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Guest Editorial: Electronic attack and protection for modern radar systems and radar networks
Get PDFIt is our great pleasure to present you with this IET Radar, Sonar and Navigation special issue on “Electronic Attack and Protection for Modern Radar Systems and Radar Networks”. The recent development of fast digital to analogue converters (DACs) and analogue to digital converters (ADCs), field programmable gate arrays (FPGAs) and parallel computing has contributed to the development of modern radars that, at the same time, can also be more easily attacked using digital radio frequency memories (DRFMs). The development of passive and netted multiband, multistatic, multichannel radars has also changed the EW scenario significantly. Classical EA can be less effective against passive, multistatic and multichannel radars and, as a result, new methods and new technologies have to be developed for effective countermeasures. Multichannel and multistatic jammers have also started to play a role in the EW scene. The aim of this special issue was to gather some of the most recent work in this area. The result is a collection of 12 interesting and timely papers aiming to address current technical challenges in electronic warfare. The papers included in this collection covers areas around the more general electronic warfare context as well as address specific challenges of electronic attack and electronic protection as summarised below.IET Radar, Sonar & Navigatio
Urban air quality management at low cost using micro air sensors: a case study from Accra, Ghana
Get PDFUrban air quality management is dependent on the availability of local air pollution data. In many major urban centers of Africa, there is limited to nonexistent information on air quality. This is gradually changing in part due to the increasing use of micro air sensors, which have the potential to enable the generation of ground-based air quality data at fine scales for understanding local emission trends. Regional literature on the application of high-resolution data for emission source identification in this region is limited. In this study a micro air sensor was colocated at the Physics Department, University of Ghana, with a reference grade instrument to evaluate its performance for estimating PM2.5 pollution accurately at fine scales and the value of these data in identification of local sources and their behavior over time. For this study, 15 weeks of data at hourly resolution with approximately 2500 data pairs were generated and analyzed (June 1, 2023, to September 15, 2023). For this time period a coefficient of determination (r2) of 0.83 was generated with a mean absolute error (MAE) of 5.44 μg m–3 between the pre local calibration micro air sensor (i.e., out of the box) and the reference-grade instrument. Following currently accepted best practice methods (see, e.g., PAS4023) a domain specific (i.e., local) calibration factor was generated using a multilinear regression model, and when this factor is applied to the micro air sensor data, a reduction, i.e. improvement, in MAE to 1.43 μg m–3 was found. Daily variation was calculated, a receptor model was applied, and time series plots as a function of wind direction were generated, including PM2.5/PM10 ratio scatter and count plots, to explore the utility of this observational approach for local source identification. The 3 data sets were compared (out of the box, domain calibrated, and reference-grade) and it was found that although there were variations in the data reported, source areas highlighted based on these data were similar, with input from local sources such as traffic emissions and biomass burning. As the temporal resolution of observational data associated with these micro air sensors is higher than for reference grade instruments (primarily due to costs and logistics limitations), they have the potential to provide insight into the complex, often hyperlocalized sources associated with urban areas, such as those found in major African cities.United States Department of State, National Science Foundation, European Commission, Environmental Protection AgencyThis work was unfunded. It is considered a contribution to knowledge from Clean Air One Atmosphere to support cleanair solutions in logistically difficult environments using science and micro air sensors based on result-oriented collaboration and team science. IMT Nord Europe acknowledges financial support from the Labex CaPPA project (ANR-11-LABX-0005-01), which is funded by the French National Research Agency(ANR) through the Programme d’Investissement d’Avenir(PIA), the Regional Council “Hauts-de-France”, and the European Regional Development Fund (ERDF).ACS ES&T Ai
A ROS-based control framework for simulating locomotion of a multi-arm space assembly robot
Get PDFThis paper proposes a ROS-based control framework for simulating the locomotion of a multi-arm space robot on a planar space structure. This framework has applications in technology demonstrations of space structure assembly, construction, and maintenance where a multi-arm robot has to traverse on a space structure building blocks known as Spatial Reticular Structures (SRS) to perform the space operation. The framework sets the desired environment and SRS structures in the first step and devises two movement primitives to achieve locomotion on the structure. A ROS-Gazebo-based simulation architecture is presented to execute the proposed control framework. The viability and limitations of the proposed control framework are discussed with simulation results.Towards Autonomous Robotic Systems 2024Lecture Notes in Computer Scienc
