Acquisition Conditions Research Articles

Enhancing spectral imaging with multi-condition image fusion

Spectral Imaging techniques such as Laser-induced Breakdown Spectroscopy (LIBS) and Raman Spectroscopy (RS) enable the localized acquisition of spectral data, providing insights into the presence, quantity, and spatial distribution of chemical elements or molecules within a sample. This significantly expands the accessible information compared to conventional imaging approaches such as machine vision. However, despite its potential, spectral imaging also faces specific challenges depending on the limitations of the spectroscopy technique used, such as signal saturation, matrix interferences, fluorescence, or background emission. To address these challenges, this work explores the potential of using techniques from conventional RGB imaging to enhance the dynamic range of spectral imaging. Drawing inspiration from multi-exposure fusion techniques, we propose an algorithm that calculates a global weight map using exposure and contrast metrics. This map is then used to merge datasets acquired with the same technique under distinct acquisition conditions. With case studies focused on LIBS and Raman Imaging, we demonstrate the potential of our approach to enhance the quality of spectral data, mitigating the impact of the aforementioned limitations. Results show a consistent improvement in overall contrast and peak signal-to-noise ratios of the merged images compared to single-condition images. Additionally, from the application perspective, we also discuss the impact of our approach on sample classification problems. The results indicate that LIBS-based classification of Li-bearing minerals (with Raman serving as the ground truth), is significantly improved when using merged images, reinforcing the advantages of the proposed solution for practical applications.

Impact of image formation factors on material discrimination in spectral CT.

The accuracy of material decomposition in spectral CT depends on the information quality captured in image acquisition, a factor that cannot be adequately assessed using conventional image quality metrologies due to the multi-energy nature of spectral CT. This work used metrologies specific to spectral CT to evaluate the impact of acquisition conditions on the quality of spectral CT images and accuracy of material decomposition techniques.
Approach: Computational phantoms were created with cylindrical shapes and variable sizes (20-40 cm), containing inserts of iodine and gadolinium (1-8 mg/mL). The phantoms were imaged using a validated CT simulator modeling a clinical photon-counting CT scanner. The acquisitions were done at different detector energy thresholds (50-90 keV) and tube currents (25-250 mAs). The images were used to develop and train a data-driven material identification and quantification algorithm. Two spectral metrologies, multivariate contrast-to-noise ratio (CNR) and separability index, were used to characterize the impact of energy threshold, tube current, phantom size, and material concentration on signal quality. The results were interpreted in terms of figures of merit of accuracy for classification and mean absolute error (MAE) and root mean squared error (RMSE) for regression. 
Main results: Signal quality for iodine and gadolinium was maximized with a low energy threshold, high tube current, and small phantom size. While conventional CNR terms predicted variable image quality for two-thirds of all conditions, multivariate CNR was above 10 for half of those. Separability index showed that for a phantom size greater than 30 cm, a minimum of 75-110 mAs is required to separate 2 mg/mL of iodine and gadolinium. For both classification and regression tasks, a random forest model with a local statistics dataset provided the best performance. Across conditions, classification performance was 0.66-0.99 for I accuracy, 0.72-0.99 for Gd accuracy. Regression performance was 0.02-0.91 mg/mL I and 0.02 - 0.59 mg/mL Gd for MAE and 0.11-1.08 mg/mL I and 0.07-0.76 mg/mL Gd for RMSE.
Significance: Multivariate CNR and separability index metrologies can predict material decomposition performance. Theses metrics demonstrated that the decomposition of iodine and gadolinium have higher separability when the acquisition is done at a lower energy threshold, with a higher tube current, and when the imaged object has a smaller size. Object size had the largest impact on metrics and decomposition performance.&#xD.

