Enhanced hyperspectral sharpening through improved relative spectral response characteristic (R-SRC) estimation for long-range surveillance applications

The fusion of low-spatial-resolution hyperspectral images (LRHSI) with high-spatial-resolution multispectral images (HRMSI) for super-resolution (SR), using coupled non-negative matrix factorization (CNMF), has been widely studied in the past few decades. However, the matching of spectral characteristics between the LRHSI and HRMSI, which is required before they are jointly factorized, has rarely been studied. One objective of this work is to study how the relative spectral response characteristics (R-SRC) of the LRHSI and HRMSI can be better estimated, particularly when the SRC of the latter is unknown. To this end, three variants of enhanced R-SRC algorithms were proposed, and their effectiveness was assessed by applying them for sharpening data using CNMF. The quality of the output was assessed using the L1-norm-error (L1NE) and receiver operating characteristics (ROC) of target detections performed using the adaptive coherent estimator (ACE) algorithm. Experimental results obtained from two subsets of a real scene revealed a two- to three-fold reduction in the reconstruction error when the scenes were sharpened by the proposed R-SRC algorithms, in comparison with Yokoya’s original algorithm. Experiments also revealed that a much higher proportion (by one order of magnitude) of small targets of 0.015 occupancy in the LRHSI scene could be detected by the proposed R-SRC methods compared with the baseline algorithm, for an equal false alarm rate. These results may suggest the possibility of SR to allow long-range surveillance using low-cost HSI hardware, particularly when the remaining issues of the occurrence of large reconstruction errors and comparatively higher false alarm rate for ‘rare’ species in the scene can be understood and resolved in future research

Supporting data for 'An insight into the hormonal interplay regulating pigment changes and colour development in the peel of ‘Granny Smith’, ‘Opal®’ and ‘Royal Gala’ apples'

This data set contains physiological (colour, size, total soluble content) and biochemical data (including plant hormones, indivicual sugars, anthocyanins) of three different apple cultivars. It also includes the gene expression of gene involved in the ethylene pathway.Spanish Agencia Estatal de Investigacio

AFJPDA: a multiclass multi-object tracking with appearance feature-aided joint probabilistic data association

This study addresses a multiclass multi-object tracking problem in consideration of clutters in the environment. To alleviate issues with clutters, we propose the appearance feature-aided joint probabilistic data association filter. We also implemented simple adaptive gating logic for the computational efficiency and track maintenance logic, which can save the lost track for re-association after occlusion or missed detection. The performance of the proposed algorithm was evaluated against a state-of-the-art multi-object tracking algorithm using both multiclass multi-object simulation and real-world aerial images. The evaluation results indicate significant performance improvement of the proposed method against the benchmark state-of-the-art algorithm, especially in terms of reduction in identity switches and fragmentation.This research was supported by the UK Research and Innovation-funded project HADO: project number 1002481

Vision-based autonomous UGV detection, tracking, and following for a UAV

This study proposes a methodology for unmanned ground vehicle (UGV) navigation in off-road environments where GPS signals are not available. The Husky-A200 at Cranfield University, United Kingdom has been used as a UGV in this research project. Due to the limited field of vision of UGVs, a UAV-UGV collaboration approach was adopted. The methodology involves five steps. The first step is divided into three phases: The aerial images of UGV from UAV are generated in the first phase. In the second phase, the UGV is detected and tracked using computer vision techniques. In the third phase, the relative pose (position and heading) between the UAV and UGV is estimated continuously using visual data. In the second step, the UAV maintain a fixed location (position and heading) relative to the UGV. The third step involves capturing aerial images from the UAV‘s mounted camera and transmitting it to the ground station instantly to create a global traversability map that classifies terrain features based on their traversability. In the fourth step, additional sensors such as LiDAR, radar, and IMU are used to refine the global traversability map. In the final step, the UGV navigates automatically using the refined traversability map. This study will focus on the first two steps of the methodology, while subsequent studies will address the remaining steps

Coupling green hydrogen production to community benefits: a pathway to social acceptance?

