Quantitative Depth Research Articles

Defect characterisation using pulse compression‐based quadratic frequency modulated thermal wave imaging

Quantitative depth estimation, along with enhanced defect detectability, is of utmost importance for subsurface analysis in thermal wave imaging for a variety of applications. However, the size and the depth of the subsurface anomalies influence this quantitative analysis due to the non-consideration of back reflection from the defect boundary in addition to three-dimensional scattering effects. This study explores an experimental validation of an analytical model for quantitative depth analysis of subsurface anomalies in thermal wave detection and ranging using quadratic frequency-modulated stimulation with pulse compression based signal processing approach and presents the depth resolution feature by considering the back reflection at the defect boundary. It also presents a study on the influence of the size of the anomaly and bandwidth of the stimulation on quantitative depth prediction using the experimentation carried over a carbon fibre reinforced plastic and mild steel specimen with artificial flat-bottom holes.

Individualized Quality Data Feedback Improves Anesthesiology Residents' Documentation of Depth of Neuromuscular Blockade Before Extubation.

Reversal of neuromuscular blockade is an important anesthesia quality measure, and anesthesiologists should strive to improve both documentation and practice of this measure. We hypothesized that the use of an electronic quality database to give individualized resident anesthesiologist feedback would increase the percentage of cases that residents successfully documented quantitative depth of neuromuscular blockade before extubation. The mean baseline success rate among anesthesiology residents was 80% (95% confidence interval [CI], 78-81) and increased by 14% (95% CI, 11-17; P < .001) after the residents were given their individualized quality data. Practice patterns improved quickly but were not sustained over 6 months.

Compensated Microsphere-Assisted Interference Microscopy

We propose an experimental method in microsphere-assisted interference microscopy that makes it possible to reconstruct surface topographies as much with high quantitative depth accuracy as when the transversal feature sizes are smaller than the classical diffraction limit. The full-field super-resolution interference microscope consists not only of one glass microsphere in the object arm but also of a second similar microsphere in the reference arm. By compensating the sphere aberrations in the two interference arms, the spatial resolution appears to be considerably increased. The increase in the three-dimensional spatial resolution allows for the topography reconstruction of 300-nm-width grating lines and 200-nm-diameter transparent nanopillars.

High resolution, programmable aperture light field laparoscope for quantitative depth mapping.

Recent applications have shown that light field imaging can be useful for developing uniaxial three-dimensional (3D) endoscopes. The immediate challenges in implementation are a tradeoff in lateral resolution and acquiring enough depth information in the physically limited environment of minimally invasive surgery. Here we propose using programmable aperture light field imaging in laparoscopy to capture 3D information without sacrificing the camera sensor's native, high spatial resolution. This hybrid design utilizes a programmable aperture to preserve the conventional laparoscope's functionality and, upon demand, to compute a depth map for surgical guidance. A working prototype is demonstrated.

Reconstituting past flood events: the contribution of citizen science

Abstract. Information gathered on past flood events is essential for understanding and assessing flood hazards. In this study, we present how citizen science can help to retrieve this information, particularly in areas with scarce or no authoritative measurements of past events. The case study is located in Yeumbeul North (YN), Senegal, where flood impacts represent a growing concern for the local community. This area lacks authoritative records on flood extent and water depth as well as information on the chain of causative factors. We developed a framework using two techniques to retrieve information on past flood events by involving two groups of citizens who were present during the floods. The first technique targeted the part of the citizens' memory that records information on events, recalled through narratives, whereas the second technique focused on scaling past flood event intensities using different parts of the witnesses' bodies. These techniques were used for three events that occurred in 2005, 2009 and 2012. They proved complementary by providing quantitative information on flood extents and water depths and by revealing factors that may have contributed to all three flood events.

Characterization of Corrosion Behavior of CLF-1 in Liquid Lithium Using Calibration-Free Laser-Induced Breakdown Spectroscopy in Depth Profile Analysis.

