Amplitude Contrast Research Articles

Searching medieval human remains using ground penetrating radar: A case study in Venosa (Basilicata, Southern Italy)

Controlled forensic geophysical research involving GPR has proven to be a valuable resource, and the information gathered from these studies has been applied to forensic casework. The probability of detecting a grave for a longer postmortem interval differs with the soil type and the materials added to the grave with the body. In the studied case a detailed GPR survey was conducted in the Basilica della Trinità at Venosa a village located about 40 km north from Potenza (Basilicata, Italy).Unfortunately during the restoration works of the Basilica, there was a cement spill inside a sarcophagus containing human remains. The necessity to perform the genetic analysis of medieval human remains to reconstruct the distribution of the original line of descent of the Norman noble families aimed the need to understand whether or not there was a body inside the sarcophagus and, if so, its exact position.The radar profiles from this survey showed the clear amplitude contrast anomalies, emanated from the corpses. The strongest amplitude contrasts are observed at around 0.2–0.5 m depth which is consistent with the depth of the buried corp.

Characterization of Carbonate Reservoir Potential in Salawati Basin, West Papua: Analysis of Seismic Direct Hydrocarbon Indicator (DHI), Seismic Attributes, and Seismic Spectrum Decomposition

Carbonate reservoir of Kais Formation in Salawati Basin, West Papua, is the most famous oil and gas reservoir in the eastern part of Indonesian Archipelago since 1970’s. Nowadays, new prospects in this area are more challenging and most relevant near the infrastructure of previous oil and gas fields. In this study, a relatively new seismic dataset was investigated to figure out new prospects in carbonate reservoir rocks in the area of interest. In this preliminary study, where seismic data are not supported by well data, direct hydrocarbon indicator (DHI), seismic attribute, and spectral decomposition (CWT: continuous wavelet transform) allow the authors to characterize the reservoir geometry and to predict pore fluids within the reservoir rocks. The reservoir geometry of carbonate reef of Kais Formation (C1) was identified by seismic reflectors with high amplitude contrast at the top C1. The hydrocarbon indicator was predicted by DHI where dim spots, flat spots, and polarity reversals are indicative of hydrocarbon prospects. From the attribute analysis, the attribute instantaneous amplitude detected the top carbonate C1, whereas pore fluids were predicted from high sweetness attribute. In addition, spectral decomposition CWT method confirms the top C1, identified as saturated rock by the frequency of 10 Hz, 20 Hz, and 30 Hz. Based on a seismic study in the researched area, the target zone is expected to be a very promising hydrocarbon reservoir, specifically a carbonate reservoir. As a result, the preferred well-test location is in a region with access to the Kais Formation limestone reef layer. This study can assist in reservoir characterization, especially in areas with limited well control.

Focusing Surface Acoustic Waves with a Plasmonic Hypersonic Lens.

Plasmonic nanoantennas have proven to be efficient transducers of electromagnetic to mechanical energy and vice versa. The sudden thermal expansion of these structures after an ultrafast optical pulsed excitation leads to the emission of hypersonic acoustic waves to the supporting substrate, which can be detected by another antenna that acts as a high-sensitivity mechanical probe due to the strong modulation of its optical response. Here, we propose and experimentally demonstrate a nanoscale acoustic lens comprised of 11 gold nanodisks whose collective oscillation at gigahertz frequencies gives rise to an interference pattern that results in a diffraction-limited surface acoustic beam of about 340 nm width, with an amplitude contrast of 60%. Via spatially decoupled pump-probe experiments, we were able to map the radiated acoustic energy in the proximity of the focal area, obtaining a very good agreement with the continuum elastic theory.

Ultrafast, Single-Event Ptychographic Imaging of Transient Electron Dynamics

Dynamic phenomena occurring on the ultrafast time scales are inherently difficult to image. While pump–probe techniques have been used for decades, probing nonrepeatable phenomena precludes this form of imaging. Additionally, many ultrafast phenomena, such as electron dynamics, exhibit low amplitude contrast in the optical wavelength range and thus require quantitative phase imaging. To better understand the underlying physics involved in a plethora of ultrafast phenomena, advanced imaging techniques must be developed to observe single events at an ultrafast time scale. Here, we present, to the best of our knowledge, the first ptychographic imaging system capable of observing ultrafast dynamics from a single event. We demonstrate ultrafast dynamic imaging by observing the conduction band electron population from a 2-photon absorption event in ZnSe pumped by a single femtosecond pulse. We verify experimental observations by comparing them to numeric solutions of a nonlinear envelope equation. Our imaging method represents a major step forward in ultrafast imaging, bringing the capabilities of ptychography to the ultrafast regime.

