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Cognitive ISAR for congested RF environments via waveform design and data recovery strategies
No full textThis paper proposes and analyzes the concept of a cognitive inverse synthetic aperture radar (ISAR) ensuring spectral compatibility in crowded electromagnetic environments. To realize the cognitive paradigm, the perception is carried out by a spectrum sensing module providing the relevant spectral parameters of the sources in the environment. The action stage employs a tailored signal design process, synthesizing a radar waveform with bespoke spectral notches, enabling ISAR imaging over a wide spectral bandwidth without interfering with the other radio frequency (RF) sources. A key enabling requirement for the proposed application is the capability to successfully recover possible gaps in the collected data. This process is carried out resorting to advanced methods based on compressed sensing recovery strategy. The capabilities of the proposed system are assessed exploiting a dataset of drone measurements in the frequency band between 13 GHz and 15 GHz. Results highlight the effectiveness of the devised architecture to enable spectral compatibility while delivering high-quality ISAR images.The work of Augusto Aubry, Antonio De Maio, and Massimo Rosamilia was partially supported by the European Union under the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, partnership on “Telecommunications of the Future” (PE00000001 - program “RESTART”).2025 IEEE Radar Conference (RadarConf25
The relationship between IoT-based information integration, decision-making uncertainty, and supply chain performance
No full textThis paper explores the interplay between the Internet of Things (IoT)-based information integration and decision-making uncertainty and their combined impact on supply chain performance. Survey data on 275 supply chain managers is analysed using partial least squares structural equation modelling to illustrate these relationships. IoT-based information integration and decision-making uncertainty is found to be a complex interaction between information accuracy, quality and efficiency; environmental complexity and richness; and demand distortion. The interactions between these factors partially mediate supply chain performance on cost management and production operations. This study provides empirical evidence for the information processing theory-based conceptualisation of the interaction between IoT-based information integration and decision-making uncertainty, and their interactions have a partial mediating role in improving supply chain performance. The research findings help supply chain managers develop a performance-oriented supply chain evaluation system to forecast the outcomes of implementing IoT-based information integration.International Journal of Logistics Research and Application
The impacts of climate change on aircraft noise near European airports
No full textThis article belongs to the Section Air Traffic and TransportationThe warmer air resulting from climate change reduces the lift force on a departing aircraft, potentially reducing its climb angle and causing more engine noise near the airport. Here, we study this phenomenon at a selection of 30 European airports in northern hemisphere summer (June–July–August). We first formulate and verify a low-complexity model of noise propagation around airports, although we emphasise that our high-level results do not explicitly depend on this agreement. The model includes anisotropic noise propagation, atmospheric absorption, and the ability to model the noise emissions from multiple engines. We study the Airbus A320, but the method could be straightforwardly generalised to other aircraft. We refer to the model as an emulator since (using Latin hypercube parameter sampling) it mimics a more comprehensive model against which it is verified. The model is used to calculate the area enclosed by the 50 dB SPL (sound pressure level) contour, A50, which agrees well with a similar metric (using the day–evening–night sound level, Lden) from the verification target, A. Using temperature and pressure data from IPCC simulations of future climate, and using a straightforward relation between climb angle and air density, we assess how climate change could affect climb angles by mid-century (2035–2064). The value of A50 is obtained by efficiently covarying (1) the engine noise at 10 m from the engines and (2) the climb angle under ‘historical’ conditions (1985–2014). The median values (across 10 climate models) of climb angle reduction in the future warmer climate are around 1–3% (depending on the airport and climate model used), but individual days can show values as high as 7.5% for the most extreme warming scenarios. By considering the variation in the absorption coefficient of the air with frequency, we find that the number of people affected by noise pollution could increase by up to 4%—as much as 2500 people for the most highly populated areas—by mid-century and that these changes are maximised for the most damaging and psychologically ‘annoying’ (low) frequencies.The work of J.W., P.D.W., and M.V. for this article is part of the AEROPLANE project, which is supported by the SESAR 3 Joint Undertaking and its founding members, Grant Agreement ID 101114682, https://cordis.europa.eu/project/id/101114682, accessed on 10 June 2025S.R. was funded by the Environmental and Networking Technologies and Applications Unit (ENTA) of the Athena Research CenterAerospac
Integrating human-centred design in AI-based automation in manufacturing: ethical challenges and findings from participatory design operator workshops
No full textUnderstanding human needs and the ways that workplace design can effectively address these needs is fundamental for improving the productivity and sustainability of a workforce. As automation increasingly replaces manual tasks in advanced manufacturing, it is crucial that human factors are considered throughout the stages of design and implementation. Initiatives like the EU AI-PRISM project aim to integrate human-centred design into new AI-based automation solutions to foster effective collaboration between humans and robots in challenging automated environments. AI-PRISM seeks to enhance productivity by combining advanced technology with human skills. To ensure that these automated solutions also meet the needs of their human operators and adhere to ethical standards, it is essential to incorporate human-centred design throughout both the design and implementation phases. The study described in this paper aims to identify the task-related challenges faced by current operators—from novices to experts—in four of the project’s use cases in manufacturing companies. To achieve this, four participatory design workshops were conducted to gather valuable feedback on the