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Self-Driving Microscopes: AI Meets Super-Resolution Microscopy.
Small Methods
January 2025
Dept. Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, CB3 0AS, UK.
The integration of Machine Learning (ML) with super-resolution microscopy represents a transformative advancement in biomedical research. Recent advances in ML, particularly deep learning (DL), have significantly enhanced image processing tasks, such as denoising and reconstruction. This review explores the growing potential of automation in super-resolution microscopy, focusing on how DL can enable autonomous imaging tasks.
View Article and Find Full Text PDFMultiscale Understanding of Anion Exchange Membrane Fuel Cells: Mechanisms, Electrocatalysts, Polymers, and Cell Management.
Adv Mater
January 2025
Institut National de la Recherche Scientifique (INRS), Centre Énergie Matériaux Télécommunications, Varennes, Québec, J3×1P7, Canada.
Anion exchange membrane fuel cells (AEMFCs) are among the most promising sustainable electrochemical technologies to help solve energy challenges. Compared to proton exchange membrane fuel cells (PEMFCs), AEMFCs offer a broader choice of catalyst materials and a less corrosive operating environment for the bipolar plates and the membrane. This can lead to potentially lower costs and longer operational life than PEMFCs.
View Article and Find Full Text PDFCharacterization of Hazelnut Trees in Open Field Through High-Resolution UAV-Based Imagery and Vegetation Indices.
Sensors (Basel)
January 2025
Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, Italy.
The increasing demand for hazelnut kernels is favoring an upsurge in hazelnut cultivation worldwide, but ongoing climate change threatens this crop, affecting yield decreases and subject to uncontrolled pathogen and parasite attacks. Technical advances in precision agriculture are expected to support farmers to more efficiently control the physio-pathological status of crops. Here, we report a straightforward approach to monitoring hazelnut trees in an open field, using aerial multispectral pictures taken by drones.
View Article and Find Full Text PDFThree-Dimensionally Printed Mini Air Scrubbing Cartridges Based on Nano-Graphite for Air Pollution Monitoring.
Sensors (Basel)
December 2024
Institute of Atmospheric Pollution Research-National Research Council (IIA-CNR), Research Area of Rome 1, Strada Provinciale 35d, Montelibretti, 9-00010 Roma, Italy.
Ecosystems and environments are impacted by atmospheric pollution, which has significant effects on human health and climate. For these reasons, devices for developing portable and low-cost monitoring systems are required to assess human exposure during daily life. In the last decade, the advancements of 3D printing technology have pushed researchers to exploit, in different fields of applications, the advantages offered, such as rapid prototyping and low-cost replication of complex sample treatment devices.
View Article and Find Full Text PDFResidual Vision Transformer and Adaptive Fusion Autoencoders for Monocular Depth Estimation.
Sensors (Basel)
December 2024
Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan.
Precision depth estimation plays a key role in many applications, including 3D scene reconstruction, virtual reality, autonomous driving and human-computer interaction. Through recent advancements in deep learning technologies, monocular depth estimation, with its simplicity, has surpassed the traditional stereo camera systems, bringing new possibilities in 3D sensing. In this paper, by using a single camera, we propose an end-to-end supervised monocular depth estimation autoencoder, which contains an encoder with a structure with a mixed convolution neural network and vision transformers and an effective adaptive fusion decoder to obtain high-precision depth maps.
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