Effects of target-amplitude and background-contrast uncertainty predicted by a normalized template-matching observer.

When detecting targets under natural conditions, the visual system almost always faces multiple, simultaneous, dimensions of extrinsic uncertainty. This study focused on the simultaneous uncertainty about target amplitude and background contrast. These dimensions have a large effect on detection and vary greatly in natural scenes. We measured the human performance for detecting a sine-wave target in white noise and natural-scene backgrounds for two levels of prior probability of the target being present. We derived and tested the ideal observer for white-noise backgrounds, a special case of a template-matching observer that dynamically moves its criterion with the background contrast (the DTM observer) and two simpler models with a fixed criterion: the template-matching (TM) observer and the normalized template-matching (NTM) observer that normalizes template response by background contrast. Simulations show that, when the target prior is low, the performance of the NTM observer is near optimal and the TM observer is near chance, suggesting that manipulating the target prior is valuable for distinguishing among models. Surprisingly, we found that the NTM and DTM observers better explain human performance than the TM observer for both target priors in both background types. We argue that the visual system most likely exploits contrast normalization, rather than dynamic criterion adjustment, to deal with simultaneous background contrast and target amplitude uncertainty. Finally, our findings show that the data collected under high levels of uncertainty have a rich structure capable of discriminating between models, providing an alternative approach for studying high dimensions of uncertainty.

Facile hermetic TEM grid preparation for molecular imaging of hydrated biological samples at room temperature

Although structures of vitrified supramolecular complexes have been determined at near-atomic resolution, elucidating in situ molecular structure in living cells remains a challenge. Here, we report a straightforward liquid cell technique, originally developed for real-time visualization of dynamics at a liquid-gas interface using transmission electron microscopy, to image wet biological samples. Due to the scattering effects from the liquid phase, the micrographs display an amplitude contrast comparable to that observed in negatively stained samples. We succeed in resolving subunits within the protein complex GroEL imaged in a buffer solution at room temperature. Additionally, we capture various stages of virus cell entry, a process for which only sparse structural data exists due to their transient nature. To scrutinize the morphological details further, we used individual particle electron tomography for 3D reconstruction of each virus. These findings showcase this approach potential as an efficient, cost-effective complement to other microscopy technique in addressing biological questions at the molecular level.

The Possibility of Using Terrestrial-Based Ground Penetrating Radar (GPR) Technology for Supplying 3rd Dimension Information for A Search and Recovery Mission for Landslide Victims

This paper highlights the possibility of using GPR for providing third-dimension (depth) information to facilitate a landslide search and recovery (SAR) mission in Malaysia. The study was based on an actual use case during the 2022 landslide tragedy that occurred at the Father’s Organic Farm, Batang Kali. Two sets of MALA RAMAC X3M with shielded antennas (250Mhz and 500 Mhz) were used to survey a 1m x 1m profile interval at a 30m x 20m and 8m x 6m grid areas in Sector B on the 18th and 19th December 2022. Grid line profiles 2211-A, 2212-A, and 2213-A detected by the 250Mhz antenna showed suspicious reflection patterns. The pattern's amplitude contrast in relation to the soil background and the consistency with the average Malaysian adult stature were considered as the most likely locations of landslide victims. The location of the reflection was viewed with greater accuracy and clarity utilising time slice y-cut on 3D processing in the Reflex3DScan ReflexW module. On 21st December 2022, a victim and his two dogs were recovered by the SAR team near the suspected GPR line profiles at sector B. The suspected GPR signal reflection corroborated with the proximity where the victim was found according to the special SAR victim location map published by authorities. Since access to ground zero post excavation was restricted, on-site validation of the suspected profiles was not possible. Nonetheless, because hyperbolas were detectable at lower frequency with the maximum depth of around 8m, this paper concludes that using terrestrial-based GPR as a search and recovery alternative for buried landslide victims is still feasible. The challenge would be having a skilled operator to detect a hyperbola or abnormality in a time-critical scenario. The study also concluded that terrestrial-based GPR would, at the very least, provide first responders with situational awareness by narrowing down the SAR potential locations, excavation depths and reducing time for searching and recovering victims, as concurred by the Batang Kali SAR team. Article Details How to Cite Halim, N., Abdullah, N., Ghazali, M., & Hassan, H. (2023). The Possibility of Using Terrestrial-Based Ground Penetrating Radar (GPR) Technology for Supplying 3rd Dimension Information for A Search and Recovery Mission for Landslide Victims. International Journal of Geoinformatics, 19(5). https://doi.org/10.52939/ijg.v19i5.2669