planned AI-PRISM automation solutions and capture operator requirements across different manufacturing sectors: wooden furniture, photonics and microelectronics, white goods and brewery. The workshops incorporated group interviews, lasting about one hour each, that provided a platform for operators to share their experiences, concerns, and suggestions regarding the integration of AI and robotics into their daily tasks. The results revealed that operators across all groups faced challenges with fault detection, precision, and repetitive tasks. Responses also included a number of recommendations for the design of AI-based automation to address these challenges including enhanced interaction interfaces, human oversight for precision tasks and robotic assistance. In conclusion, the findings from this research highlight the importance of aligning human skills with automated technologies, which can lead to a more efficient and effective future for manufacturing.The work has been conducted on AI-PRISM project (101058589)10th International Conference series on Robot Ethics and Standards (ICRES 2025)Crisis or Redemption with AI and Robotics? The Dawn of a New Era: Proceedings of the ICRES 2025 Conferenc
Generalised robotic anomaly detection in dynamic public environments
No full textThis paper presents a real-time anomaly detection system integrated into a ROS-enabled mobile robot for public safety monitoring in dynamic environments such as shopping centers. The system targets three critical anomalies: fallen individuals, abandoned bags, and visible knives. Our final approach combines YOLO-World v2 for object detection, YOLO-Pose for posture estimation, and GPT-4V for contextual reasoning. In controlled and public scenarios, the system achieved 88.3% accuracy and high precision (fall: 95.6%, knife: 91.7%), improving on the state of the art in fall detection recall (79.6% vs. ~49.7%). We detail the architecture, deployment strategy, and performance evaluation, and discuss latency, lighting sensitivity, and privacy challenges.2025 30th International Conference on Automation and Computing (ICAC
Reinforcement learning for UAV path planning under complicated constraints with GNSS quality awareness
Get PDFRequirements for Unmanned Aerial Vehicle (UAV) applications in low-altitude operations are escalating, which demands resilient Position, Navigation and Timing (PNT) solutions incorporating global navigation satellite system (GNSS) services. However, UAVs often operate in stringent environments with degraded GNSS performance. Practical challenges often arise from dense, dynamic, complex, and uncertain obstacles. When flying in complex environments, it is important to consider signal degradation caused by reflections (multipath) and obscuration (Non-Line of Sight (NLOS)), which can lead to positioning errors that must be minimized to ensure mission reliability. Recent works integrate GNSS reliability maps derived from pseudorange error estimations into path planning to reduce loss-of-GNSS risks with PNT degradations. To accommodate multiple constraint conditions attempting to improve flight resilience against GNSS-degraded environments, this paper proposes a reinforcement learning (RL) approach to feature GNSS signal quality awareness during path planning. The non-linear relations between GNSS signal quality in the form of dilution of precision (DoP), geographic locations, and the policy of searching sub-minima points are learned by the clipped Proximal Policy Optimization (PPO) method. Other constraints considered include static obstacle occurrence, altitude boundary, forbidden flying regions, and operational volumes. The reward and punishment functions and the training method are designed to maximize the success criteria of approaching destinations. The proposed RL approach is demonstrated using a real 3D map of Indianapolis, USA, in the Godot engine, incorporating forecasted DoP data generated by a Geospatial Augmentation system named GNSS Foresight from Spirent. Results indicate a 36% enhancement in mission success rates when GNSS performance is included in the path planning training. Additionally, the varying tensor size, representing the UAV’s DoP perception range, exhibits a positive proportion relation to a higher mission rate, despite an increment in computational complexity.European Navigation Conference 2024Engineering Proceeding
Startle and surprise in helicopter operations: reported prevalence and application of mitigation strategies
Get PDFStartle and surprise can impair pilot performance and affect flight safety. This study investigates the prevalence of different startle and surprise events among helicopter pilots, its impact on pilot stress and mental effort and the influence of training background. It also looks at currently used startle mitigation strategies and evaluates the usability of a previously proposed “Aviate, Breathe, Check (ABC)” startle management method (Piras et al. 2023). A survey among 234 helicopter pilots revealed that 96% had experienced impactful startle or surprise events during operations. Scenarios such as disorientation, tail rotor incidents, and flight into instrument meteorological conditions (IMC) were considered particularly stressful. Reported levels of stress and mental effort during startle and surprise events did not differ between pilots with higher and lower experience levels or between pilots with a different training background (military or civilian). Only 38% of pilots indicated they were specifically trained to deal with startle and surprise and only 1% were trained to use a breathing technique. Most pilots (90%) expressed openness to implementing the ABC method and expected benefits from using it. Concerns regarding time constraints in critical situations emerged as the primary objection to adopting this technique. Overall, the findings indicate that the introduction of a startle management method tailored for helicopter operations could significantly enhance safety, especially given the higher accident rates compared to fixed-wing operations. Future research should focus on developing effective training protocols that account for the unique challenges of helicopter flying.Cognition, Technology & Wor
Smart snake reconnaissance system [SASAR]
Get PDFLawson, Craig - Associate SupervisorThe motivation for this thesis is to develop a new, innovative snake-like robot
platform: the Smart Snake Reconnaissance System (SASAR). The snake is
chosen as the basis of our design due to their natural adaptability and the
versatility of environments they are able to inhabit and traverse through. We
therefore design a robot capable of a wide range of applications, from
maintenance, to exploration, to photography, in both defence and commercial
scenarios. This PhD research has especially focused on three core design pillars:
Modularity, Semi-Autonomy, and Novelty.
