Post-processing Requirements Research Articles

Adaptive contour-tracking to aid wide-field swept-source optical coherence tomography imaging of large objects with uneven surface topology.

High-speed and wide-field optical coherence tomography (OCT) imaging is increasingly essential for clinical applications yet faces challenges due to its inherent sensitivity roll-off and limited depth of focus, particularly when imaging samples with significant variations in surface contour. Here, we propose one innovative solution of adaptive contour tracking and scanning methods to address these challenges. The strategy integrates an electrically tunable lens and adjustable optical delay line control with real-time surface contour information, enabling dynamic optimization of imaging protocols. It rapidly pre-scans the sample surface to acquire a comprehensive contour map. Using this map, it generates a tailored scanning protocol by partitioning the entire system ranging distance into depth-resolved segments determined by the optical Raleigh length of the objective lens, ensuring optimal imaging at each segment. Employing short-range imaging mode along the sample contour minimizes data storage and post-processing requirements, while adaptive adjustment of focal length and reference optical delay line maintains high imaging quality throughout. Experimental demonstrations show the effectiveness of the adaptive contour tracking OCT in maintaining high contrast and signal-to-noise ratio across the entire field of view, even in samples with significantly uneven surface curvatures. Notably, this approach achieves these results with reduced data volume compared to traditional OCT methods. This advancement holds promise for enhancing OCT imaging in clinical settings, particularly in applications requiring rapid, wide-field imaging of tissue structures and blood flow.

Speeding up UAV-based crop variability assessment through a data fusion approach using spatial interpolation for site-specific management

Innovations in precision agriculture enhance complex tasks, reduce environmental impact, and increase food production and cost efficiency. One of the main challenges is ensuring rapid information availability for autonomous vehicles and standardizing processes across platforms to maximize interoperability. The lack of drone technology standardisation, communication barriers, high costs, and post-processing requirements sometimes hinder their widespread use in agriculture. This research introduces a standardized data fusion framework for creating real-time spatial variability maps using images from different Unmanned Aerial Vehicles (UAVs) for Site-Specific Crop Management (SSM). Two spatial interpolation methods were used (Inverse Distance Weight, IDW, and Triangulated Irregular Networks, TIN), selected for their computational efficiency and input flexibility. The proposed framework can use different UAV image sources and offers versatility, speed, and efficiency, consuming up to 98 % less time, energy, and computing requirements than standard photogrammetry techniques, providing rapid field information, allowing edge computing incorporation into the UAV data acquisition phase. Experiments conducted in Spain, Serbia, and Finland in 2022 under the H2020 FlexiGroBots project demonstrated a strong correlation between results from this method and those from standard photogrammetry techniques (up to r = 0.93). In addition, the correlation with Sentinel 2 satellite images was as strong as that obtained with photogrammetry-based orthomosaics (up to r = 0.8). The proposed approach could support irrigation leak detection, soil parameter estimation, weed management, and satellite integration for agriculture.

Blast-Assisted Subsurface Characterisation Using a Novel Distributed Acoustic Sensing Setup Based on Geometric Phases.

A novel DAS setup based on geometric phases in coherent heterodyne detection is applied for the first time to the characterisation of the Earth's subsurface. In addition, an optimisation of the proposed setup in terms of its spatial resolution is also presented for the first time. The surface waves are generated by strong blasts of 25 kg of explosives at a dedicated test site. A 10 km dark fiber link in the vicinity of the test site connected to the test setup records the resulting strain signals. The spike-free and low-noise strain data thus obtained minimize post-processing requirements, making the setup a candidate for real-time seismic monitoring. An analysis of the dispersion characteristics of the generated surface waves is performed using a recently reported optimised seismic interferometric technique. Based on the dispersion characteristics, the shear wave velocities of the surface waves as a function of the depth profile of the Earth's crust are determined using an optimised evolutionary algorithm.

