Multiplicative Algebraic Reconstruction Technique Research Articles

Improving depth uncertainty in plenoptic camera-based velocimetry

This work describes the development of a particle tracking velocimetry (PTV) algorithm designed to improve three-dimensional (3D), three-component velocity field measurements using a single plenoptic camera. Particular focus is on mitigating the longstanding depth uncertainty issues that have traditionally plagued plenoptic particle image velocimetry (PIV) experiments by leveraging the camera’s ability to generate multiple perspective views of a scene in order to assist both particle triangulation and tracking. 3D positions are first estimated via light field ray bundling (LFRB) whereby particle rays are projected into the measurement volume using image-to-object space mapping. Tracking is subsequently performed independently within each perspective view, providing a statistical amalgamation of each particle’s predicted motion through time in order to help guide 3D trajectory estimation while simultaneously protecting the tracking algorithm from physically unreasonable fluctuations in particle depth positions. A synthetic performance assessment revealed a reduction in the average depth errors obtained by LFRB as compared to the conventional multiplicative algebraic reconstruction technique when estimating particle locations. Further analysis using a synthetic vortex ring at a magnification of − 0.6 demonstrated plenoptic-PIV capable of maintaining the equivalent of 0.1–0.15 voxel accuracy in the depth domain at a spacing to displacement ratio of 5.3–10.5, an improvement of 84–89% compared to plenoptic-PIV. Experiments were conducted at a spacing to displacement ratio of approximately 5.8 to capture the 3D flow field around a rotor within the rotating reference frame. The resulting plenoptic-PIV/PTV vector fields were evaluated with reference to a fixed frame stereoscopic-PIV (stereo-PIV) validation experiment. A systematic depth-wise (radial) component of velocity directed toward the wingtip, consistent with observations from prior literature and stereo-PIV experiments, was captured by plenoptic-PTV at magnitudes similar to the validation data. In contrast, the plenoptic-PIV did not discern any coherent indication of radial motion. Our algorithm constitutes a significant advancement in enhancing the functionality and versatility of single-plenoptic camera flow diagnostics by directly addressing the primary limitation associated with plenoptic imaging.Graphical abstract

Estimation of pollutant source using source-receptor response matrix from maximum-length sequence pulse technique

Accurate estimation of pollutant sources in built environments is crucial for monitoring air quality and preventing harm to health and property. This study presented an improved method for estimating the dynamical pollutant source strength using the concentration signal monitored at the receptor point. We employed a built environment system subjected to a pollutant pulse signal in accordance with the maximum-length sequence (m-sequence) to acquire response concentration. Subsequently, the source-receptor response matrix was inversely identified based on the known pulse signal and the response concentration. Then, the unknown source release rate was inversely estimated by the monitored concentration signal and previously identified response matrix. In addition, the performances of different inverse algorithms and m-sequence orders were also analyzed. Here both simulation and application to a real experiment of a wind duct were implemented, and the results demonstrated the higher-order m-sequence method exhibited better noise resistance compared to the traditional unit pulse method. In both simulation and experiment, the m-sequence pulse method reduced the root mean square error (RMSE) by 39.9 % and 70.7 %, respectively, compared to the unit pulse method. In experiment, the simultaneous multiplicative algebraic reconstruction technique (SMART) combined with the Tikhonov regularization (TR) method showed relatively stable performance.

Convergence of the Multiplicative Algebraic Reconstruction Technique for the Inconsistent System of Equations

Convergence of the Multiplicative Algebraic Reconstruction Technique for the Inconsistent System of Equations

Uniformly sampled polar and cylindrical grid approach for 2D, 3D image reconstruction using algebraic algorithm

Image reconstruction by Algebraic Methods (AM) outperforms the transform methods in situations where the data collection procedure is constrained by time, space, and radiation dose. AM algorithms can also be applied for the cases where these constraints are not present but their high computational and storage requirement prohibit their actual breakthrough in such cases. In the present work, we propose a novel Uniformly Sampled Polar/Cylindrical Grid (USPG/USCG) discretization scheme to reduce the computational and storage burden of algebraic methods. The symmetries of USPG/USCG are utilized to speed up the calculations of the projection coefficients. In addition, we also offer an efficient approach for USPG to Cartesian Grid (CG) transformation for the visualization. The Multiplicative Algebraic Reconstruction Technique (MART) has been used to process the projections data in order to determine the field function of the suggested grids. We first use cyber phantoms of various shapes, sizes and densities to test the USPG/USCG-based MART algorithm. Experimental projections data of a frog and Cu-Lump have been further exercised to validate the proposed approach. A variety of image quality measures have been evaluated to check the accuracy of the reconstruction. Results indicate that the current strategies speed up (when compared to CG-based algorithms) the reconstruction process by a factor of 2.5 and reduce the memory requirement by the factor p, the number of projections used in the reconstruction.