Full-Stokes parameters detection enabled by a non-interleaved fiber-compatible metasurface.

Polarization of the optical field determines the way of light-matter interaction, which lays the foundation for various applications such as chiral spectroscopy, biomedical imaging, and machine vision. Currently, with the rise of the metasurface, miniaturized polarization detectors have attracted extensive interest. However, due to the limitation of the working area, it is still a challenge to integrate polarization detectors on the fiber end face. Here, we propose a design of compact non-interleaved metasurface that can be integrated on the tip of a large-mode-area photonic crystal fiber (LMA-PCF) to realize full-Stokes parameters detection. Through concurrent control over the dynamic phase and Pancharatnam-Berry (PB) phase, different helical phases are assigned to the two orthogonal circular polarization bases, of which the amplitude contrast and relative phase difference can be represented by two non-overlapped foci and an interference ring pattern, respectively. Therefore, the determination of arbitrary polarization states through the proposed ultracompact fiber-compatible metasurface can be achieved. Moreover, we calculated full-Stokes parameters according to simulation results and obtained that the average detection deviation is relatively low at 2.84% for 20 elucidated samples. The novel metasurface exhibits excellent polarization detection performance and overcomes the limitation of the small integrated area, which provides insights into the further practical explorations of ultracompact polarization detection devices.

Adopting Signal Processing Technique for Osteoporosis Detection Based on CT Scan Image

Machine learning (ML) and artificial intelligence (AI) are widely applied in many disciplines including medicine. Pattern recognition or automatization has been successfully implemented in various field studies. Similarly, multiple efforts have been made in medicine to implement AI/ML technology to solve medical problems, for example, for automating osteoporosis detection. In general, the success of AI/ML technology is highly dependent on the amount of available data, especially during the training stage. Feature generation is a common technique that allows the manipulation of available data for the training stages. This paper aims to study the feasibility of adopting signal-processing techniques for feature generation in medical image processing. Signal attributes from signal processing workflow were adopted and applied to image processing of CT and DEXA scanning data to differentiate between normal and osteoporotic bone. Five attributes, namely amplitude, frequency, instantaneous phase, roughness, and first derivative or contrast attributes, have been tested. An attribute index number is formulated to indicate the attribute’s strength at the selected region of interest (ROI). A case study applying these attributes to the CNN model is presented. More than five hundred CT scan images of normal and osteoporosis bone were used during the training stage to test classification performance with and without developed attributes as an input. From the ten selected CT scan images used to test the CNN model, 90% were well predicted in the scenario only utilizing the grayscale as input. However, when including the developed attributes, the CNN can predict all the images well (100% were well predicted). In conclusion, the technique adopted from the signal-processing technique has the potential to enhance feature generation in image processing, whereby the results can be used for the early application of AI/ML in osteoporosis identification. Further research testing this proposed method in different image modalities needs to be conducted to verify the robustness of the proposed method.

3D reconstruction of unstained weakly scattering cells from a single defocused hologram.

We investigate the problem of 3D complex field reconstruction corresponding to unstained red blood cells (RBCs) with a single defocused off-axis digital hologram. The main challenge in this problem is the localization of cells to the correct axial range. While investigating the volume recovery problem for a continuous phase object like the RBC, we observe an interesting feature of the backpropagated field that it does not show a clear focusing effect. Therefore, sparsity enforcement within the iterative optimization framework using a single hologram data frame cannot effectively restrict the reconstruction to the true object volume. For phase objects, it is known that the amplitude contrast of the backpropagated object field at the focus plane is minimum. We use this information available in the recovered object field in the hologram plane to device depth-dependent weights that are proportional to the inverse of amplitude contrast. This weight function is employed in the iterative steps of the optimization algorithm to assist the object volume localization. The overall reconstruction process is performed using the mean gradient descent (MGD) framework. Experimental illustrations of 3D volume reconstruction of the healthy as well as malaria-infected RBCs are presented. A test sample of polystyrene microsphere bead is also used to validate the axial localization capability of the proposed iterative technique. The proposed methodology is simple to implement experimentally and provides an approximate tomographic solution, which is axially restricted and consistent with the object field data.