It is important to get fast and quantitative compositional depth profiles for the boundary layer of the corroded specimen in order to understand the corrosion process and mechanism due to liquid lithium induced corrosion problems to structural material of fusion reactors. In this work, calibration-free laser-induced breakdown spectroscopy (CF-LIBS) is introduced to investigate the compatibility of CLF-1(China low-activation Ferritic steel) exposed in liquid lithium at 500 °C for 500 h. The results show that CF-LIBS constitutes an effective technique to observe the corrosion layer of specimens which are non-uniform and the elements of matrix show gradient distribution from the boundary to the inner layer. The concentration was 82–95 wt.% Fe, 5–12 wt.% Cr, 0.45–0.85 wt.% Mn, 1.6–1.1 wt.% W, 0.11–0.16 wt.% V, and <0.2 wt.% Li along the longitudinal corrosion depth for the corrode CLF-1. The results reveal the quantitative elemental variation trend of CLF-1 in the lithium corrosion process and indicate that the CF-LIBS approach can be applied to the analysis of composition in multi-element materials.

Digital Interactive Quantitative Curiosity Assessment Tool: Questions Worlds

Curiosity is a 21st century skill and is of paramount importance in the digital age. However, the assessment of curiosity is often based on self-report or subjective observations. We present the development and evaluation of a digital quantitative assessment game for question-asking-based exploration. The student navigates a graphically presented question graph by selecting questions about a series of virtual alien worlds. The game extracts question-related quantitative measures, e.g., the breadth, depth and specificity of the answers to the questions. We conducted a study with Youth University students and administered a curiosity-based questionnaire to their class teachers as an external validation. Our results show that the measure of total question specificity in the last presented world is a significant predictor of children’s curiosity, as rated by their teachers. This suggests that curiosity can be quantitatively assessed by an entertaining digital question-based game.

Perspectives of Quantitative Depth Profiling

This article primarily deals with the compensation of “matrix effect” in secondary ion mass spectrometry (SIMS) for direct quantitative analysis of materials using MCs+-SIMS approach. Emphasis has been given on exploring the formation mechanisms of MCs+n (n = 1, 2,..) molecular ions (M denotes the element to be analyzed and Cs+ is the bombarding ion) emitted in the SIMS process. Following a brief introduction on SIMS, a study on MCs+n molecular ions emitted from various metal and semiconductor targets under Cs+ primary bombardment has been discussed.

The development of Air Quality Indices using AOD-Retrieved Images during haze events in Peninsular Malaysia

Advances in satellite sensors provide new datasets to assist in the observation of air quality at urban and local scales. Quantitative Aerosol Optical Depth (AOD) and qualitative true colour image data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the Terra satellite have been widely used to assess air quality. In this study, both 3 km and 10 km MODIS aerosol products were used to provide an overview of the state of fine particulate matter (PM2.5) and coarse particulate matter (PM10) during haze episodes over Peninsular Malaysia covering the period 21 to 26 June 2013. The result showed a high concentration of PM during the haze period based on ground-level data, with an average level of PM2.5 (mean ± standard deviation) of 99.86 ± 23.99, 190.37 ± 105.59, and 205.191 ± 165.76 (µg/m3) in Tanah Merah station, Cheras station, and Putrajaya station, respectively. Meanwhile, the PM10 in the Tanah Merah, Charas, and Putrajaya stations was 100.56 ± 28.17, 181.31 ± 83.70, and 197.99 ± 74.67, respectively. Overall, the results showed a correlation between monitoring station measurements and satellite data. Satellite remote sensing analysis of atmospheric aerosols remain a great method for estimating haze distribution and can be further used to monitor the atmospheric environment in Malaysia.

Universal Function Approximation by Deep Neural Nets with Bounded Width and ReLU Activations

This article concerns the expressive power of depth in neural nets with ReLU activations and a bounded width. We are particularly interested in the following questions: What is the minimal width w min ( d ) so that ReLU nets of width w min ( d ) (and arbitrary depth) can approximate any continuous function on the unit cube [ 0 , 1 ] d arbitrarily well? For ReLU nets near this minimal width, what can one say about the depth necessary to approximate a given function? We obtain an essentially complete answer to these questions for convex functions. Our approach is based on the observation that, due to the convexity of the ReLU activation, ReLU nets are particularly well suited to represent convex functions. In particular, we prove that ReLU nets with width d + 1 can approximate any continuous convex function of d variables arbitrarily well. These results then give quantitative depth estimates for the rate of approximation of any continuous scalar function on the d-dimensional cube [ 0 , 1 ] d by ReLU nets with width d + 3 .