Tomographic optical emission spectroscopy of an atmospheric pressure plasma jet and surface ionization waves on planar and structured surfaces

In this paper, an approach for 3D plasma structure diagnostics using tomographic optical emission spectroscopy (Tomo-OES) of a nanosecond pulsed atmospheric pressure plasma jet (APPJ) is presented. In contrast to the well-known Abel inversion, Tomo-OES does not require cylindrical symmetry to recover 3D distributions of plasma light emission. Instead, many 2D angular projections are measured with intensified cameras and the multiplicative algebraic reconstruction technique is used to recover the 3D distribution of light emission. This approach solves the line-of-sight integration problem inherent to optical diagnostics, allowing recovery of localized OES information within the plasma that can be used to better infer plasma parameters within complex plasma structures. Here, Tomo-OES was applied to investigate an APPJ operated with helium in ambient air and impinging on planar and structured dielectric surfaces. Surface charging caused the guided streamer from the APPJ to transition to a surface ionization wave (SIW) that propagated along the surface. The SIW experienced variable geometrical and electrical material properties as it propagated, leading to 3D configurations that were non-symmetric and spatially complex. Light emission from He, N, and N2 were imaged at ten angular projections and the respective time-resolved 3D emission distributions in the plasma were then reconstructed. The spatial resolution of each tomographic reconstruction was 7.4 µm and the temporal resolution was 5 ns, sufficient to observe the guided streamer and the effects of the structured surface on the SIW. Emission from He showed the core of the jet and emission from N and N2 indicated effects of entrainment of ambient air. Penning ionization of N2 created a ring or outer layer of N that spatially converged to form the ‘plasma bullet’ or spatially diverged across a surface as part of a SIW. The SIW entered trenches of size 150 µm, leading to decreases in plasma light emission in regions above the trenches. The plasma light emission was higher in some regions with trenches, possibly due to effects of field enhancement.

A Double-Adaptive Adjustment Algorithm for Ionospheric Tomography

A double-adaptive adjustment algorithm (DAAA) is proposed to reconstruct three-dimensional ionospheric electron density (IED) distribution. In the DAAA method, the relaxation factor of the multiplicative algebraic reconstruction technique (MART) is first adaptively adjusted by introducing adaptive MART (AMART). To avoid the voxels without any rays traversing them becoming dependent on the initial IED values, smoothing constraints are generally imposed on the adaptive multiplicative algebraic reconstruction technique (AMART). In general, the elements of the smoothing matrices are invariant in the iterative process. They affect the accuracy and efficiency of the IED inversion. To overcome the above limitation, the adaptive adjustments of the constrained matrix elements are subsequently carried out. Both numerical simulation and actual global navigation satellite system (GNSS) experimental results validate that the accuracy and efficiency of ionospheric tomography have been improved by the DAAA method. Finally, the new algorithm is applied to reconstruct the three-dimensional structure of the ionosphere during different geomagnetic activities. The comparisons show that the vertical profiles of the DAAA method are in agreement with those recorded from the ionosonde, and the inverted vertical total electron content (VTEC) of the DAAA method also agrees with the ionospheric products of center for orbit determination in Europe (CODE) during geomagnetic quiet and geomagnetic storms. The comparisons confirm the reliability and superiority of the DAAA method.

Global Three‐Dimensional Ionospheric Tomography by Combination of Ground‐Based and Space‐Borne GNSS Data