Low-energy electron microscopy contrast of stacking boundaries: Comparing twisted few-layer graphene and strained epitaxial graphene on silicon carbide

Stacking domain boundaries occur in Van der Waals heterostacks whenever there is a twist angle or lattice mismatch between subsequent layers. Not only can these domain boundaries host topological edge states, imaging them has been instrumental in determining local variations in twisted bilayer graphene. Here, we analyze the mechanisms causing stacking domain boundary contrast in bright field low-energy electron microscopy (BF-LEEM) both for graphene on SiC, where domain boundaries are caused by strain, and for twisted few-layer graphene. We show that when domain boundaries are between the top two graphene layers, BF-LEEM contrast is observed due to amplitude contrast and corresponds well to calculations of the contrast based purely on the local stacking in the domain boundary. Conversely, for deeper-lying domain boundaries, amplitude contrast only provides a weak distinction between the inequivalent stackings in the domains themselves. However, for small domains, electrons from different parts of the unit cell interfere, causing a very strong phase contrast. We derive a general rule-of-thumb of expected BF-LEEM contrast for domain boundaries in Van der Waals materials.

Stimulus Design for Visual Evoked Potential Based Brain-Computer Interfaces.

Visual stimuli design plays an important role in brain-computer interfaces (BCIs) based on visual evoked potentials (VEPs). Variations in stimulus parameters have been shown to affect both decoding accuracy and subjective perception experience, implying the need for a trade-off in design. In this study, we comprehensively and systematically compared various combinations of amplitude contrast and spectral content parameters in the stimulus design to quantify their impact on decoding performance and subject comfort. Specifically, three parameters were investigated: 1) contrast level, 2) temporal pattern (periodic steady-state or pseudo-random code-modulated), and 3) frequency range. We collected electroencephalogram (EEG) data and subjective perception ratings from ten subjects and evaluated the decoding accuracy and subject comfort rating for different combinations of the stimulus parameters. Our results indicate that while high-frequency steady-state VEP (SSVEP) stimuli were rated the most comfortable, they also had the lowest decoding accuracy. Conversely, low-frequency SSVEP stimuli were rated the least comfortable but had the highest decoding accuracy. Standard and high-frequency M-sequence code-modulated VEPs (c-VEPs) produced intermediates between the two. We observed a consistent trade-off relationship between decoding accuracy and subjective comfort level across all parameters. Based on our findings, we offer c-VEP as a preferable stimulus for achieving reliable decoding accuracy while maintaining a reasonable level of comfortability.

Understanding the Influence of Receptive Field and Network Complexity in Neural Network-Guided TEM Image Analysis.

Trained neural networks are promising tools to analyze the ever-increasing amount of scientific image data, but it is unclear how to best customize these networks for the unique features in transmission electron micrographs. Here, we systematically examine how neural network architecture choices affect how neural networks segment, or pixel-wise separate, crystalline nanoparticles from amorphous background in transmission electron microscopy (TEM) images. We focus on decoupling the influence of receptive field, or the area of the input image that contributes to the output decision, from network complexity, which dictates the number of trainable parameters. For low-resolution TEM images which rely on amplitude contrast to distinguish nanoparticles from background, we find that the receptive field does not significantly influence segmentation performance. On the other hand, for high-resolution TEM images which rely on both amplitude and phase-contrast changes to identify nanoparticles, receptive field is an important parameter for increased performance, especially in images with minimal amplitude contrast. Rather than depending on atom or nanoparticle size, the ideal receptive field seems to be inversely correlated to the degree of nanoparticle contrast in the image. Our results provide insight and guidance as to how to adapt neural networks for applications with TEM datasets.