Local Governance and School-based Approaches to English Language Acquisition: A Comparative Analysis

In order to improve the effectiveness of English acquisition, it is necessary to discuss it in conjunction with local governance policies. Local governments should conduct an analysis of English acquisition from the aspects of policies, systems, and social participation. This paper puts forward three hypotheses: local policies and systems can improve the effectiveness of English learning, which are related. Public attention can promote the improvement of English acquisition effect and play a supervisory role; Laws and regulations can ensure the foundation of basic conditions for English acquisition and promote the improvement of school infrastructure. The results show that local policies and institutions are the conditions for improving English acquisition methods, social attention is the condition for verification, and school infrastructure is the key condition. Social issues, local policies and institutions all play an indirect role in promoting English language acquisition, while infrastructure plays a direct role. Therefore, local governments should improve English acquisition policies and systems, promote the introduction of English acquisition laws and regulations, and guide the public to supervise.

Deep learning-based Covid-19 diagnosis: a thorough assessment with a focus on generalization capabilities

The Covid-19 pandemic has significantly spurred the development of deep learning (DL) models for the pathology automatic diagnosis based on CT scan images. However, the assumption about the generalization of the proposed models remains to be assessed and shown for concrete clinical use. In this work, we have investigated the real value of widely used public datasets for the elaboration of DL models that are dedicated to automatic diagnosis of Covid-19 using CT scans. We have collected various international public datasets from 13 countries. Different Convolutional Neural Networks (CNNs) have been trained and their performances carefully assessed. Two evaluations have been conducted: (1) an internal evaluation following a cross-validation procedure, and (2) an external evaluation on real patients coming from new and different sources. The objective is to assess the generalization capabilities considering real-world conditions: different acquisition conditions, devices and configurations. Three families from the most effective CNN models have been selected (ResNet, DenseNet and EfficientNet). These have been fine-tuned, evaluated and used within a training methodology based on transfer learning. The most effective models have been further customized in order to create new models that are dedicated to the task at hand. These models have significantly improved the diagnosis performance.

Tanggung Gugat Sengketa Hak Pertanahan Terhadap Pembeli yang Beritikad Baik

The purpose of this study is to determine the legal basis or legal considerations of the panel of judges in deciding Civil Case Number 29/Pdt.G/2023/PN.Mjk. To find out these problems, the author uses Normative Research methods, legislative approaches, scientific journals, court decisions, with Library data collection techniques, and descriptive and qualitative analysis.The result of the author's analysis is that he disagrees with the judge's consideration in deciding the case, with considerations including the existence of procedures or conditions for the legal acquisition of sale and purchase of land based on Legislation, as well as the existence of a Supreme Court Circular Letter governing Good Faith Buyers, and the absence of strict regulations governing legal protection for good faith buyers, causing multiple interpretations of existing rules and resulting in different court decisions even in the same type of case.

Hybrid Wavelet‐Deep Learning Framework for Fluorescence Microscopy Images Enhancement

ABSTRACTFluorescence microscopy is a powerful tool for visualizing cellular structures, but it faces challenges such as noise, low contrast, and autofluorescence that can hinder accurate image analysis. To address these limitations, we propose a novel hybrid image enhancement method that combines wavelet‐based denoising, linear contrast enhancement, and convolutional neural network‐based autofluorescence correction. Our automated method employs Haar wavelet transform for noise reduction and a series of adaptive linear transformations for pixel value adjustment, effectively enhancing image quality while preserving crucial details. Furthermore, we introduce a semantic segmentation approach using CNNs to identify and correct autofluorescence in cellular aggregates, enabling targeted mitigation of unwanted background signals. We validate our method using quantitative metrics, such as signal‐to‐noise ratio (SNR) and peak signal‐to‐noise ratio (PSNR), demonstrating superior performance compared to both mathematical and deep learning‐based techniques. Our method achieves an average SNR improvement of 8.5 dB and a PSNR increase of 4.2 dB compared with the original images, outperforming state‐of‐the‐art methods such as BM3D and CLAHE. Extensive testing on diverse datasets, including publicly available human‐derived cardiosphere and fluorescence microscopy images of bovine endothelial cells stained for mitochondria and actin filaments, showcases the flexibility and robustness of our approach across various acquisition conditions and artifacts. The proposed method significantly improves fluorescence microscopy image quality, facilitating more accurate and reliable analysis of cellular structures and processes, with potential applications in biomedical research and clinical diagnostics.

