Au-on-Ag nanostructure forin-situSERS monitoring of catalytic reactions

Photon counting Raman spectroscopy: a benchmarking study vs surface plasmon enhancement

We demonstrate a single-photon counting Raman spectroscope and benchmark it against conventional and surface-enhanced Raman spectroscopy. For direct comparison without ambiguity, we use the same solutions of Rhodamine 6G and a common optical setup with either a spectrometer or an acousto-optic tunable filter, whereas the surface enhancement is realized with immobilized Ag nanoparticles. Our results find that the single photon counting significantly elevates the detection sensitivity by up to eight orders of magnitude, arriving at a comparable level of surface-enhanced Raman spectroscopy. Another significant advantage is with the time-resolving measurement, where we demonstrate time-gated and time-correlated single-photon counting with sub-nanosecond resolution. It offers insights into the samples' transient responses and enables the isolation of Raman scattering from fluorescence signals.

SERS detection of dopamine using metal-chelated Ag nanoshell

As the concentrations of different neurotransmitters can indicate the presence of certain disorders affecting brain functions, quantitative analyses of neurotransmitters have attracted increasing attention in various fields. Surface-enhanced Raman scattering (SERS) spectroscopy is an outstanding spectroscopic analytical tool that enables detection at the single molecule level with high specificity. As local field enhancement of surface plasmon is effective within nanometers, active interaction between SERS-active noble metals (gold and silver) and analyte molecules enhances the molecular detection capacity of SERS. However, neurotransmitters and noble metal nanoparticles are often not affinitive, because neurotransmitters generally have a hydroxyl group rather than a thiol group. As a result, the interaction between the two typically remains inactive, which makes detection more difficult. To overcome this limitation, in the present work we utilized metal-chelation to attract dopamine, a neurotransmitter molecule, close to the surface of silver nanoparticles. AgNS was capped with poly(vinyl alcohol) (PVA) and sequentially integrated with copper ion to bind dopamine in the form of chelate bonding between dopamine and copper. The PVA linked AgNS and metal ions through a coordinate bond between hydroxyl groups and metal ions. This metal-chelation-functionalized nanoprobe allowed us to stably detect dopamine in aqueous solution at a concentration of less than 10-6 M. Therefore, this method provides a convenient and easy-to-prepare option for the effective detection of dopamine, thus meaning it has the potential to be applied to other neurotransmitters.

Miner Fatigue Detection from Electroencephalogram-Based Relative Power Spectral Topography Using Convolutional Neural Network

Fatigue of miners is caused by intensive workloads, long working hours, and shift-work schedules. It is one of the major factors increasing the risk of safety problems and work mistakes. Examining the detection of miner fatigue is important because it can potentially prevent work accidents and improve working efficiency in underground coal mines. Many previous studies have introduced feature-based machine-learning methods to estimate miner fatigue. This work proposes a method that uses electroencephalogram (EEG) signals to generate topographic maps containing frequency and spatial information. It utilizes a convolutional neural network (CNN) to classify the normal state, critical state, and fatigue state of miners. The topographic maps are generated from the EEG signals and contrasted using power spectral density (PSD) and relative power spectral density (RPSD). These two feature extraction methods were applied to feature recognition and four representative deep-learning methods. The results showthat RPSD achieves better performance than PSD in classification accuracy with all deep-learning methods. The CNN achieved superior results to the other deep-learning methods, with an accuracy of 94.5%, precision of 97.0%, sensitivity of 94.8%, and F1 score of 96.3%. Our results also show that the RPSD-CNN method outperforms the current state of the art. Thus, this method might be a useful and effective miner fatigue detection tool for coal companies in the near future.

Research on obstacle avoidance optimization and path planning of autonomous vehicles based on attention mechanism combined with multimodal information decision-making thoughts of robots

With the development of machine perception and multimodal information decision-making techniques, autonomous driving technology has become a crucial area of advancement in the transportation industry. The optimization of vehicle navigation, path planning, and obstacle avoidance tasks is of paramount importance. In this study, we explore the use of attention mechanisms in a end-to-end architecture for optimizing obstacle avoidance and path planning in autonomous driving vehicles. We position our research within the broader context of robotics, emphasizing the fusion of information and decision-making capabilities. The introduction of attention mechanisms enables vehicles to perceive the environment more accurately by focusing on important information and making informed decisions in complex scenarios. By inputting multimodal information, such as images and LiDAR data, into the attention mechanism module, the system can automatically learn and weigh crucial environmental features, thereby placing greater emphasis on key information during obstacle avoidance decisions. Additionally, we leverage the end-to-end architecture and draw from classical theories and algorithms in the field of robotics to enhance the perception and decision-making abilities of autonomous driving vehicles. Furthermore, we address the optimization of path planning using attention mechanisms. We transform the vehicle's navigation task into a sequential decision-making problem and employ LSTM (Long Short-Term Memory) models to handle dynamic navigation in varying environments. By applying attention mechanisms to weigh key points along the navigation path, the vehicle can flexibly select the optimal route and dynamically adjust it based on real-time conditions. Finally, we conducted extensive experimental evaluations and software experiments on the proposed end-to-end architecture on real road datasets. The method effectively avoids obstacles, adheres to traffic rules, and achieves stable, safe, and efficient autonomous driving in diverse road scenarios. This research provides an effective solution for optimizing obstacle avoidance and path planning in the field of autonomous driving. Moreover, it contributes to the advancement and practical applications of multimodal information fusion in navigation, localization, and human-robot interaction.