Hierarchical domain structures associated with oxygen octahedra tilting patterns in lead-free (Bi1/2Na1/2)TiO3

Hierarchical domain structures associated with oxygen octahedra tilting patterns were observed in lead-free (Bi1/2Na1/2)TiO3 ceramics using aberration-corrected high-resolution transmission electron microscopy (HRTEM). Three types of domains are induced by distinct mechanisms: the ‘orientation-domain’ is induced at micrometer scale formed by different tilting orientations of the oxygen octahedra, the ‘meso-chemical-domain’ occurs at a few tens of nanometer scale by chemical composition variation on the A-site in the ABO3 perovskite structure, and the ‘nano-cluster-region’ runs across several unit-cells with apparent A-site cation segregation with oxygen vacancies clustering around Na cations. Based on HRTEM amplitude contrast imaging (ACI), the correlation between the oxygen octahedral tilting pattern and compositional non-stoichiometry was established. The role of the hierarchical domain structure associated with the tilting patterns of the oxygen octahedra on the ferroelectric behavior of (Bi1/2Na1/2)TiO3 is also discussed.

Π-Net: A double-branch network framework for scanning coherent diffraction imaging

Ptychography is a coherent diffractive imaging technique that surpasses the resolution limitations of typical X-ray optics. It generates label-free, high-contrast quantitative amplitude and phase images without requiring prior knowledge. How to quickly recover high-fidelity amplitude and phase contrast images of an authentic sample is a challenge, however, due to the expensive computation that precludes real-time imaging. This paper proposes a framework with a double-branch network, Π-Net, to solve this complex problem. It can simultaneously and robustly reconstruct the object's phase and amplitude information from a degraded image. By analyzing the performance of various activation functions, we find that the activation function plays a crucial role in phase retrieval. Numerical experiments demonstrate the robustness of Π-Net, and our reconstruction results are superior to those of state-of-the-art methods. In addition, ablation studies have revealed that the negative output of neurons can contribute to improved reconstruction outcomes.

Polarization-independent tunable terahertz slow light with electromagnetically induced transparency metasurface

Tunable slow light systems have gained much interests recently due to their efficient control of strong light–matter interactions as well as their huge potential for realizing tunable device applications. Here, a dynamically tunable polarization independent slow light system is experimentally demonstrated via electromagnetically induced transparency (EIT) in a terahertz (THz) metasurface constituted by plus and dimer-shaped resonators. Optical pump-power dependent THz transmissions through the metasurface samples are studied using the optical pump THz probe technique. Under various photoexcitations, the EIT spectra undergo significant modulations in terms of its resonance line shapes (amplitude and intensity contrast) leading to dynamic tailoring of the slow light characteristics. Group delay and delay bandwidth product values are modulated from 0.915 ps to 0.42 ps and 0.059 to 0.025 as the pump fluence increases from 0 to 62.5 nJ cm−2. This results in tunable slow THz light with group velocities ranging from 2.18 × 105 m s−1 to 4.76 × 105 m s−1, almost 54% change in group velocity. The observed tuning is attributed to the photo-induced modifications of the optoelectronic properties of the substrate layer. The demonstrated slow light scheme can provide opportunities for realizing dynamically tunable slow light devices, delay lines, and other ultrafast devices for THz domain.

Phase contrast imaging with inelastically scattered electrons from any layer of a thick specimen

A controversy exists as to whether the signal in a high resolution phase contrast electron micrograph of a particle in a thick specimen is the same irrespective of the particle’s position along the beam axis. Different conceptions of inelastic scattering and its effects on wave interference have led to radically different expectations about the degree of phase contrast vs. depth. Here we examine the information available from bright field phase contrast images of small crystalline particles on the top or bottom of a thick support. The support is an aluminium foil which has strong plasmon resonances that cause a large proportion of the electron beam to lose energy in transit. Phase contrast micrographs of the atomic lattice of two ensembles of platinum particles were measured in an energy loss window corresponding to the first plasmon resonance. The signal measured for particles on top was equal to that for particles on the bottom of the foil to within a 99% confidence interval, and the measurements exclude other models of depth dependent phase contrast in the literature to >5σ. These observations are consistent with quantum theory which considers dynamical effects as independent of event sequence and is distinct from the “top-bottom effect” observed in amplitude contrast. We thus confirm that phase contrast using inelastically scattered electrons can be obtained equally well from particles within any layer of a thick specimen.