AbstractThe inhomogeneous distribution of Global Navigation Satellite System (GNSS) data is a significant problem for global GNSS computerized ionospheric tomography (CIT). By combining ground‐based GNSS data from 460 global stations with space‐borne GNSS data onboard eleven Low‐Earth‐Orbit (LEO) satellites, we employed the EOF (empirical orthogonal function)‐based multiplicative algebraic reconstruction technique (EMART) to reconstruct the global ionospheric electron densities. In comparison with GNSS + LEO (EMART_GL) solutions, the proportion of crossed voxels for GNSS‐only (EMART_G) solutions can rise by up to 12.54%, from 56.83% to 69.37%. Validation experiments were conducted under quiet and disturbed conditions using GNSS slant total electron content (STEC) references that were not previously used in ionospheric tomography. The EMART_GL solutions outperformed the EMART_G solutions when compared to the STEC references under quiet and disturbed conditions. The root mean square values of the CIT‐derived STEC for the EMART_G solutions can be decreased by 31.3% and 30.3% on average under quiet and disturbed conditions, respectively. The vertical TEC references from the Jason‐2 satellite were also used for validation over the ocean areas. Due to insufficient data over the ocean areas, the EMART_GL solutions showed no significant improvements. The reconstructed 3D IEDs were further validated with the COSMIC electron density profiles. Generally, the tomographic results of the EMART_GL solutions were more consistent with the COSMIC profiles when compared to those of the EMART_G solutions and the NeQuick‐2 model. It demonstrates that it is beneficial for global ionospheric tomography by combining ground‐based and space‐borne data.

Assessment of tomographic window and sampling rate effects on GNSS water vapor tomography

The ground-based Global Navigation Satellite System (GNSS) water vapor tomography is increasingly important in GNSS meteorology. As the multi-GNSS and more ground-based GNSS sites can be incorporated into the regional water vapor tomographic model, determining the tomographic window and sampling rate is crucial for the modelling of the water vapor tomography. These two factors affect not only the number of available signal rays from the satellites, but also the number of tomographic voxels crossed by the signal rays. This study uses Hong Kong as the research area to explore the impact of 12 schemes with different tomographic window and sampling rate on the three water vapor tomography methods, including Least squares, Kalman filtering, and Multiplicative Algebraic Reconstruction Technique (MART). Numerical results show that the tomographic results with the three methods get better as the width of the tomographic window decreases and the sampling rate increases in these 12 schemes, and it is found that the Least squares method is most affected by the two factors, followed by Kalman filtering and MART methods. It is recommended to set a tomographic window width of 10 min and a sampling rate of 300 s in a GNSS water vapor tomographic experiment with dense GNSS site like Hong Kong.

Particle field deconvolution multiplicative algebraic reconstruction technique for tomographic particle image velocimetry reconstruction

Precision tomographic reconstruction is critical for obtaining high-accuracy velocity measurements in tomographic particle image velocimetry. Traditional tomographic reconstruction methods, such as the multiplicative algebraic reconstruction technique (MART), can only be applied at low particle concentrations, limiting the spatial resolution of velocity measurements. In addition, the actual shape of the particles is not reconstructed well due to the limited views. In this study, we propose a novel method named particle field deconvolution MART (Deconv-MART) to repair the shape of actual particles while suppressing ghost particles reconstructed by MART iterations. This method first uses the Gaussian particle shape prior to estimate the convolution kernel obtained by MART reconstruction. Then, the estimated kernel is utilized to deconvolute the particle field and suppress ghost particles based on the prior information of the lower intensity of ghost particles as well as the sparsity of the particle field. Reconstruction fields are estimated with numerical and real experiments, and the results are compared with the results of advanced reconstruction methods. Comparisons of reconstruction demonstrate that the proposed method is effective at suppressing ghost particles and restoring the shape of actual particles. Comparisons of velocity measurements reveal that Deconv-MART has good performance and high measurement accuracy.

Ionospheric Tomography: A Compressed Sensing Technique Based on Dictionary Learning

GNSS (Global Navigation Satellite System) observation insufficiency limits the development of the voxel-based computerized ionospheric tomography (CIT) technique. Electron densities of voxels without observation cannot be accurately estimated by the commonly used algebraic reconstruction techniques. In this study, we proposed a compressed sensing technique (CST) based on dictionary learning for ionospheric tomography. Specifically, the K-SVD (singular value decomposition) algorithm was used for dictionary learning based on training sets that are derived from the NeQuick model, hereafter referred to as the CST_NeQuick algorithm. K-SVD uses the orthogonal matching pursuit (OMP) for sparse coding and the SVD approach for dictionary updating. Both simulations and real experiments demonstrated the feasibility and superiority of the CST algorithm when compared to the widely used multiplicative algebraic reconstruction technique (MART). It was found that the CST_NeQuick algorithm’s tomographic performances were mostly superior to those of the MART algorithm in comparison with the independent slant total electron content (STEC) references. Another CST-based tomographic experiment was performed by using the MART-based solutions for dictionary learning, hereafter referred to as the CST_MART algorithm. It showed that the CST_MART algorithm can reduce the average root mean square (RMS) of the CIT-derived STEC by 37.3 % and 20.2 %, respectively, when compared to the MART and CST_NeQuick algorithms. Besides, the CST_MART algorithm’s electron density profiles also showed more agreement with the electron density profiles that were derived from radio occultation data.