A Novel Fingerprint Recognition Framework with Attention Mechanism Based on Domain Adaptation for Improving Applicability in Overpressured Situations

Fingerprint recognition is a widely adopted biometric technology, valued for its reliability and precision in identifying individuals. However, traditional recognition methods relying on handcrafted features struggle under challenging scenarios such as overpressured fingerprints, where excessive pressure distorts ridge patterns, significantly affecting performance. To address these challenges, this study proposes a novel framework combining domain adaptation techniques and an attention mechanism. The framework aligns feature distributions between source and target domains, enhancing the model's generalizability to diverse datasets and acquisition conditions. Additionally, the attention mechanism emphasizes critical regions of the fingerprint, improving robustness to distortions. Experimental results demonstrate that the proposed model significantly outperforms the original ResNet, achieving a reduced Equal Error Rate (EER) of 0.0837 compared to 0.1840 for the baseline. Grad-CAM visualizations further validate the model's ability to focus on essential fingerprint features, even under distorted conditions. This study highlights the effectiveness of integrating domain adaptation and attention mechanisms in overcoming real-world challenges in fingerprint recognition.

Radon-domain acoustic and elastodynamic interferometric redatuming of VSP data

SUMMARY The virtual source method (VSM) is a useful tool for imaging and monitoring below complex and time-varying overburden. When it is applied to VSP geometries, the redatumed virtual source data often suffer from artifacts and aliasing due to the violation of theoretical acquisition conditions and partial focusing of virtual multiples, which further degrade seismic imaging quality. While the conventional Radon-domain VSM (R-VSM) mitigates these issues to some extent, it is still possible to improve upon the imaging. This study develops a simple and effective high-resolution Radon-domain VSM (HR-VSM) to effectively address these issues, offering superior noise and artifact suppression compared to traditional VSM and R-VSM. HR-VSM shows a strong performance across a wide range of VSP configurations and data types, especially in applications to multicomponent and passive seismic data, demonstrating its versatility and effectiveness in seismic exploration and showing potential for use in earthquake seismology. To extend subsurface illumination, we combined correlation-type and convolution-type HR-VSM for automatically redatuming virtual surface seismic profile shot gathers with a complete receiver array covering all surface source locations, without the additional pre-processing steps typically required by VSM. It was also tested on reverse VSP synthetic active data and passive seismic data, proving effective in constructing virtual shot gathers and showing potential for extending its use to earthquake data and ambient-noise seismology. Moreover, HR-VSM can perform elastodynamic interferometric redatuming, enabling the redatuming of pure P-P and pure P-SV waves from multicomponent VSP data. We applied HR-VSM to redatum virtual single well profile data containing fault-related P-P and P-SV reflections from VSP data, which were further used for fault imaging. Finally, HR-VSM was used to generate virtual crosswell data with receivers in both boreholes, eliminating the need for downhole sources. The obtained virtual crosswell containing direct waves, as well as P-P and P-SV reflections, enable constructing a velocity model and imaging the structure between wells.

An effective unsupervised domain adaptation for in-field potato disease recognition

Accurate disease recognition through computer vision is crucial for the intelligent management of potato production. Popular data-driven classification methods face challenges including limited labelled data and poor model portability. Unsupervised Domain Adaptation (UDA) addresses these challenges with a novel learning strategy. However, the complex field environment introduces a significant domain shift problem due to varying conditions. Existing UDA methods usually concentrate on aligning global data distribution and employ a single structure for disease feature extraction, thereby limiting their efficacy in true field environment. To tackle this challenge of potato disease recognition, the Multi-Representation Adaptive Network (MRSAN) based on subdomain alignment is presented. MRSAN effectively aligns feature distributions across diverse data by minimising distribution differences among relevant subdomains. Simultaneously, the multi-representation extraction method captures finer details from various perspectives in the disease images. The combination of these two approaches efficiently mitigates the adverse effects caused by various interference factors in field environment. Based on the acquisition conditions of light variation and disease progression, two field potato disease image datasets are created, containing five and six kinds of potato leaf disease, respectively. Extensive transfer experiments are conducted on the two datasets. MRSAN achieves average classification accuracies of 87.03% and 80.06% on the datasets for the corresponding transfer tasks, outperforming the other compared methods. This not only validates the effectiveness of MRSAN but also demonstrates its robust ability to generalise across changes in regard to light variation and disease progression.