Studying the Impact on Urban Health over the Greater Delta Region in Egypt Due to Aerosol Variability Using Optical Characteristics from Satellite Observations and Ground-Based AERONET Measurements

This research addresses the aerosol characteristics and variability over Cairo and the Greater Delta region over the last 20 years using an integrative multi-sensor approach of remotely sensed and PM10 ground data. The accuracy of these satellite aerosol products is also evaluated and compared through cross-validation against ground observations from the AErosol RObotic NETwork (AERONET) project measured at local stations. The results show the validity of using Multi-angle Imaging Spectroradiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua platforms for quantitative aerosol optical depth (AOD) assessment as compared to Ozone Monitoring Instrument (OMI), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), and POLarization and Directionality of the Earth’s Reflectances (POLDER). In addition, extracted MISR-based aerosol products have been proven to be quite effective in investigating the characteristics of mixed aerosols. Daily AERONET AOD observations were collected and classified using K-means unsupervised machine learning algorithms, showing five typical patterns of aerosols in the region under investigation. Four seasonal aerosol emerging episodes are identified and analyzed using multiple indicators, including aerosol optical depth (AOD), size distribution, single scattering albedo (SSA), and Ångström exponent (AE). The movements and detailed aerosol composition of the aforementioned episodes are demonstrated using NASA’s Goddard Space Flight Center (GSFC) back trajectories model in collaboration with aerosol subtype products from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission. These episodes indicate that during the spring, fall, and summer, most of the severe aerosol events are caused by dust or mixed related scenarios, whereas during winter, aerosols of finer size lead to severe heavy conditions. It also demonstrates the impacts of different aerosol sources on urban human health, which are presented by the variations of multiple parameters, including solar radiation, air temperature, humidity, and UV exposure. Scarce ground PM10 data were collected and compared against satellite products, yet owed to their discrete nature of availability, our approach made use of the Random Decision Forest (RDF) model to convert satellite-based AOD and other meteorological parameters to predict PM10. The RDF model with inputs from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) and Global Land Data Assimilation System (GLDAS) datasets improves the performance of using AOD products to estimate PM10 values. The connection between climate variability and aerosol intensity, as well as their impact on health-related PM2.5 over Egypt is also demonstrated.

Quantitative depth evaluation of microgrooves on polymer material beyond the diffraction limit

The demand for dimensional micro and nano metrology is evident, and with the miniaturization process of microstructures and the trend towards using polymer material, the available technologies are imperative. This paper proposes a quantitative depth evaluation method for microgrooves with width less than the diffraction limit and a measurement system capable of depth detection on polymer materials. The depth of microgrooves can be quantitatively related to the near-field optical phase difference, which cannot be practically observed but can be computed from practical far-field observations by proposed depth evaluation method. The developed measurement system uses low-coherence illumination to reduce the spatial speckle noise and multiple interference noise on transparent polymer materials. Microgrooves (nominal width = 300 nm, Rayleigh Criterion of measurement system = 772 nm) on a transparent polymer surface (thermoplastic COC 5013L molded sample) were measured, and the results were compared with AFM measurements. It is demonstrated that the proposed method is instrumental in evaluating the depth of microgrooves beyond the diffraction limit with an accuracy of less than 10% of the test depth.

Unsupervised binocular depth prediction network for laparoscopic surgery.

Minimally invasive surgery (MIS) is characterized by less trauma, shorter recovery time, and lower postoperative infection rate. The two-dimensional (2D) laparoscopic imaging lacks depth perception and does not provide quantitative depth information, thereby limiting precise and complex surgical operations. Three-dimensional (3D) laparoscopic imaging provides surgeons depth perception. This study aims to 3D reconstruction of the surgical scene based on the disparity map generated by the depth estimation algorithm. An unsupervised learning autoencoder method was proposed to calculate the accurate disparity with a 101-layer residual convolutional network. The loss function included three parts: left-right consistency loss, structure similarity loss, and reconstruction error loss, the combination can improve reconstruction accuracy and robustness. The method was validated on a Hamlyn Center Laparoscopic/Endoscopic Video Dataset. The structural similarity index (SSIM) is 0.8349 ± 0.0523 and the peak signal-to-noise ratio (PSNR) is 14.4957 ± 1.9676. The depth prediction network has high accuracy and robustness. The average time to produce each disparity map is about 16 ms. The experimental result shows that the proposed depth estimation method can offer dense disparity map, and can meet surgical real-time requirement. Future work will focus on network structure optimization and loss function design, transfer learning to improve the robustness and accuracy further.