Modified Polar Grid-based Accelerated Image Reconstruction Technique for X-ray CT

Image reconstruction by projections is basically a mathematical problem. The speed and accuracy of the reconstruction mainly depend upon, (1) discretization scheme (2) solution technique. Solution by Algebraic Method (AM) is the most promising technique for manufacturing the low-cost (few detectors arrangement) X-ray CT set-up. Slow converging rate and huge memory requirement are the two major drawbacks of the AM algorithms. Projection coefficient calculations and their storages are major time and storage-consuming processes of the algebraic methods. Among all algebraic methods, the Multiplicative Algebraic Reconstruction Technique (MART) is more effective because it maximizes the entropy of the image space. The conventional MART algorithm uses square grids (SG) in the discretization process of the image reconstruction. In the present work, we employed a modified polar grid to cope with the shortcomings of the MART method. In this new discretization scheme, only the projection coefficients of the first view, need to be calculated. Projection coefficients for other views can be easily calculated by using the symmetries of the modified polar grid. This method significantly reduces the reconstruction time and storage requirement of the MART algorithm. In addition, we also presented the direct method of polar to square grid transformation for the visualization of the reconstructed images. We tested the proposed method to fan-beam geometry for 2D image reconstruction. Structural similarity index(SSIM) and L2 error are used for quality assessment of the reconstructed images.

Hydrodynamics characteristics of non-uniform inflow in reactor coolant pump based on time-resolved tomographic particle image velocimetry

Non-uniform inflow at the inlet of a reactor coolant pump generates performance variations, such as in the head, efficiency, cavitation, and vibrations. The three-dimensional velocity and pressure for non-uniform inflow are reconstructed via time-resolved tomographic particle image velocimetry to evaluate its effect on the reactor coolant pump. Five volume velocity fields were reconstructed through the multiplicative algebraic reconstruction technique, which constitutes the entire non-uniform flow field. The statistics of the velocity fields were used to study the non-uniform inflow characteristics. The non-uniform inflow contains two large-scale vortices in the form of counter-rotating vortex pairs. The influence of non-uniform inflow on the performance of the pump may be caused by the non-uniform pressure field. For power fluctuations during reactor operations, this non-uniform pressure distribution may generate a low-pressure region on the impeller, which induces cavitation. The non-uniform inflow shown by inhomogeneous vectors was evaluated statistically through the turbulent kinetic energy, which represents the velocity variance in each direction. For a constant head, the non-uniformity of the flow field increased with the flow rate, and the scale of small-scale turbulent vortices decreased. With proper orthogonal decomposition analysis, 90% of the energy region and flow structures were dominated by the previous 412 modes. Furthermore, the temporal modes 1, 2, 3, 100, 200, and 400 show that the frequency of large-scale eddy turnover was about 6.6–13.2 Hz. However, the large-scale eddy could be characterized within the first mode of the spatial distribution.

Tomographic Inversion of the Ionosphere by Rejecting Abnormal Corrections and Rays

The errors contained in slant total electron content (STEC) have a strong impact on the image generated by ionosphere tomography. This paper presents a method that rejects abnormal corrections and rays (RACR) in the multiplicative algebraic reconstruction technique (MART) algorithm by applying a correction threshold and a rejecting ratio threshold. The RACR algorithm was validated using ionosonde observations, Swarm satellite measurements, independent STEC observations and a vertical total electron content (TEC) map. Its performance was compared with the MART algorithm on both geomagnetically quiet days and disturbed days. The results show that the RACA algorithm is able to capture the main phase and the recovery phase of a storm and outperforms the MART algorithm under both geomagnetic conditions. The average improvements over the MART algorithm are 36.01%, 36.56%, 6.18%, 22.10% and 6.03% in the validation tests of the peak density of F2 layer, peak height of F2 layer, the electron density of the topside ionosphere, STEC and VTEC, respectively. The quality of the image produced by the RACR algorithm was controlled by the correction threshold and the rejection threshold. Smaller threshold values tend to make the image smoother. The RACR algorithm provides not only a way to produce a better tomographic image but also a means to detect abnormal rays.