Direct estimation and correction of residual aberrations in the reconstructed exit-wavefunction of a crystalline specimen

Measuring residual aberrations up to second order using a crystalline specimen in transmission electron microscope is challenging. Here, we show by good examples of both experimental and simulated images that it is feasible to measure aberrations up to the second-order, using minimum amplitude contrast criterion for the exit wave function reconstructed. We propose a two-steps strategy for the task: (i) Firstly measuring defocus and two-fold astigmatism simultaneously to avoid error accumulation and to reduce the number of dimensions in parameters space. (ii) Then, with minimized misleading effects (or errors) in defocus and two-fold astigmatism, estimations of three-fold astigmatism and coma can be conducted more efficiently and effectively. Influences of other factors such as specimen structure, resolution and specimen thickness on the validity of the method are also discussed in detail. Our study provides a practical procedure for correcting residual aberrations in image wave using crystalline materials, which can then facilitate application of exit wave reconstruction technique to materials research.

Zastosowanie metod uczenia maszynowego do charakterystyki porowatości i typu nasycenia przy użyciu atrybutów sejsmicznych

The application of machine learning (ML) tools and data-driven modeling became a standard approach for solving many problems in exploration geology and contributed to the discovery of new reservoirs. This study explores an application of machine learning ensemble methods – random forest (RF) and extreme gradient boosting (XGBoost) to derive porosity and saturation type (gas/water) in multi-horizon sandstone formations from Miocene deposits of the Carpathian Foredeep. The training of ML algorithms was divided into two stages. First, the RF algorithm was used to compute porosity based on seismic attributes and well location coordinates. The obtained results were used as an extra feature to saturation type modeling using the XGBoost algorithm. The XGBoost was run with and without well location coordinates to evaluate the influence of the spatial information for the modeling performance. The hyperparameters for each model were tuned using the Bayesian optimization algorithm. To check the training models' robustness, 10-fold cross-validation was performed. The results were evaluated using standard metrics, for regression and classification, on training and testing sets. The residual mean standard error (RMSE) for porosity prediction with RF for training and testing was close to 0.053, providing no evidence of overfitting. Feature importance analysis revealed that the most influential variables for porosity prediction were spatial coordinates and seismic attributes sweetness. The results of XGBoost modeling (variant 1) demonstrated that the algorithm could accurately predict saturation type despite the class imbalance issue. The sensitivity for XGBoost on training and testing data was high and equaled 0.862 and 0.920, respectively. The XGBoost model relied on computed porosity and spatial coordinates. The obtained sensitivity results for both training and testing sets dropped significantly by about 10% when well location coordinates were removed (variant 2). In this case, the three most influential features were computed porosity, seismic amplitude contrast, and iso-frequency component (15 Hz) attribute. The obtained results were imported to Petrel software to present the spatial distribution of porosity and saturation type. The latter parameter was given with probability distribution, which allows for identifying potential target zones enriched in gas.

Ptychography at the carbon K-edge

Ptychography is a coherent diffraction imaging technique that measures diffraction patterns at many overlapping points on a sample and then uses an algorithm to reconstruct amplitude and phase images of the object and probe. Here, we report imaging, spectroscopy and linear dichroism ptychographic measurements at the carbon K-edge. This progress was achieved with a new generation of scientific Complementary Metal Oxide Semiconductor (sCMOS) X-ray cameras with an uncoated image sensor which has fast image transfer and high quantum efficiency at the carbon K-edge. Reconstructed amplitude and phase contrast images, C 1s spectral stacks, and X-ray linear dichroism of carbon nanotubes at the carbon K-edge were measured with ptychography. Ptychography and conventional Scanning Transmission X-ray Microscopy (STXM) are compared using results acquired from the same area. Relative to STXM, ptychography provides both improved spatial resolution and improved image quality. We used defocus ptychography, with an X-ray beam spot size of 1.0 micron, in order to reduce radiation damage and carbon deposition. Comparable spatial resolution was achieved to that of ptychography performed with a focused beam. Ptychography at the carbon K-edge offers unique opportunities to perform high resolution spectromicroscopy on organic materials important in medicine, biology, environmental science and energy materials.