Application of oxidized metallic surfaces as a medium to store biochemical agents with antimicrobial properties

Creating air-stable antimicrobial metallic surfaces that are resistant to washing could impact both food handling and medical applications. This work analyzes the release kinetics of nisin nanoparticles (an antimicrobial peptide) from cracked and uncracked oxide films on stainless steel and titanium to develop an antibacterial metallic surface. The cracked morphology creates mudflat crack patterns that penetrate approximately 4 μm into the substrate, deeper than the 100 nm thick oxides. The desorption of the peptide from uncracked oxides on stainless steel is promoted by acidic pH, slows down at neutral, and stops at basic pH. Increases in temperature contribute to significant accelerated desorption process only at acidic pH. Cracked oxide films effectively immobilized nisin in the cracks. For Ti-6Al-4 V, even under the harshest conditions tested (50 °C and pH 2) the peptide did not release into solution after 24 h. Quantitative depth profiling detected nitrogen to depths of up to 2.8 μm, indicating the presence of nisin in the cracks. A simple test of the antibacterial performance of these surfaces against Listeria monocytogenes has been demonstrated.

Quantitative Aerosol Optical Depth Detection during Dust Outbreaks from Meteosat Imagery Using an Artificial Neural Network Model

This study presents the development of an artificial neural network (ANN) model to quantitatively estimate the atmospheric aerosol load (in terms of aerosol optical depth, AOD), with an emphasis on dust, over the Mediterranean basin using images from Meteosat satellites as initial information. More specifically, a back-propagation ANN model scheme was developed to estimate visible (at 550 nm) aerosol optical depth (AOD550 nm) values at equal temporal (15 min) and spatial (4 km) resolutions with Meteosat imagery. Accuracy of the ANN model was thoroughly tested by comparing model estimations with ground-based AOD550 nm measurements from 14 AERONET (Aerosol Robotic NETwork) stations over the Mediterranean for 34 selected days in which significant dust loads were recorded over the Mediterranean basin. Using a testbed of 3076 pairs of modeled and measured AOD550 nm values, a Pearson correlation coefficient (rP) equal to 0.91 and a mean absolute error (MAE) of 0.031 were found, proving the satisfactory accuracy of the developed model for estimating AOD550 nm values.

Characterization of arsenic plasma doping and postimplant processing of silicon using medium energy ion scattering

Plasma doping (PLAD) is increasingly applied in microelectronic device manufacture to produce high throughput, high fluence implants. In this medium energy ion scattering (MEIS) study of the PLAD process, Si(100) wafers were exposed to an As containing plasma while pulse biased negatively to 7 kV to cause (recoil) implantation and deposition of As. Quantitative MEIS depth profiling analysis in conjunction with energy spectrum simulation was applied to characterize the near-surface layer changes of the Si wafer following (i) the PLAD process, (ii) two types of chemical wet clean (oxidizing and nonoxidizing), and (iii) spike annealing in an N2 atmosphere. MEIS analysis showed that the PLAD process produced an intermixed As/Si layer, with a near-surface As content of ∼40% that decayed almost linearly to near-zero at a depth of ∼17 nm. This mixed As/Si layer was unstable in air and the initially recorded 1.2 nm thick oxide cap layer grew over a period of four months to 3.5 nm with a concurrent 25% As loss by sublimation. The application of the industry standard, oxidizing wet chemical clean removed the top As and concurrently produced a ∼14 nm thick Si oxide above the remaining implanted As profile, which matched the tail of original As implant profile. As depth profiles measured for the 7 kV PLAD process after a wet clean and spike annealing showed solid phase epitaxial regrowth of the disordered layer. A detailed comparison of the random and aligned MEIS spectra yielded depth profiles of substitutional As with a concentration in excess of 1 × 1021 As cm−3 over a depth greater than 10 nm. The retained dose of 1.35 × 1015 cm−2 represents a ∼70% increase in substitutional As compared to that recorded after a nonoxidizing clean. Such an alternative wet chemical clean, in which Si reoxidation did not occur, was applied to determine the depth of the mixed As/Si layer removed. Found to be 7 nm, the analysis indicated that the etching process ceased when the Si concentration reached 4 × 1022 cm−3. After spike annealing, part of the remaining As had segregated in a thin layer under a 1.6 nm thick surface oxide. The retained As dose in this case was 8 × 1014 cm−2, equivalent to a 1% As substitutional dopant concentration to a depth of ∼14 nm. Different substitutional As doses measured with MEIS were found to correlate closely with sheet resistance measurements, confirming that equating the substitutional As with the active As dose remains correct for these ultrashallow profiles, typically 10 nm deep.