Toxicity, bioaccumulation and mitigating strategies of heavy metals stress on morpho-physiology of spinach
Get PDFThe purpose of this review was to look into the different ways that heavy metal stress affects spinach, and how hazardous they are to soil, people's health, and plant ecosystems. Heavy metals in soil are caused by anthropogenic and industrial activity, and when they accumulate in food chains, they pose a major risk to human health. This paper presents an overview of heavy metals' negative impacts on soil fertility, plant physiology, and human health. Using spinach as a model plant, it is simple to cultivate and maintain, making it a diverse choice for studying how plants respond to stresses such as heavy metals. They describe how heavy metal stress affects spinach morphology and physiology, including absorption, detoxification, and translocation throughout the plant system. Understanding these procedures is critical when assessing the potential risks associated with the accumulation of hazardous components in spinach's edible parts. This review investigates the impact of heavy metal stress on the nutritional quality and yield of spinach after metal exposure. It is critical to investigate numerous strategies for reducing heavy metal stress in spinach, including soil remediation approaches, phytoremediation capabilities, and genetic procedures aimed to increase plant resistance to metals. The goal of this overview is to shed light on the mechanisms underlying the effects of heavy metals on spinach and to propose strategies to alleviate them, thereby protecting agricultural sustainability and public health (Fig. 1).Discover Plant
End-to-end identification of autoregressive with exogenous input (ARX) models using neural networks
Get PDFTraditional parametric system identification methods usually rely on apriori knowledge of the targeted system, which may not always be available, especially for complex systems. Although neural networks (NNs) have been increasingly adopted in system identification, most studies have failed to derive interpretable parametric models for further analysis. In this paper, we propose a novel end-to-end autoregressive with exogenous input (ARX) model identification framework using NNs. An order-wise neural network structure is introduced and trained using a multitask learning approach to simultaneously identify both the model terms and coefficients of the ARX model. Through testing with various neural network backbones and training data sizes in different scenarios, we empirically demonstrate that the proposed framework can effectively identify an arbitrary stable ARX model with finite simulation training data. This study opens up a new research opportunity for parametric system identification by harnessing the power of deep learning.Machine Intelligence Researc
Contribution of data acquired from spectroscopic, genomic and microbiological analyses to enhance mussels’ quality assessment
Get PDFIn this study, a large amount of heterogeneous data (i.e., microbiological, spectral and Next Generation Sequencing data) were obtained analyzing mussels of different species and origin, to acquire a comprehensive view about the quality and safety of these products. More specifically, spectral data were collected through Fourier transform Infrared (FTIR) spectroscopy, while the overall profile of microorganisms present in these samples, affecting quality and safety of mussels throughout storage, was determined through Next Generation Sequencing (NGS) using 16S rRNA metabarcoding analysis. In parallel, conventional microbiological analysis for the estimation of culturable spoilage microorganisms (total aerobes, Pseudomonas spp., B. thermosphacta, Shewanella spp. and Enterobacteriaceae) was applied. Different machine learning algorithms, namely Partial Least Square (PLS), Support Vector Machines (SVM), k-Nearest Neighbors (kNN), Random Forest (RF) Neural Networks (NN)) were applied accordingly, to assess the potential of FTIR and NGS data to provide useful information about mussels’ microbiological quality. Microbial counts ranged from 3.5 to 9.0 log CFU/g, while NGS revealed several bacterial genera such as Pseudoalteromonas, Psychrobacter, Acinetobacter, Pseudomonas, B. thermosphacta, Psychrobacter, Kistimonas, Psychrilyobacter to affect the quality of mussels, depending on the mussel species, batch and storage conditions. According to the performance metrics, the SVM algorithm in tandem with FTIR achieved the highest prediction accuracy for microbial counts in M. chilensis samples (Rsquared; 0.89, RMSE; 0,74), while in the case of predicting the abundance of microbial genera using spectroscopic data, the best performing algorithm varied by bacterial genus. Indicatively, in M. chilensis, RF, kNN and NN performed better in predicting Enterococcus, Enhydrobacterium and Pseudoalteromonas, respectively (Rsquared = 0.92, 0.93, 0.99). Associations between genomics data and specific spectral regions were further investigated, revealing certain spectral regions that are associated with mussels’ quality and safety. The application of “multi-omics” in seafood supply chain can provide insightful information about mussels’ quality and safety compared to the methodologies followed in current quality and safety management systems.European CommissionFood Research Internationa
