance of these inferences…