Image Reconstruction Algorithm Using Weighted Mean of Ordered-Subsets EM and MART for Computed Tomography

Iterative image reconstruction algorithms have considerable advantages over transform methods for computed tomography, but they each have their own drawbacks. In particular, the maximum-likelihood expectation-maximization (MLEM) algorithm reconstructs high-quality images even with noisy projection data, but it is slow. On the other hand, the simultaneous multiplicative algebraic reconstruction technique (SMART) converges faster at early iterations but is susceptible to noise. Here, we construct a novel algorithm that has the advantages of these different iterative schemes by combining ordered-subsets EM (OS-EM) and MART (OS-MART) with weighted geometric or hybrid means. It is theoretically shown that the objective function decreases with every iteration and the amount of decrease is greater than the mean between the decreases for OS-EM and OS-MART. We conducted image reconstruction experiments on simulated phantoms and deduced that our algorithm outperforms OS-EM and OS-MART alone. Our algorithm would be effective in practice since it incorporates OS-EM, which is currently the most popular technique of iterative image reconstruction from noisy measured projections.

Predicting 3D distribution of soot particle from luminosity of turbulent flame based on conditional-generative adversarial networks

Optical detection has been fully applied and developed in combustion research, but its results are severely limited by detection methods and detection equipment with no expanded space. In this paper, a new method was developed to research soot formation and distribution in turbulent jet flame with Reynolds number range of 2000–8000, SSIM (structure similarity index) was selected as the metric. The main work of this paper included three parts: MART (Multiplicative Algebraic Reconstruction Technique) was used to implement the three-dimensional reconstruction of the turbulent jet flame by luminosity signal, the iteration of reconstruction algorithm would be stopped when the accuracy is greater than 0.9. C-GAN (Conditional-Generative Adversarial Network) was used to implement the prediction of two-dimensional soot signal, the results of soot prediction model showed that the prediction accuracies in different conditions are in the range of 0.83–0.91. Combining the previous two methods, the three-dimensional distribution of soot particle can be reconstructed by inputting the luminosity projections of jet flame, which provided new insight into the soot formation and distribution. The accuracy of this method can be verified by a unique validation experiment through oblique-sections of the flame, the validation accuracy is up to 0.92 in all conditions. The method can be applied to predict 3D soot field by using a well-trained deep learning model and several luminosity images to decrease the cost and limitation of experiment. It has the potential to be used for predicting other optical signals such as laser induced fluorescence and chemiluminescence if these datasets can be observed and trained. Therefore, this method is expected to be a solution for the visualization research of actual combustion devices.

First Flight of the EUV Snapshot Imaging Spectrograph (ESIS)

The Extreme-ultraviolet Snapshot Imaging Spectrograph (ESIS) launched on a sounding rocket from White Sands Missile Range on 2019 September 30. ESIS is a computed tomography imaging spectrograph (CTIS) designed to map emission line profiles across a wide field of view, revealing the structure and dynamics of small-scale transient events that are prevalent at transition region temperatures. In this paper, we review the ESIS instrument, mission, and data captured. We demonstrate how this unique data set can be interpreted qualitatively and further present some initial quantitative inversions of the data. Using a multiplicative algebraic reconstruction technique, we combine information from all four ESIS channels into a single spatial–spectral cube at every exposure. We analyze two small explosive events in the O v 629.7 Å spectral line with jets near ±100 km s−1 that evolve on 10 s timescales and vary significantly over small spatial scales. Intriguingly, each of these events turns out to be a bimodal (red+blue) jet with outflows that are asymmetric and unsynchronized. We also present a qualitative analysis of a small jetlike eruption captured by ESIS and draw comparisons to previously observed mini-filament eruptions. In 5 minutes of observing time, ESIS captured the spatial and temporal evolution of tens of these small events across the ∼11.′5 field of view, as well as several larger extended eruptions, demonstrating the advantage of CTIS instruments over traditional slit spectrographs in capturing the spatial and spectral information of dynamic solar features across large fields of view.

Application and algorithm research of TDLAS

Tunable diode laser absorption spectroscopy (TDLAS) is extensively utilized in monitoring of trace gases in the environment. With the relative entropy tomographic reconstruction, simultaneous multiplicative algebraic reconstruction technique algorithm and optimization of existing functions and models, TDLAS has been applied to reconstruction of temperature and humidity field, combustion diagnosis, mass flow monitoring and other domains, this paper will analyze the existing TDLAS application and algorithm research.