Trajectory intent prediction of autonomous systems using dynamic mode decomposition
Get PDFProliferation of autonomous systems have increased the threat space and the economic risk in several national infrastructures, e.g., at airports. Therefore, reliable detection of their intention is paramount to ensure smooth operation of national services and societal safety. This article reports a data-driven trajectory intent prediction algorithm which is based on a linear model structure of the autonomous system dynamics obtained from a dynamic mode decomposition algorithm. The model computation is enhanced by two sources of physics informed knowledge associated to the energy functional. Two different prediction algorithms that consider fixed or time-varying references are designed in terms of the availability of control input measurements. Rigorous theoretical results are provided to support the approach using matrix decomposition and optimization techniques. Simulation and experimental studies are carried out to verify the effectiveness of the proposal.Royal Academy of Engineering, Engineering and Physical Sciences Research Council, UK Research and InnovationEngineering and Physical Sciences Research Council (Grant Number: EP/V026763/1).Royal Academy of Engineering and the Office of the Chief Science Adviser for National Security under the U.K. Intelligence Community Postdoctoral Research Fellowship Programme.IEEE Transactions on Systems, Man, and Cybernetics: System
Facilitating lean implementation through change management
Get PDFImplementing lean manufacturing can be challenging and requires all stakeholders to be engaged. Several frameworks have been presented in the last decades, with most of them being conceptual and failing to consider that change inertia exists. In the present paper, a social change management model is used to develop a lean implementation framework, with each step prescribed for the practitioners. The elements of the model are selected after a series of workshops, a thorough literature review, and the development of an interpretive structural model for this reason. The model was validated through experts’ opinions and presents an alternative approach to the established lean implementation roadmaps available in the literature.34th CIRP Design Conference 2024Procedia CIR
Randomness-restricted diffusion model for ocular surface structure segmentation
Get PDFOcular surface diseases affect a significant portion of the population worldwide. Accurate segmentation and quantification of different ocular surface structures are crucial for the understanding of these diseases and clinical decision-making. However, the automated segmentation of the ocular surface structure is relatively unexplored and faces several challenges. Ocular surface structure boundaries are often inconspicuous and obscured by glare from reflections. In addition, the segmentation of different ocular structures always requires training of multiple individual models. Thus, developing a one-model-fits-all segmentation approach is desirable. In this paper, we introduce a randomness-restricted diffusion model for multiple ocular surface structure segmentation. First, a time-controlled fusion-attention module (TFM) is proposed to dynamically adjust the information flow within the diffusion model, based on the temporal relationships between the network’s input and time. TFM enables the network to effectively utilize image features to constrain the randomness of the generation process. We further propose a low-frequency consistency filter and a new loss to alleviate model uncertainty and error accumulation caused by the multi-step denoising process. Extensive experiments have shown that our approach can segment seven different ocular surface structures. Our method performs better than both dedicated ocular surface segmentation methods and general medical image segmentation methods. We further validated the proposed method over two clinical datasets, and the results demonstrated that it is beneficial to clinical applications, such as the meibomian gland dysfunction grading and aqueous deficient dry eye diagnosis.IEEE Transactions on Medical Imagin
Effect of operation time on the performance and accuracy of the Condor reflectometer
Get PDFThis paper evaluates the potential effect that operation lifetime could have on the accuracy and reproducibility of the Condor reflectometer. For this purpose, three Condors with different operation lifetimes have been used and compared in this study. In addition to the device’s operation lifetimes, reproducibility and the repeatability of the measurements have also been evaluated. Silvered glass mirrors at different states have been used, e.g., clean, soiled and eroded in order to evaluate the effect of the surface properties on the difference reported using different devices. The obtained results have shown that the difference in specular reflectance reported by the three different Condors is more noticeable in case of soiled and eroded glass mirrors compared to clean sample. This could be linked to the surface roughness more than to the years of operation of the device itself.SolarPACES 202