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Abstract Accurate velocity measurement of unmanned aerial vehicles (UAVs) is essential in various applications. Traditional vision-based methods rely heavily on visual features, which are often inadequate in low-light or feature-sparse environments. This study presents a novel approach to measure the axial velocity of UAVs using motion blur images captured by a UAV-mounted monocular camera. We introduce a motion blur model that synthesizes imaging from neighboring frames to enhance motion blur visibility. The synthesized blur frames are transformed into spectrograms using the Fast Fourier Transform (FFT) technique. We then apply a binarization process and the Radon transform to extract light-dark stripe spacing, which represents the motion blur length. This length is used to establish a model correlating motion blur with axial velocity, allowing precise velocity calculation. Field tests in a hydropower station penstock demonstrated an average velocity error of 0.048 m/s compared to ultra-wideband (UWB) measurements. The root-mean-square error was 0.025, with an average computational time of 42.3 ms and CPU load of 17%. These results confirm the stability and accuracy of our velocity estimation algorithm in challenging environments.

Functional near-infrared spectroscopy (fNIRS) seems opportune for neurofeedback in robot-assisted rehabilitation training due to its noninvasive, less physical restriction, and no electromagnetic disturbance. Previous research has proved the cross-session reliability of fNIRS responses to non-motor tasks (e.g., visual stimuli) and fine-motor tasks (e.g., finger tapping). However, it is still unknown whether fNIRS responses remain reliable 1) in gross-motor tasks, 2) within a training session, and 3) for different training parameters. Hence, this study aimed to investigate the within-session reliability of fNIRS responses to gross-motor tasks for different training parameters. Ten healthy participants were recruited to conduct right elbow extension-flexion in three robot-assisted modes. The Passive mode was fully motor-actuated, while Active1 and Active2 modes involved active engagement with different resistance levels. FNIRS data of three identical runs were used to assess the within-session reliability in terms of the map- ( ${R}^{{2}}$ ) and cluster-wise ( ${R}_{\textit {over}\textit {lap}}$ ) spatial reproducibility and the intraclass correlation (ICC) of temporal features. The results revealed good spatial reliability ( ${R}^{{2}}$ up to 0.69, ${R}_{\textit {over}\textit {lap}}$ up to 0.68) at the subject level. Besides, the within-session temporal reliabilities of Slope, Max/Min, and Mean were between good and excellent ( $0.60< $ ICC < 0.86). We also found that the within-session reliability was positively correlated with the intensity of the training mode, except for the temporal reliability of HbO in Active2 mode. Overall, our results demonstrated good within-session reliability of fNIRS responses, suggesting fNIRS as reliable neurofeedback for constructing closed-loop robot-assisted rehabilitation systems.

Simultaneous Location and Mapping (SLAM) is a common algorithm for position estimation in GNSS-denied environments. However, the high structural consistency and low lighting conditions in tunnel environments pose challenges for traditional visual SLAM and LiDAR SLAM. To this end, this paper presents LiDAR and optical flow fusion odometry (LOFF), which uses a direction-separated data fusion method to fuse optical flow odometry into the degenerate direction of the LiDAR SLAM without sacrificing the accuracy. Moreover, LOFF incorporates detectors and a compensator, allowing for a smooth transition between general environments and degeneracy environments. This capability facilitates the stable flight of unmanned aerial vehicles (UAVs) in GNSS-denied tunnel environments, including corners and long-distance consistency. Through real-world experiments conducted in a GNSS-denied pedestrian tunnel, we demonstrate the superior position accuracy and trajectory smoothness of LOFF compared to state-of-the-art visual SLAM and LiDAR SLAM.

Substantial Phanerozoic crustal growth and reworking in the eastern Central Asian Orogenic Belt has partly obscured the Precambrian geological record. Accurate identification of such Precambrian magmatism and its petrogenesis is paramount to unveiling the early crustal evolution of the eastern Central Asian Orogenic Belt. Two outcrops of Neoarchean and Paleoproterozoic magmatism in the Longjiang area of NE China, eastern Central Asian Orogenic Belt, were recently discovered. The Kaoquantun outcrop consists of monzogranites that yield a zircon 207Pb/206Pb age of 2568 ± 8 Ma and are similar to peraluminous A1-type granites. They were derived from the partial melting of a hybrid crustal source comprising newly accreted Mesoarchean crust and abundant supracrustal metapelites in a post-collisional extensional setting. Such metapelites were initially part of the upper crust but were buried in the middle-lower crust due to crustal thickening prior to ca. 2.6 Ga. In the Zhanbeitun outcrop, basaltic andesites overlie monzogranites, and both have been intruded by syenogranites. The monzogranites and syenogranites yield zircon 207Pb/206Pb ages of 1881 ± 10 Ma and 1843 ± 3 Ma, respectively, whereas the basaltic andesites are inferred to have formed at 1.88-1.84 Ga. The 1.88 Ga monzogranites have relatively enriched zircon Hf-O isotopic compositions and were derived from a hybrid melt of mafic rocks in the lower crust and ancient metabasalts that had undergone low-temperature alteration at supracrustal depths. The 1.88-1.84 Ga basaltic andesites are Nb-rich and were generated by the partial melting of a mantle wedge that was metasomatized by subduction-derived fluids, recycled sediments, and slab-derived melts. The 1.84 Ga syenogranites are peraluminous and K-rich, and record a profound mixing and assimilation process involving melts derived from supracrustal sediments and the lower crust. Three stages of Paleoproterozoic magmatism emerged from subductionrelated crustal thickening, slab breakoff, and collision, respectively. The zircon Hf isotopic data further indicate that the eastern Central Asian Orogenic Belt underwent at least one stage of proto-crustal growth at ca. 3.0 Ga, followed by multiple phases of crustal reworking during the Neoarchean and Paleoproterozoic, with dominant involvement of supracrustal components. [ABSTRACT FROM AUTHOR]