GLCM-Based FBLS: A Novel Broad Learning System for Knee Osteopenia and Osteoprosis Screening in Athletes

Due to the physical strain experienced during intense workouts, athletes are at a heightened risk of developing osteopenia and osteoporosis. These conditions not only impact their overall health but also their athletic performance. The current clinical screening methods for osteoporosis are limited by their high radiation dose, complex post-processing requirements, and the significant time and resources needed for implementation. This makes it challenging to incorporate them into athletes’ daily training routines. Consequently, our objective was to develop an innovative automated screening approach for detecting osteopenia and osteoporosis using X-ray image data. Although several automated screening methods based on deep learning have achieved notable results, they often suffer from overfitting and inadequate datasets. To address these limitations, we proposed a novel model called the GLCM-based fuzzy broad learning system (GLCM-based FBLS). Initially, texture features of X-ray images were extracted using the gray-level co-occurrence matrix (GLCM). Subsequently, these features were combined with the fuzzy broad learning system to extract crucial information and enhance the accuracy of predicting osteoporotic conditions. Finally, we applied the proposed method to the field of osteopenia and osteoporosis screening. By comparing this model with three advanced deep learning models, we have verified the effectiveness of GLCM-based FBLS in the automatic screening of osteoporosis for athletes.

Amorphous-crystalline nanostructured Nd-Fe-B permanent magnets using laser powder bed fusion: Metallurgy and magnetic properties

Laser powder-bed fusion (PBF-LB), a class of additive manufacturing (AM), has attracted wide interest in the production of Nd-Fe-B permanent magnets, benefiting from the minimisation of waste of rare-earth elements and the post-processing requirements. Most research on PBF-LB Nd-Fe-B has focused on reducing defects in printed parts alongside the improvement of the resultant magnetic properties. Detailed analysis of the microstructure that results in permanent magnetic properties is yet to be published. In this research, a combination of high-resolution microstructural investigations was conducted for this purpose. For the first time, an in-depth analysis of the grain structure in terms of morphology, size distribution, and texture is presented and correlated to the permanent magnetic performance. Melt pools showed a hierarchical grain size distribution of primary Nd2Fe14B phase grains with a polygonal morphology and random crystalline alignment, in addition to a small amount of Nd-rich and Nd-lean precipitates in the matrix of the Ti-rich amorphous grain boundaries. The permanent magnetic properties of this material are mainly determined by the nanostructured Nd2Fe14B grains and the amorphous Ti-rich iron-based intergranular phase but could be weakened by precipitates that act as magnetic pores. Remelting during PBF-LB led to the transformation of the coarse grains of the previously solidified layer to fine ones, favourable for the permanent magnetic properties. The mechanisms of these complex phase formations and transformations during processing and the development of the nanocrystalline microstructure are elucidated in this paper as a basis for informing the optimisation process for microstructural development.

Establishing and Leading a 3D Postprocessing Radiology Lab: A Managerial and Leadership Perspective.

The rapid acquisition of larg volumes of thin-section CT images has created a considerable need and interest for 3D postprocessing during the interpretation of medical imaging. As a result of the increasing number of postprocessing applications, requiring diagnostic radiologists to perform postprocessing is no longer realistic. This article is a comprehensive review of medical resources regarding establishing a postprocessing radiology laboratory. Besides, leadership and managerial aspects have been covered through a professional business lens. In large-volume settings, a dedicated 3D postprocessing lab ensures the quality, reproducibility, and efficiency of images. Adequate staffing is necessary to fulfill the postprocessing requirements. Educational and experience requirements for 3D technologists may vary among different running laboratories. To evaluate the establishment and running of a 3D lab, it is beneficial to implement diagnostic radiology cost-effectiveness tools. Although establishing a 3D lab has many benefits, certain challenges should be considered. Outsourcing or offshoring may serve as alternatives for establishing a postprocessing laboratory. Building and operating a 3D lab is a significant change in healthcare facilities, and it is crucial for organizations to be aware of the strong resistance toward alternatives the status quo, known as the status quo trap. The change process has essential steps, and skipping the steps creates an illusion of speed but never produces satisfactory results. The organization should ensure the engagement of all interested parties in the whole process. Moreover, a clear vision and proper communication of the vision are vital, and it is crucial to value small wins and ensure expectation clarity in leading the lab during the process.