Measurement of thermal diffusivity of fiber reinforced polymers using quadratic frequency modulated thermal wave imaging

Application specific manufacturing of materials necessitates precise estimation of their thermo physical properties for a variety of applications. Recent quantitative evaluation in non stationary thermal wave imaging facilitates the assessment of these properties despite the available complicated procedures. This manuscript proposes a novel enhanced frequency resolution based quadratic frequency modulated thermal wave imaging methodology to simultaneously estimate the thermal diffusivity and to detect subsurface integrity of fiber reinforced polymer along with its quantitative thermal modeling. The proposed modality is validated using an experimentation carried over fiber reinforced polymer specimens with artificial flat bottom holes. It also showcases the salient feature of the proposed methodology to provide enhanced depth resolution than any other contemporary post processing approaches along with a precise quantitative depth analysis.

Round‐robin test of medium‐energy ion scattering for quantitative depth profiling of ultrathin HfO2/SiO2/Si films

Medium‐energy ion scattering (MEIS) has been used for quantitative depth profiling with single atomic layer resolution to determine the composition, thickness, and interface structure of ultrathin films and nanoparticles. To assure the consistency of the MEIS analysis, an international round‐robin test (RRT) with nominally 1‐, 3‐, 5‐, and 7‐nm thick HfO2 films was conducted among 12 institutions. The measurements were performed at each participating laboratory under their own conditions, and the collected data were analyzed. For the data analysis, the Moliere potential, the stopping and range of ions in matter (SRIM) 95 and new fitted electronic stopping power and the Chu straggling were used. For analyzing the MEIS data from the magnetic sector and electrostatic analyzers, the neutralization corrections of Marion and Young for 100‐keV H+ and He+ ions and of Armstrong for 400‐ to 500‐keV He+ ions were used. The standard deviations were 5.3% for the composition, 15.3% for the thickness, and 13.3% for the Hf content, and they were improved to 7.3%, 4.5%, and 7.0% by using refitted electronic stopping powers based on the experimental data. Hence, this study suggests that correct electronic stopping powers are critical for quantitative MEIS analysis.

Three-Dimensional Nanoscale Mapping of Porosity in Solution-Processed ITO Multilayer Thin Films for Patternable Transparent Electrodes

Indium tin oxide (ITO) films constitute components of many layered heterostructures used for emergent technologies beyond conventional optoelectronics. Compositional and morphological changes have a direct impact on the device’s performance. Hence control over the morphology with advanced multimodal characterization approaches is required to evaluate the devices. Herein multilayer ITO films deposited by spin-coating were quantified in nanoscale detail in three dimensions by combining results from depth-sensitive neutron reflectometry (NR), noncontact topographic AFM images, and cross-sectional SEM images. Films with a different number of deposited layers were visually transparent even though the topmost layer was as high as 60% porous, with porosity gradually decreasing as the number of the underneath sublayers increased. Surface and interfacial roughness through the total film and individual layer thickness were obtained. NR data also furnished quantitative depth information on the films’ chemical composition and layer-by-layer bulk density, which has never been obtained before, providing a way to monitor and ultimately control the sheet resistivity via the pore network. When the same formulation is used for inkjet printing patterns, the larger pores disappear, and the optical properties are improved to >90% transmittance at all visible wavelengths. All 5L films achieved sheet resistivities as low as 10–2 Ω cm and can therefore be used as patternable transparent electrodes for many devices including liquid crystal displays.

Unsupervised binocular depth prediction network for laparoscopic surgery

Minimally invasive laparoscopic surgery is associated with small wounds and short recovery time, reducing postoperative infections. Traditional two-dimensional (2D) laparoscopic imaging lacks depth perception and does not provide quantitative depth information, thereby limiting the field of vision and operation during surgery. However, three-dimensional (3D) laparoscopic imaging from 2 D images lets surgeons have a depth perception. However, the depth information is not quantitative and cannot be used for robotic surgery. Therefore, this study aimed to reconstruct the accurate depth map for binocular 3 D laparoscopy. In this study, an unsupervised learning method was proposed to calculate the accurate depth while the ground-truth depth was not available. Experimental results proved that the method not only generated accurate depth maps but also provided real-time computation, and it could be used in minimally invasive robotic surgery.