Thyroid scintigraphy of healthy cats using small-field-of-view gamma cameras.

Small-field-of-view (SFOV) gamma cameras can offer higher sensitivities than conventional gamma cameras. However, there are currently no reports on the efficacy and safety of thyroid scintigraphy using SFOV gamma cameras in veterinary medicine. Therefore, we aimed to evaluate the efficacy and radiation safety of an SFOV gamma camera for feline thyroid scintigraphy. Three veterinary staff members (operator, staff 1, and staff 2) performed thyroid scintigraphy on 10 healthy cats in this study. The operator administered either 2 or 4 mCi of technetium-99m pertechnetate (99mTcO- 4) through the cephalic vein. At 20, 40, and 60 min after injection, thyroid images were obtained using a SFOV gamma camera under various acquisition conditions (100,000, 150,000, and 200,000 counts and 30 and 60 s). Thyroid scintigraphy images were analyzed by calculating the thyroid-to-salivary ratios (TSR) and thyroid-to-background ratios (TBR). Surface and ambient radiation were measured hourly from immediately after injection to 6 h. The cumulative occupational radiation doses were measured during the procedure. The TSR and TBR median values aligned with the previously reported normal range obtained using a large-field-of-view gamma camera. There were no notable differences in TSR and TBR between the two doses of 99mTcO- 4, nor across acquisition conditions and timelines. The 4-mCi group consistently emitted more ambient (p < 0.05) and surface (p < 0.05) radiation than did the 2-mCi group. Staff 1 consistently received higher cumulative radiation doses than did staff 2 and the operator (p < 0.05). The SFOV gamma camera demonstrated adequate image quality for thyroid scintigraphy in healthy cats even with relatively low doses and short acquisition conditions. Radiation exposure during the procedure posed minimal safety concerns. Therefore, the SFOV gamma camera could be a valuable tool for evaluating thyroid glands in cats.

Spectral Unmixing of Pigments on Surface of Painted Artefacts Considering Spectral Variability

Abstract. Painted artefacts, such as murals and paintings, are the treasures of human civilization. Pigment is an important component of their surfaces. It is crucial to study the composition and proportion of pigments on the surface of painted artefacts for the field of heritage conservation. In this study, hyperspectral remote sensing was used to invert the abundance of mixed pigments by spectral unmixing. Spectral variability effects are often present in hyperspectral images. Hyperspectral images of cultural artefacts also suffer from spectral variability due to factors such as particle size and impurities within the pigment, and acquisition conditions. Therefore, spectral variability was incorporated into the Linear Mixed Model of the pigment spectral unmixing. The unmixing methods are classified into two categories based on whether or not spectral libraries are used. In the experiment, computer synthesized data and laboratory-made sample blocks of mixed pigments, which mixed with different ratios of Azurite and Malachite, were selected as validation data. Five commonly used algorithms for solving the spectral variability problem, namely MESMA, Fractional Sparse SU, ELMM, RUSAL, and BCM, are used for pigment unmixing and compared with FCLS, which does not consider spectral variability. The results show that ELMM has the highest unmixing accuracy of aRMSE and xRMSE compared to other methods. At the same time, it can be seen from the metric of SAM that ELMM is able to extract reliable endmember variability spectral, which is more suitable for solving the problem of spectral variability in pigment unmixing. Finally, we apply ELMM to real artefacts Yungang Grottoes mural paintings, and obtain a lower xRMSE than FCLS, which improves the unmixing accuracy.