Sensitivity Analysis of Ozone Profiles Retrieved from SCIAMACHY Limb Radiance Based on the Weighted Multiplicative Algebraic Reconstruction Technique

A detailed sensitivity analysis of ozone density profile retrieval was applied to scattering solar radiance spectra measured with the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument aboard the ENVIronmental SATellite (ENVISAT). The vertical density distribution of ozone between 10 and 69 km was obtained using the weighted multiplicative algebraic reconstruction technique and the radiative transfer model for SCIAMACHY. This study investigates the error sources for the retrieved ozone profiles, which are relevant to explain the difference between two independent instruments. The numerical simulation method was adapted to quantify the impact of various error sources on the retrieval accuracy of ozone profiles. First, the tangent height (TH) registration was found to be the largest error source. Assuming an aerosol-free atmosphere, under the condition of background aerosol, the ozone profile showed a negative deviation of ~2–10% below 40 km. With an incorrect a priori profile, ozone estimates may result in a 5–10% average error at the upper and lower boundaries. The ozone retrieval error caused by the uncertainty of surface albedo, ozone absorption cross-section, temperature, pressure profile, and low clouds was relatively small. The random error caused by the disturbance of the measurement vector obeying a Gaussian distribution did not exceed 5%. Second, the estimation of various error sources for different solar zenith angles was investigated. The error sources most strongly dependent on SZAs were aerosols, surface albedo, and clouds. Finally, the error estimation of the ozone retrieval between the northern hemisphere (NH) and the southern hemisphere (SH) was investigated, revealing that there were no strong interhemispheric differences, except for cloud height. These results can be used for interpretation of instrumental comparisons and validation of SCIAMACHY ozone profiles retrieved from different algorithms in a rigorous manner.

Multi view interferometric tomography measurements of convective phenomena in a differentially-heated nanofluid layer

ABSTRACT Three-dimensional reconstructions of thermal field in a differentially heated cylindrical cavity with dilute concentrations of nanofluids as the working medium have been reported. Experiments have been conducted in a specially designed tomography cell filled with dilute concentrations of Al2O3 nanofluids with ethylene glycol dimethacrylate (EGD) as the basefluid. The two horizontal walls of the cell have been differentially heated to apply the temperature potential across the fluid layer. Multiple projection data in the form of interferometric images have been recorded at every 20° view angle by slowly turning the cell over the range of 0 to 360°. The two-dimensional temperature fields retrieved through the quantitative analysis of interferometric images recorded at various view angles have been used to reconstruct the complete three-dimensional temperature field at select horizontal planes of the fluid layer. Iterative multiplicative algebraic reconstruction technique (MART) has been employed for tomography reconstructions. Based on the tomography reconstructions, possible fluid movement in the test cavity and the strong dependence of convective field on varying nanofluid concentrations and cavity temperature differences have been explained. Distribution of local temperature fields near the cavity wall surfaces revealed the phenomena of breakdown of large length scale fluid structures into multiple roll-like patterns with increasing concentration of nanoparticles. The distribution of the local Nusselt number, on select horizontal planes near the top and bottom wall surfaces of the cavity, highlighted the possible role of nanoparticles enhancing the heat transfer rates. An overall energy balance in the cavity was also to be seen based on the reconstructions of the local profiles of Nusselt numbers.

Influence of Reynolds number on the dynamics of rigid, slender and non-axisymmetric fibres in channel flow turbulence

We investigate experimentally the dynamics of non-axisymmetric fibres in channel flow turbulence, focusing specifically on the importance of the fibre size relative to the flow scales. To this aim, we maintain the same physical size of the fibres and we increase the shear Reynolds number. Experiments are performed in the TU Wien Turbulent Water Channel for three values of shear Reynolds number, namely 180, 360 and 720. Fibres are slender – length to diameter ratio of 120 – rigid, curved and neutrally buoyant particles and their shape ranges from low curvature – almost straight fibres – to moderate curvature. In all cases, fibre size remains small compared with the channel height (${\leqslant }1.5\,\%$). Three-dimensional and time-resolved recordings of the laser-illuminated measurement region are obtained from four high-speed cameras and used to infer fibre dynamics. With the aid of multiplicative algebraic reconstruction techniques, fibre position, orientation, velocity and rotation rates are determined. Our measurements span over the half-channel height, from wall to centre, and allow a complete characterisation of the fibre dynamics in all regions of the flow. Specifically, we measure fibre preferential distribution and orientation. We observe that the fibre dynamics is always influenced by their curvature. Through a comparison between measurements of the near-wall dynamics of the fibres and the near-wall dynamics of the flow, we identify a causal relationship between fibre velocity and orientation, and the near-wall turbulence dynamics. Finally, we have been able to provide original measurements of the tumbling rate of the fibres, for which we report the influence of fibre curvature. We underline that our measurements confirm previous findings obtained in numerical and experimental works.