Sub-second synthesis of silver nanoparticles in 3D printed monolithic multilayered microfluidic chip: Enhanced chemiluminescence sensing predictions via machine learning algorithms

Futuristic microfluidics will require alternative ways to extend its potential in vast areas by integrating various facets such as automation of different subsystems, multiplexing, incorporation of cyber-physical capabilities, and rapid prototyping. On the rapid prototyping aspect, for the last decade, additive manufacturing (AM) or 3D printing (3DP) has advanced to become an alternative fabrication process for microfluidic devices, enabling industry-level abilities towards mass production. In this context, for the first time, this work demonstrates the fabrication of monolithic multilayer microfluidic devices (MMMD) from planar orientation (1 layer) to nonplanar (4 layers) monolithic microchannels. The developed MMM device was impeccable for synthesizing highly potentialized silver nanoparticles (AgNPs) in <1 s. Moreover, the transport of chemical species with laminar flow simulations was performed on the process along with the thorough characterizations of produced AgNPs, finding the mean AgNPs particle size of around 35 nm without any post-processing requirements. The well-known catalytic activity of AgNPs was leveraged to enhance weak chemiluminescence (CL) sensing signals by >1300 %, increasing CL sensitivity. Further, machine learning (ML) predictive models encouraged to obtain the experimental parameters without human intervention iterations for target-specific applications. The proposed methodology finds the potential to save resources, time, and enables automation with rapid prototyping, providing possibilities for mass fabrications.

Surface quality improvement for 316L additive manufactured prototype based on magnetorheological polishing

Abstract. Additive manufacturing has attracted increasing attention in recent years due to its flexibility and near-net shaping advantages. Although recent advancements in metal additive manufacturing accuracy have met the post-processing requirement for dimensional tolerance, the finishing post-processing of functional surfaces must be further investigated in conjunction with material characteristics. This research aims to investigate the use of a flexible process in the polishing of additive molding samples. As an example, the surface of a 316L stainless steel sample formed by powder bed laser melting was polished using magnetorheological polishing technology. Magnetic field simulation was used to create a longitudinally staggered magnetorheological polishing tool. Surface roughness and residual stress were studied with process parameters such as abrasive content, magnetic particle content, machining gap, and spindle speed. Results show that the polishing effect is better at 4 % and 40 % abrasive and magnetic particles, respectively. The surface roughness Ra is 99 nm when the working gap is 0.6 mm, the surface roughness Ra value is the lowest when the spindle speed is 600 r min−1. The surface roughness was reduced to 61.43 nm after polishing the sample under improved processing conditions (4 % abrasive, 40 % magnetic, 0.6 mm working clearance, 600 r min−1 spindle speed). A nano-scale smooth surface can be obtained by powder bed laser melting and magnetorheological polishing of 316L stainless steel.

A Review of Additive Manufacturing Processes for Fabricating Ceramics and Composites

Abstract This article reviews additive manufacturing technologies that can be used for manufacturing ceramics and ceramic-based composites. Stereolithography and associated techniques offer the possibility of producing ceramic components with finer resolution. Powder-based techniques like binder jetting and indirect selective laser sintering can be used for designing porous ceramics. Selective laser melting offers practitioners the option of manufacturing ceramics with minimal post-processing requirements, although further research is needed to decrease its cost.

Study of emerging avenues in supply chain resilience; the case of integration of additive manufacturing with spare parts procurement

PurposeThe purpose of this research was to study the impacts of adoption of additive manufacturing (AM) for spare parts procurement, specifically in the context of supply chain resilience (SCR) especially regarding efficient spare parts inventory management. Furthermore, key narratives in the adoption of AM toward better SCR are explored.Design/methodology/approachIn-depth interviews with semi-structured open-ended questionnaire were conducted to collect primary qualitative data from 24 supply chain management (SCM) experts. Respondents consisted of experts across various industries. The data were analyzed by thematic content analysis method.FindingsThe results indicated that AM could be a suitable tool to reduce dependence on original equipment manufacturers (OEMs) for spare parts procurement. Data analysis also revealed that AM adoption might lead to significant cost and lead time reduction. Designs protected as intellectual properties (IP), substantive post-processing requirements and material compatibility were revealed to be barriers in adoption.Research limitations/implicationsThe impacts of utilizing AM for procurement of spare parts on the overall resilience of the supply chain were highlighted. Theoretical analysis of the findings was based on theoretical aspects of SCR. This was especially regarding efficient spare parts inventory management. The study results revealed the factors responsible adoption of the AM technology. A novel approach was undertaken to study the effect of AM adoption on “time-to-market” of newly launched products.Practical implicationsThe research provided insights regarding practical applications of AM adoption in spare parts procurement. The study could be beneficial to the early adopters of AM across industries for making managerial decisions. Unfamiliarity of supply chain managers with the AM technology was believed to be a major reason to adopt the technology. The study provided essential inputs regarding challenges and alternate adoption strategies of AM. Thus, the research was believed to be of potential value for creating awareness among supply chain managers regarding AM technology.Originality/valueThe research provided new insights on the impact of AM adoption in the context of SCR toward efficient spare parts inventory management. Various limiting and facilitating factors specific to Indian context were also explored.