Optimising the acquisition conditions of high information quality low-field NMR signals based on a cutting-edge approach applying information theory and Taguchi's experimental designs – Virgin olive oil as an application example

BackgroundDeveloping a new spectrometric analytical method based on a fingerprinting approach requires optimisation of the experimental stage, particularly with novel instruments like benchtop low-field NMR spectrometers. To ensure high-quality LF-NMR spectra before developing the multivariate model, an experimental design to optimise instrument conditions is essential. However, difficult-to-control factors may be critical for optimisation. Taguchi methodology addresses these factors to obtain a system robust to variation. This study uses the Taguchi methodology to optimise instrument settings for acquiring high-quality 1H and 13C LF-NMR signals in a short time from virgin olive oil (VOO). ResultsTwo experimental trials (for 1H and 13C signals, respectively) were carried out and analysed to find an optimal and robust combination of instrument settings against changes in two difficult-to-control factors: ambient temperature and small deviations of the NMR tube volume (700 ± 50 μL). The responses to be optimised, run time and spectral information quality, were analysed separately and jointly, as some factors showed opposite behaviour in the effect on the responses. Multiple response analysis based on suitable desirability functions yielded a combination of factors resulting in desirability values above 0.8 for 1H LF-NMR signals and almost 1.0 for 13C LF-NMR signals.In addition, a novel approach to assess the information quality of an analytical signal was proposed, addressing a major challenge in analytical chemistry. By applying information theory and calculating information entropy, this approach demonstrated its potential for selecting the highest quality (i.e. most informative) analytical signals. SignificanceThe acquisition instrument conditions of LF-NMR were successfully optimised using Taguchi methodology to acquire highly informative 1H and 13C spectra in a minimum run time. The importance lies in the future development of non-targeted analytical applications for VOO quality control. In addition, the innovative use of information entropy to a priori assess the signal quality represents a significant advance and proposes a solution to a long-standing challenge in analytical chemistry.

Photo-capacitance measurement in dual-frequency mode and its application to study of Pt/κ-Ga2O3 planar Schottky diode

In this study, a dual-frequency method for the impedance/admittance measurements on illuminated junctions has been critically analyzed as an appropriate methodological approach for the measure of the depletion capacitance, both under steady-state light and during on-off illumination cycles. A planar Pt/κ-Ga2O3 Schottky diode was used as a test structure. Then capacitance-voltage (C-V) measurements were carried out under various applied AC frequencies and monochromatic UV-C light (λ = 254 nm) and compared to dark values. Transient photo-capacitance (TPC) measurements were performed at fixed applied voltages and the same AC frequencies and illumination conditions as the C-V measurements.Thanks to capacitance-frequency (C-f) measurements, the device under test was preliminary demonstrated to be well represented, both in dark and under light, by the equivalent circuit required for the applicability of the dual-frequency approach. It must include a depletion capacitance, parallel leakage resistance, and a series resistance. Dual-frequency capacitance resulted comparable to the single frequency C-V and TPC data obtained in the high-frequency limit for series configuration measurements and those obtained at low frequency in measurements carried out in parallel configuration.The experimental conditions of reliable data acquisition were discussed leading to the interpretation of the results in terms of the presence of slow-response traps.

The Power of Reagent Titration in Flow Cytometry.

Flow cytometry facilitates the detection of multiple cell parameters simultaneously with a high level of resolution and throughput, enabling in-depth immunological evaluations. High data resolution in flow cytometry depends on multiple factors, including the concentration of reagents used in the staining protocol, and reagent validation and titration should be the first step in any assay optimization. Titration is the process of finding the concentration of the reagent that best resolves a positive signal from the background, with the saturation of all binding sites, and minimal antibody excess. The titration process involves the evaluation of serial reagent dilutions in cells expressing the antigen target for the tested antibody. The concentration of antibody that provides the highest signal to noise ratio is calculated by plotting the percentage of positive cells and the intensity of the fluorescence of the stained cells with respect to the negative events, in a concentration-response curve. The determination of the optimal antibody concentration is necessary to ensure reliable and reproducible results and is required for each sample type, reagent clone and lot, as well as the methods used for cell collection, staining, and storage conditions. If the antibody dilution is too low, the signal will be too weak to be accurately determined, leading to suboptimal data resolution, high variability across measurements, and the underestimation of the frequency of cells expressing a specific marker. The use of excess antibodies could lead to non-specific binding, reagent misuse, and detector overloading with the signal off scale and higher spillover spreading. In this publication, we summarized the titration fundamentals and best practices, and evaluated the impact of using a different instrument, sample, staining, acquisition, and analysis conditions in the selection of the optimal titer and population resolution.

