Sensors

In this article, an adaptive neural network (ANN) controller based on feature augmentation (FA) is designed for quadrotors. The proposed controller consists of two components: a position sub-controller and an attitude sub-controller. We use the ANN to estimate unknown internal and external disturbance terms within quadrotors. To improve the learning accuracy of the ANN, we design an FA structure, which enables networks to more effectively learn the characteristics in the data. To increase the learning rate of the ANN, a state predictor (SP) is proposed to anticipate the state errors, which subsequently updates the learning rate of the ANN. Based on stability analysis, we prove that the closed-loop system is input-to-state stable (ISS). Finally, the effectiveness of our proposed control algorithm is demonstrated by comparing it with related control algorithms on both the MATLAB R2020a/Simulink simulation platform and a quadrotor experimental platform.

This paper reports magnetic microscopy using high-sensitivity room-temperature tunnel magnetoresistance (TMR) devices for thin geological sections. The sensitivity region of the TMR sensor has dimensions of 178 µm (L) × 0.1 µm (W) × 100 µm (H), consisting of two TMR devices. Magnetic images were obtained for a vertically magnetized Hawaii basalt thin section in two sensor configurations, with the sensor length aligned parallel to the X- (lift-off = 174 μm) and Y-axes (lift-off = 200 μm), without introducing anisotropic distortion in the magnetic images. Although the magnetic images obtained with a scanning SQUID microscope (SSM) were similar, slight discrepancies were observed in the high-spatial-resolution region. A magnetic point source (50 μm × 50 μm) with a perpendicular magnetization film was prepared for evaluation. The SSM measurements showed a clear magnetic dipole at an angle of approximately 1° from the vertical direction. The FWHMs for both the SSM and TMR sensors increased linearly with lift-off. However, the peak magnetic fields, magnetic moments, and dipole tilts of the TMR sensor were significantly larger than those of the SSM sensor. This discrepancy may be due to the vertical extent of the active region of the TMR sensor, as well as due to sensor noise and drift.

Wildfires are environmental hazards with severe ecological, social, and economic impacts. Wildfires devastate ecosystems, communities, and economies worldwide, with rising frequency and intensity driven by climate change, human activity, and environmental shifts. Analyzing wildfire insights such as detection, predictive patterns, and risk assessment enables proactive response and long-term prevention. However, most of the existing approaches have been focused on isolated processing of data, making it challenging to orchestrate cross-modal reasoning and transparency. This study proposed a novel orchestrator-based multi-agent system (MAS), with the aim of transforming multimodal environmental data into actionable intelligence for decision making. We designed a framework to utilize Large Multimodal Models (LMMs) augmented by structured prompt engineering and specialized Retrieval-Augmented Generation (RAG) pipelines to enable transparent and context-aware reasoning, providing a cutting-edge Visual Question Answering (VQA) system. It ingests diverse inputs like satellite imagery, sensor readings, weather data, and ground footage and then answers user queries. Validated by several public datasets, the system achieved a precision of 0.797 and an F1-score of 0.736. Thus, powered by Agentic AI, the proposed, human-centric solution for wildfire management, empowers firefighters, governments, and researchers to mitigate threats effectively.