Hydrogen energy technologies are forecasted to play a critical supporting role in global decarbonisation efforts, as reflected by the growth of national hydrogen energy strategies in recent years. Notably, the UK government published its Hydrogen Strategy in August 2021 to support decarbonisation targets and energy security ambitions. While establishing techno-economic feasibility for hydrogen energy systems is a prerequisite of the prospective transition, social acceptability is also needed to support visions for the ‘hydrogen economy’. However, to date, societal factors are yet to be embedded into policy prescriptions. Securing social acceptance is especially critical in the context of ‘hydrogen homes’, which entails replacing natural gas boilers and hobs with low-carbon hydrogen appliances. Reflecting the nascency of hydrogen heating and cooking technologies, the dynamics of social acceptance are yet to be explored in a comprehensive way. Similarly, public perceptions of the hydrogen economy and emerging national strategies remain poorly understood. Given the paucity of conceptual and empirical insights, this study develops an integrated acceptance framework and tests its predictive power using partial least squares structural equation modelling. Results highlight the importance of risk perceptions, trust dynamics, and emotions in shaping consumer perceptions. Foremost, prospects for deploying hydrogen homes at scale may rest with coupling renewable-based hydrogen production to local environmental and socio-economic benefits. Policy prescriptions should embed societal factors into the technological pursuit of large-scale, sustainable energy solutions to support socially acceptable transition pathways

Platform health management for aircraft maintenance – a review

Aircraft health management has been researched at both component and system levels. In instances of certain aircraft faults, like the Boeing 777 fuel icing problem, there is evidence suggesting that a platform approach using an Integrated Vehicle Health Management (IVHM) system could have helped detect faults and their interaction effects earlier, before they became catastrophic. This paper reviews aircraft health management from the aircraft maintenance point of view. It emphasizes the potential of a platform solution to diagnose faults, and their interaction effects, at an early stage. The paper conducts a thorough analysis of existing literature concerning maintenance and its evolution, delves into the application of Artificial Intelligence (AI) techniques in maintenance, explains the rationale behind their employment, and illustrates how AI implementation can enhance fault detection using platform sensor data. Further, it discusses how computational severity and criticality indexes (health indexes) can potentially be complementary to the use of AI for the provision of maintenance information on aircraft components, for assisting operational decisions

Achieving a columnar-to-equiaxed transition through dendrite twinning in high deposition rate additively manufactured titanium alloys

The coarse β-grain structures typically found in titanium alloys like Ti–6Al–4V (wt pct, Ti64) and Ti–6Al–2Sn–4Zr–2Mo–0.1Si (Ti6242), produced by high deposition rate additive manufacturing (AM) processes, are detrimental to mechanical performance. Certain modified processing conditions have been shown to lead to a more refined grain structure, which has generally been attributed to a change in the solidification conditions with respect to the experimental Hunt diagram proposed by Semiatin and Kobryn. It is shown that with Wire Arc AM (WAAM) increasing the wire feed speed (WFS) is effective in promoting a columnar-equiaxed transition (CET). Conversely, estimates of the dendrite-tip undercooling using the KGT model suggest that this will be too small for free nucleation without the addition of artificial nucleants, due to the very low solute partitioning in Ti alloys. It is also shown that it is difficult to promote a CET with plasma transferred arc WAAM as computational fluid dynamics (CFD) melt-pool simulations indicate that the solidification parameters remain within the columnar region on the Semiatin-Kobryn Hunt map, within the constraints of a stable process. However, a high fraction of twin boundaries was observed in the refined β-grain structures seen at high WFS. This has been attributed to departure of {001}β alignment from the direction of maximum thermal gradient, caused by the curvature of the fusion boundary, stimulating dendrite twinning during solidification. In addition, it is shown that increasing the WFS leads to a change in melt-pool geometry and a reduction of remelt depth, which promoted dendrite twinning and grain refinement.The authors are appreciative of the EPSRC program grants NEWAM (EP/R027218/1) and LightForm (EP/R001715/1), for supporting aspects of this research. The authors acknowledge the use of equipment associated with the Advanced Metals Processing and Characterization themes of the Henry Royce Institute for Advanced Materials, funded through EPSRC grants EP/R00661X/1, EP/S019367/1, EP/P025021/1, and EP/P025498/1. P.B. Prangnell is grateful to the Royal Academy of Engineering, UK, and Airbus for supporting his research through the Airbus-University of Manchester Centre for Metallurgical Excellence