Pen-based vibrotactile feedback rendering of surface textures under unconstrained acquisition conditions

Haptic rendering of surface textures enhances user immersion of human–computer interaction. However, strict input conditions and measurement methods limit the diversity of rendering algorithms. In this regard, we propose a neural network-based approach for vibrotactile haptic rendering of surface textures under unconstrained acquisition conditions. The method first encodes the interactions based on human perception characteristics, and then utilizes an autoregressive-based model to learn a non-linear mapping between the encoded data and haptic features. The interactions consist of normal forces and sliding velocities, while the haptic features are time–frequency amplitude spectrograms by short-time Fourier transform of the accelerations corresponding to the interactions. Finally, a generative adversarial network is employed to convert the generated time–frequency amplitude spectrograms into the accelerations. The effectiveness of the proposed approach is confirmed through numerical calculations and subjective experiences. This approach enables the rendering of a wide range of vibrotactile data for surface textures under unconstrained acquisition conditions, achieving a high level of haptic realism.

Efficient microplastic identification by hyperspectral imaging: A comparative study of spatial resolutions, spectral ranges and classification models to define an optimal analytical protocol

Microplastics (MPs) pollution is a global and challenging issue, necessitating the development of efficient analytical strategies for their detection to monitor their environmental impact.This study aims to define an optimal analytical protocol for characterizing MPs by hyperspectral imaging (HSI), comparing different setups based on spatial resolution, spectral range and classification models. The investigated MPs include polymers commonly found in the environment, such as polystyrene (PS), polypropylene (PP) and high-density polyethylene (HDPE), subdivided in three size classes (1000–2000 μm, 500–1000 μm, 250–500 μm). Furthermore, MP particles with diameters ranging from 30 to 250 μm were assessed to determine the limit of detection (LOD) in the different configurations. Hyperspectral images were acquired with two spatial resolutions, 150 and 30 μm/pixel, and two spectral ranges, 1000–1700 nm (NIR) and 1000–2500 nm (SWIR). Three classification models, Partial Least Square-Discriminant Analysis (PLS-DA), Error Correction Output Coding-Support Vector Machine (ECOC-SVM) and Neural Network Pattern Recognition (NNPR) were tested on the acquired images. The correctness of these models was evaluated by prediction maps and statistical parameters (Recall, Specificity and Accuracy). The results demonstrated that for MP particles larger than 250 μm, the optimal setup is a spatial resolution of 150 μm/pixel and a spectral range of 1000–1700 nm, utilizing a linear classification model like PLS-DA. This approach offers accurate predictions while being time- and cost-efficient. For MPs smaller than 250 μm, a higher spatial resolution of 30 μm/pixel with a spectral range of 1000–2500 nm and a non-linear classification method like ECOC-SVM is preferable. The LOD is 250 μm for the 150 μm/pixel resolution and ranges from 100 to 200 μm for the 30 μm/pixel resolution. These findings provide a valuable guide for selecting the appropriate HSI acquisition conditions and data processing methods to optimally characterize MPs of different sizes.

Optimization of Conditions for Conidial Production in Bipolaris oryzae Isolated from Rice

Conidial production is a critical factor in testing pathogenicity and studying the physiology and ecology of fungal pathogens. Therefore, selecting an appropriate condition and medium for consistent conidia production is essential. In this study, we investigated light conditions and suitable medium conditions using the slide culture method to establish optimal conditions for continuous spore acquisition of &lt;i&gt;Bipolaris oryzae&lt;/i&gt;. Primarily, we observed conidial production using two &lt;i&gt;B. oryzae&lt;/i&gt; isolates, CM23-042 and 23CM10, under two different light conditions: (1) consistent near-ultraviolet (NUV) with fluorescent light, and (2) a 12-hr shift of the NUVdark cycle. Secondly, we examined conidial formation under seven different media on potato dextrose agar (PDA), V8-Juice agar, minimal medium (MM), sucrose-proline agar (SPA), rabbit food agar (RFA), rice bran agar (RBA), and rice leaf agar (RLA). Under consistent NUV light with fluorescent conditions, conidia were induced in both isolates, whereas conidia were not produced under other conditions after 7 days post-inoculation (dpi). Moreover, &lt;i&gt;B. oryzae&lt;/i&gt; isolate CM23-042 produced the highest number of conidia in MM, while isolate 23CM10 yielded the highest number of conidia in PDA after 7 dpi. In summary, our data demonstrated that the consistent NUV light with fluorescent conditions were most conducive for conidia induction in &lt;i&gt;B. oryzae&lt;/i&gt;. The selection of a medium for conidiation may vary depending on the &lt;i&gt;B. oryzae&lt;/i&gt; isolates, but using MM and PDA or SPA and RFA medium could be effective for spore induction. These findings will contribute to improving conidiation according to the characteristics of collected isolates of &lt;i&gt;B. oryzae&lt;/i&gt;.