UAV LiDAR Metrics for Monitoring Crop Height, Biomass and Nitrogen Uptake: A Case Study on a Winter Wheat Field Trial

Efficient monitoring of crop traits such as biomass and nitrogen uptake is essential for an optimal application of nitrogen fertilisers. However, currently available remote sensing approaches suffer from technical shortcomings, such as poor area efficiency, long postprocessing requirements and the inability to capture ground and canopy from a single acquisition. To overcome such shortcomings, LiDAR scanners mounted on unmanned aerial vehicles (UAV LiDAR) represent a promising sensor technology. To test the potential of this technology for crop monitoring, we used a RIEGL Mini-VUX-1 LiDAR scanner mounted on a DJI Matrice 600 pro UAV to acquire a point cloud from a winter wheat field trial. To analyse the UAV-derived LiDAR point cloud, we adopted LiDAR metrics, widely used for monitoring forests based on LiDAR data acquisition approaches. Of the 57 investigated UAV LiDAR metrics, the 95th percentile of the height of normalised LiDAR points was strongly correlated with manually measured crop heights (R2 = 0.88) and with crop heights derived by monitoring using a UAV system with optical imaging (R2 = 0.92). In addition, we applied existing models that employ crop height to approximate dry biomass (DBM) and nitrogen uptake. Analysis of 18 destructively sampled areas further demonstrated the high potential of the UAV LiDAR metrics for estimating crop traits. We found that the bincentile 60 and the 90th percentile of the reflectance best revealed the relevant characteristics of the vertical structure of the winter wheat plants to be used as proxies for nitrogen uptake and DBM. We conclude that UAV LiDAR metrics provide relevant characteristics not only of the vertical structure of winter wheat plants, but also of crops in general and are, therefore, promising proxies for monitoring crop traits, with potential use in the context of Precision Agriculture.

Exploring Collection of Sign Language Videos through Crowdsourcing

Inadequate sign language data currently impedes advancement of sign language ML and AI. Training on existing datasets results in limited models due to small size, and lack of diverse signers in real-world settings. Complex labeling problems in particular often limit scale. In this work, we explore the potential for crowdsourcing to help overcome these barriers. To do this, we ran a user study with exploratory crowdsourcing tasks designed to support scalability: 1) to record videos of specific content -- thereby enabling automatic, scalable labeling -- and 2) to perform quality control checks for execution consistency -- further reducing post-processing requirements. We also provided workers with a searchable view of the crowdsourced dataset, to boost engagement and transparency and align with Deaf community values. Our user study included 29 participants using our exploratory tasks to record 1906 videos and perform 2331 quality control checks. Our results suggest that a crowd of signers may be able to generate high-quality recordings and perform reliable quality control, and that the signing community values visibility into the resulting dataset.

Optimization of Tree-like Support for Titanium Overhang Structures Produced via Electron Beam Melting

Support structures play a significant role in all additive manufacturing (AM) processes. The type of supports, as well as their size, placement, and other characteristics, greatly determine how effectively and efficiently the AM process works. In order to reduce the amount of material and post-processing requirements, tree-like support structures are revolutionary support structures that have so far been employed in polymer AM and have shown good performance. However, they have not yet been investigated for metal AM processes. Therefore, this study aims to propose and optimize the tree-like support structures for additively manufactured metal (Ti6Al4V) overhangs. The overhang specimens are fabricated using Electron Beam Melting (EBM) with a variety of design and process parameters. The effect of design and process structure parameters on the performance of the support is evaluated and optimized experimentally. MOGA-ll is used to perform multi-objective optimization. The results have shown the feasibility of using tree-like support structures in metal AM. The findings of this study demonstrate how important it is to choose the proper minimum distance between rows in order to reduce support volume and support removal time. Furthermore, the most crucial factors in limiting the overhang deviation are the beam current and beam scanning speed. Additionally, the data demonstrate that lowering the beam current and raising the beam scanning speed significantly reduce deformation. Consequently, it is critical to find the right balance between beam current, beam scanning speed, minimum spacing between rows, and branch top diameters that can produce the lowest support volume, lowest support removal time, and least amount of deformation.

