Categories: AI/ML News

Making the most of quite little: Improving AI training for edge sensor time series

Engineers at the Tokyo Institute of Technology (Tokyo Tech) have demonstrated a simple computational approach for improving the way artificial intelligence classifiers, such as neural networks, can be trained based on limited amounts of sensor data. The emerging applications of the Internet of Things often require edge devices that can reliably classify behaviors and situations based on time series.

The future of edge AI: Dye-sensitized solar cell-based synaptic device

Physical reservoir computing (PRC) utilizing synaptic devices shows significant promise for edge AI. Researchers from the Tokyo University of Science have introduced a novel self-powered dye-sensitized solar cell-based device that mimics human synaptic behavior for efficient edge AI processing, inspired by the eye's afterimage phenomenon. The device has light intensity-controllable…

November 26, 2024

In "AI/ML News"

Using LLMs for Late Multimodal Sensor Fusion for Activity Recognition

This paper was accepted at the Learning from Time Series for Health workshop at NeurIPS 2025. Sensor data streams provide valuable information around activities and context for downstream applications, though integrating complementary information can be challenging. We show that large language models (LLMs) can be used for late fusion for…

November 21, 2025

In "FAANG"

Researchers use machine learning to create a fabric-based touch sensor

A new study from NC State University combines three-dimensional embroidery techniques with machine learning to create a fabric-based sensor that can control electronic devices through touch. The paper is published in the journal Device.

April 18, 2024

In "AI/ML News"

AI Generated Robotic Content