Quantitative photocurrent scanning probe microscopy on PbS quantum dot monolayers.

Photoconductive atomic force microscopy can probe monolayers of PbS/perovskite quantum dots (QDs) with a contact area of 1-3 QDs in stable and reproducible acquisition conditions for I/V curves and photocurrent maps. From the measurements, quantitative values for the barrier height, built-in voltage, diffusion constant and ideality factor are deduced with high precision. The data analysis is based on modelling a superposition of the drift current of the photo-excited charges and a diffusion current across the interface barriers, providing physical insight into the underlying processes. Besides looking into PbS/perovskite on an indium tin oxide substrate, it is shown how the photocurrent is modified by changing either the QD ligand (to thiocyanate) or the substrate (to micro- and nanostructured gold). The dependence of the photocurrent on the light irradiance is found to follow a power law with an exponent of 0.64. Generally, quantitative measurements with high spatial resolution (on the single QD level) can provide significant insight into the processes in nanostructured hybrid optoelectronic components.

Video frame interpolation neural network for 3D tomography across different length scales

Three-dimensional (3D) tomography is a powerful investigative tool for many scientific domains, going from materials science, to engineering, to medicine. Many factors may limit the 3D resolution, often spatially anisotropic, compromising the precision of the information retrievable. A neural network, designed for video-frame interpolation, is employed to enhance tomographic images, achieving cubic-voxel resolution. The method is applied to distinct domains: the investigation of the morphology of printed graphene nanosheets networks, obtained via focused ion beam-scanning electron microscope (FIB-SEM), magnetic resonance imaging of the human brain, and X-ray computed tomography scans of the abdomen. The accuracy of the 3D tomographic maps can be quantified through computer-vision metrics, but most importantly with the precision on the physical quantities retrievable from the reconstructions, in the case of FIB-SEM the porosity, tortuosity, and effective diffusivity. This work showcases a versatile image-augmentation strategy for optimizing 3D tomography acquisition conditions, while preserving the information content.

An Ensemble Deep Neural Network-Based Method for Person Identification Using Electrocardiogram Signals Acquired on Different Days

Electrocardiogram (ECG) signals are a measure minute electrical signals generated during the cardiac cycle, a biometric signal that occurs during vital human activity. ECG signals are susceptible to various types of noise depending on the data acquisition conditions, with factors such as sensor placement and the physiological and mental states of the subject contributing to the diverse shapes of these signals. When the data are acquired in a single session, the environmental variables are relatively similar, resulting in similar ECG signals; however, in subsequent sessions, even for the same person, changes in the environmental variables can alter the signal shape. This phenomenon poses challenges for person identification using ECG signals acquired on different days. To improve the performance of individual identification, even when ECG data is acquired on different days, this paper proposes an ensemble deep neural network for person identification by comparing and analyzing the ECG recognition performance under various conditions. The proposed ensemble deep neural network comprises three streams that incorporate two well-known pretrained models. Each network receives the time-frequency representation of ECG signals as input, and a stream reuses the same network structure under different learning conditions with or without data augmentation. The proposed ensemble deep neural network was validated on the Physikalisch-Technische Bundesanstalt dataset, and the results confirmed a 3.39% improvement in accuracy compared to existing methods.