Deposition of a conductive nanocomposite layer on cotton fibers by injecting nanomaterial colloid into an atmospheric pressure plasma jet

Smart textiles play a decisive role in our daily lives as well as in various fields of medicine and the military. The choice of material and fabrication method are two major factors in the production of conductive textiles. This study reports the fabrication of conductive fibers by atmospheric plasma jet. Here, an innovative, one-step, and low-cost method was designed for the deposition of nanomaterial on flexible and sensitive surfaces to create electrically conductive coatings. The developed approach offers several advantages over competing techniques due to its compatibility with flexible substrates with no post-processing requirements. Graphene nanoplates, carbon nanotubes, iron chloride, and polyaniline (PANI) were chosen as the working material which was injected into the plasma gap. The colloid of the mentioned compounds was deposited by an atmospheric pressure plasma jet (APPJ). The fibers were then investigated in terms of surface morphology, electrical conductivity, and durability after washing them in aqueous solutions of non-ionic detergents. The deposited fibers exhibited an electrical conductivity of 3.66 KΩ/sq for a length of 1 cm. These obtained conductive can be utilized in various applications including electrical devices, wearable electronics, sensors, actuators, supercapacitors, and batteries.

A Unified Framework for Real Time Motion Completion

Motion completion, as a challenging and fundamental problem, is of great significance in film and game applications. For different motion completion application scenarios (in-betweening, in-filling, and blending), most previous methods deal with the completion problems with case-by-case methodology designs. In this work, we propose a simple but effective method to solve multiple motion completion problems under a unified framework and achieves a new state-of-the-art accuracy on LaFAN1 (+17% better than previous sota) under multiple evaluation settings. Inspired by the recent great success of self-attention-based transformer models, we consider the completion as a sequence-to-sequence prediction problem. Our method consists of three modules - a standard transformer encoder with self-attention that learns long-range dependencies of input motions, a trainable mixture embedding module that models temporal information and encodes different key-frame combinations in a unified form, and a new motion perceptual loss for better capturing high-frequency movements. Our method can predict multiple missing frames within a single forward propagation in real-time and get rid of the post-processing requirement. We also introduce a novel large-scale dance movement dataset for exploring the scaling capability of our method and its effectiveness in complex motion applications.

Tolerancing the laser powder bed fusion process based on machine capability measures with the aim of process control

Metal Additive Manufacturing evolves steadily towards industrialization. Laser Powder Bed Fusion (L-PBF) is a commonly used process to manufacture near net-shape metallic parts with high densities. Industrial usability requires high standards of reproducibility, which are defined by capability measures of machines and processes. Meeting the flexibility of Additive Manufacturing (AM) and aiming for mass customization, AM processes have to be under full control and must fulfill all standards.This study discusses a reverse machine capability approach with the goal of calculating achievable tolerance bands by the means of six sigma. Crucial technical part properties such as surface quality characteristics are evaluated in correspondence to the newly developed formalism. After that warning limits and intervention limits are defined and joined into a process control chart with raw data and distribution charts. The presented formalism in conjunction with surface quality characteristics helps designers to identify post-processing requirements, choose post-processing techniques and set oversizes of parts.