Four prototype joints to actuate the new robot were designed, following an
investigation which examined and assessed existing snake-like robots, and other
innovative robotic mechanisms. We analysed their physical characteristics and
investigated how the number of degrees of freedom within the mechanism affect
their performance. Additionally, research and development were conducted into
different methods of control for a snake-like robot. This led to the creation of both
2D and 3D simulations to analyse the motion of the new robot design.
Finally, we present our new snake-like robot, consisting of four independent, low
cost, 3D printed modules. Our design implements modularity through the use of
a common electrical and mechanical connector, plus we utilise a CAN bus to
communicate between the modules of the robot. Semi-autonomy has been
implemented by developing a kinematic model for the control of the robot, which
takes two simple inputs and calculates the motion required for each link in the
chain. The novelty mainly reflects in the design and manufacture of the unique
“Quaternion” style Joint module, which uses a complex three-bar parallel linkage
to actuate the spine of the robot. The performance of this Joint Module is then
analysed, compared to previous snake-robot designs. We therefore manage to
achieve our initial goal of creating a novel, modular, snake-like robot.PhD in Aerospac
AC_MAPPER: a robust approach to ATT&CK technique classification using input augmentation and class rebalancing
No full textThe detection and classification of adversarial techniques from cyber threat intelligence (CTI) text is a critical task in threat analysis and mitigation. While recent transformer-based models have shown promise, their general-purpose nature often limits effectiveness on complex, domain-specific datasets. In this paper, we present a novel model designed to address the challenges of technique classification across heterogeneous CTI datasets. The proposed method is evaluated against several baselines, including CTI-specific models as well as general-purpose transformers like SciBERT and DistilBERT. The proposed approach “AC_MAPPER” consistently outperforms all baselines in both Accuracy and F1 scores across five benchmark datasets, achieving up to 93.59% accuracy and 93.78% macro F1 on the TRAM Bootstrap dataset. It also demonstrates superior robustness on highly imbalanced and sparse datasets such as HALdata and CAPEC, where baseline models struggle. Comprehensive performance comparisons, highlights the effectiveness of proposed approach. These results underscore the potential of integrating domain-specific design with transformer architectures to advance automated CTI analysis. Our findings contribute toward more accurate and reliable threat detection systems in real-world security applications.Lunds UniversitetInternational Journal of Information Securit
Adaptations in agricultural water management in arid regions: modelling farmer behaviour and cooperation on irrigation sustainability in Morocco
No full textClimate change has disrupted weather patterns and heightened drought risks in arid and semi-arid regions, requiring adaptations to crop and irrigation strategies to sustain food production. This study integrates qualitative and quantitative approaches to examine the factors influencing farmers crop and irrigation management decisions, with a focus on groundwater management and drip irrigation adoption. Semi-structured interviews 70 farmers from Al Haouz Basin, Morocco provided insights into motivations for crop and irrigation choices. Inductive coding was used for qualitative responses, and data analysis examined how farm size and tenure influenced decision-making. An integrated modelling approach combining the theory of planned behaviour and structural equation modelling (SEM) was used to interpret drivers of irrigation management strategy. The interviews revealed that 83 % of farmers were concerned about groundwater decline, with 40 % identifying salinity as a major challenge. We found that falling groundwater levels and soil salinization have already impacted yields and raised concerns about further declines, prompting large-scale farmers to transition to more profitable and drought-resilient olive cultivation. Analysis of the SEM showed that attitudes toward drip irrigation efficiency, maintaining groundwater supply, and preventing increases in groundwater salinity influence farmers’ intentions regarding their water usage. Additionally, perceived behavioural control played a key role in shaping adoption behaviours, reinforcing the importance of structural and economic factors in decision-making. Land ownership conferred greater long-term perceived control over sustainable water use. However, qualitative findings revealed that cooperation on groundwater management was limited, with many farmers citing a lack of perceived benefits and logistical challenges, highlighting collective action challenges. Complexities related to subsidy applications and land tenure deter drip irrigation adoption, especially among smallholders, constraining climate change resilience. Our study contributes to understanding farmers' coping strategies and presents a foundation from which to develop evidence-based policy reforms enhancing agricultural and water sustainability across arid and semi-arid regions.This project is funded through a collaboration between Cranfield University (CU), Rothamsted Research (RRes), and Mohammed VI Polytechnic University (UM6P), with financial support provided by the Office Chérifien des Phosphates (OCP Group).Agricultural Water Managemen