Edge computing devices, devices located in proximity to the source of data instead of in large data centers, could perform computations locally. This could reduce latency, particularly in real-time applications, as it would minimize the need to transfer data from the...
Over the past decade or so, computer scientists have developed increasingly advanced computational techniques that can tackle real-world tasks with human-comparable accuracy. While many of these artificial intelligence (AI) models have achieved remarkable results,...
Over the past couple of decades, computer scientists have developed a wide range of deep neural networks (DNNs) designed to tackle various real-world tasks. While some of these models have proved to be highly effective, some studies found that they can be unfair,...
Organic electrochemical transistors (OECTs) are an emerging class of transistors based on organic superconducting materials known for their ability to modulate electrical current in response to small changes in the voltage applied to their gate electrode. Like other...
The continuous improvement of circuits and electronic components is vital for the development of new technologies with enhanced capabilities and unique characteristics. In recent years, most electronics engineers have been specifically focusing on reducing the size of...
Recent technological advances have enabled the development of increasingly sophisticated sensors, which can help to advance the sensing capabilities of robots, drones, autonomous vehicles, and other smart systems. Many of these sensors, however, rely on individual...
Evolutionary robotics is a sub-field of robotics aimed at developing artificial "organisms" that can improve their capabilities and body configuration in response to their surroundings, just as humans and animals evolve, adapting their skills and appearance over time....
In recent years, electronics engineers have been trying to develop new brain-inspired hardware that can run artificial intelligence (AI) models more efficiently. While most existing hardware is specialized in either sensing, processing or storing data, some teams have...
Graph neural networks (GNNs) are promising machine learning architectures designed to analyze data that can be represented as graphs. These architectures achieved very promising results on a variety of real-world applications, including drug discovery, social network...
While artificial intelligence (AI) models are becoming increasingly advanced, training and running these models on conventional computer hardware is very energy consuming. Engineers worldwide have thus been trying to create alternative, brain-inspired hardware that...