Randomised nano-/micro- impact testing – A novel experimental test method to simulate erosive damage caused by solid particle impacts

A novel randomised nano-/micro-scale impact test method has been developed to experimentally simulate particulate erosion where statistically distributed impacts with defined energy occur sequentially within the test area. Tests have been performed on two brittle glasses (fused silica and BK7) to easily highlight the interaction between impacts, as well as on two ceramic thermal barrier coating systems (TBCs, yttria stabilised zirconia, 7YSZ, and gadolinium zirconate, GZO) that experience erosion in service. Differences in erosion resistance were reproduced in the randomised impact tests, with GZO less impact resistant than 7YSZ, and BK7 significantly worse than fused silica. The impact data show that erosion resistance is influenced by different factors for the glasses (crack morphology, longer-length interaction of radial-lateral cracks in BK7 vs cone-cracking in fused silica) and TBCs (fracture toughness).Support from Innovate UK under Smart Award project #10020751, High temperature tools for designing sustainable erosion resistant coatings, is gratefully acknowledged

Investigating the influence of sulphur amendment and temperature on microbial activity in bioremediation of diesel-contaminated soil

This study investigated the effectiveness of incorporating sulphur (S) with nitrogen (N) and phosphorus (P) for enhancing microbial activity in diesel-contaminated soil during ex-situ bioremediation. While N and P amendments are commonly used to stimulate indigenous microorganisms, the potential benefits of adding S have received less attention. The study found that historically contaminated soil with a moderate concentration of total petroleum hydrocarbons (TPH; 1270 mg/kg) did not have nutrient limitation, and incubation temperature was found to be more critical for enhancing microbial activity. However, soil spiked with an additional 5000 mg/kg of diesel showed increased activity following NP and NPS amendment. Interestingly, NPS amendment at 10 °C resulted in higher microbial activity than at 20 °C, indicating the potential for a tailored nutrient amendment approach to optimize bioremediation in cold conditions. Overall, this study suggests that incorporating S with N and P can enhance microbial activity in diesel-contaminated soil during ex-situ bioremediation. Furthermore, the study highlights the importance of considering incubation temperature in designing a nutrient amendment approach for bioremediation, especially in cold conditions. These findings can guide the design and implementation of future effective bioremediation strategies for petroleum hydrocarbon-contaminated soil.The authors thank ERS and the BBSRC NIBB's Environmental Biotechnology Network, (EBNET, grant reference BB/S009795/1) for funding this research project

Critical assessment of the lattice Boltzmann method for cavitation modelling based on single bubble dynamics

The lattice Boltzmann Method (LBM) is recognised as a popular technique for simulating cavitation bubble dynamics due to its simplicity. In the validation of LBM results, the Rayleigh-Plesset (R-P) equation is commonly employed. However, most studies to date have neglected the impact of simulation settings on the predictions. This article sets out to quantify the impact of LBM domain size and bubble size, and the initial conditions of the R-P equations on the predicted bubble dynamics. First, LBM results were validated against the classical benchmarks of Laplace’s law and Maxwell’s area construction. LBM results corresponding to these fundamental test cases were found to be in satisfactory agreement with theory and previous simulations. Secondly, a one-to-one comparison was considered between the predictions of the LBM and the R-P equation. The parameters of the two models were matched based on careful considerations. Findings revealed that a good overlap between the predictions is observable only under certain conditions. The warming-up period of the LBM simulations, small domain size, and small bubble radius were identified as key factors responsible for the measured differences. The authors hope that the results will promote good simulation practices for cavitation simulation including both single bubbles and bubble clusters