Multiple HD Screen-Based Virtual Studio System with Learned Mask-Free Portrait Harmonization

Virtual studio technology allows producers to combine live-action footage and computer-generated imagery and has promoted the development of film and television industry. However, the existing green screen-based virtual studio limits the visual effects just in postproduction. In this paper, we propose a new virtual studio system based on the recent emerging technology using multiple high-definition (HD) screens. Besides using traditional computer graphics like other similar systems, our system enables to capture panoramic video from the real world as the background and project it onto multiple HD screens, so as to recreate the scene in the studio. In addition, we propose a mask-free portrait harmonization network to make sure that the appearances of the foreground region and the background are consistent. Our portrait harmonization network does not need any auxiliary foreground mask and works in an end-to-end manner, which meets the postprocessing requirements for our virtual studio. Experimental result on real composite images shows that our network is superior to the state-of-the-art image harmonization method under the task of portrait harmonization. We also demonstrate our system with a CAVE system.

Transparent Object Reconstruction Based on Compressive Sensing and Super-Resolution Convolutional Neural Network

The detection and reconstruction of transparent objects have remained challenging due to the absence of their features and variations in the local features with variations in illumination. In this paper, both compressive sensing (CS) and super-resolution convolutional neural network (SRCNN) techniques are combined to capture transparent objects. With the proposed method, the transparent object’s details are extracted accurately using a single pixel detector during the surface reconstruction. The resultant images obtained from the experimental setup are low in quality due to speckles and deformations on the object. However, the implemented SRCNN algorithm has obviated the mentioned drawbacks and reconstructed images visually plausibly. The developed algorithm locates the deformities in the resultant images and improves the image quality. Additionally, the inclusion of compressive sensing minimizes the measurements required for reconstruction, thereby reducing image post-processing and hardware requirements during network training. The result obtained indicates that the visual quality of the reconstructed images has increased from a structural similarity index (SSIM) value of 0.2 to 0.53. In this work, we demonstrate the efficiency of the proposed method in imaging and reconstructing transparent objects with the application of a compressive single pixel imaging technique and improving the image quality to a satisfactory level using the SRCNN algorithm.

Online clustering-based multi-camera vehicle tracking in scenarios with overlapping FOVs

Multi-Target Multi-Camera (MTMC) vehicle tracking is an essential task of visual traffic monitoring, one of the main research fields of Intelligent Transportation Systems. Several offline approaches have been proposed to address this task; however, they are not compatible with real-world applications due to their high latency and post-processing requirements. This lack of suitable approaches motivates our proposal: A new low-latency online approach for MTMC tracking in scenarios with partially overlapping fields of view (FOVs), such as road intersections. Firstly, the proposed approach detects vehicles at each camera. Then, the detections are merged between cameras by applying cross-camera clustering based on appearance and location. Lastly, the clusters containing different detections of the same vehicle are temporally associated to compute the tracks on a frame-by-frame basis. The experiments show promising low-latency results while addressing real-world challenges such as the a priori unknown and time-varying number of targets and the continuous state estimation of them without performing any post-processing of the trajectories. Our code is available at http://www-vpu.eps.uam.es/publications/Online-MTMC-Tracking.

Development of theoretical models for dimensions of single-layer multi-track and multi-layer multi-track depositions by μ-PTA additive manufacturing process

This paper presents development and experimental validation of theoretical models to evaluate layer thickness of single-layer multi-track deposition, total height and total width of multi-layer multi-track depositions by micro-plasma transferred arc additive manufacturing (μ-PTAAM) process. The models have been developed as function of μ-PTAAM process parameters and thermal properties of deposition and substrate materials and considering formation of circular concave fillet between two adjacent deposition tracks. These features have made them generic models which can be used for any combination of deposition and substrate materials and for any form of deposition material i.e. wire, powder, particulate or their combination. Models of multi-layer multi-track deposion can alternatively used to compute number of deposition layers required to achieve given deposition height and number of deposition tracks required to achieve given deposition width. Experimental validation found prediction errors in a range from −9.7 to 9.2% for layer thickness of single-layer multi-track deposition; −5.3 to 11.1% for total height and −4.8 to 7.8% for total width of multi-layer multi-track deposition. Considerable narrow ranges of prediction errors validate the developed models and also imply that the developed model slightly under-predicts or over-predicts deposition layer thickness. It was found that deposition layer thickness increase with increase micro-plasma input power and volumetric material deposition rate, and decreases with increase in micro-plasma torch travel speed. The developed models will be useful for different coating, cladding, and surface texturing applications, freeform manufacturing of different products, minimizing post-processing requirements, and deposition material wastage thus improving techno-economic aspects of μ-PTAAM process.