Iris Images Research Articles

Iris Image Watermarking Technique for Security and Manipulation Reveal

Providing security while storing or sharing iris images has been considered as an interesting research topic and accordingly different iris image watermarking techniques have been presented. Most of the available techniques have been presented to ensure the attachment of the secret data to their related iris images or to hide a logo which can be used for copyright purposes. The previous security techniques can successfully meet their aims; however, they cannot reveal the manipulations in the iris region. This paper presents an iris image watermarking technique that can provide security and reveal manipulations in the iris region. At the sender side, the proposed technique divides the image into two regions (i.e., iris region and non-iris region) and generates the manipulation reveal data from the iris region then embeds it in the non-iris region. At the receiver side, the secret data is extracted from the non-iris region and compared with calculated data from the iris region to reveal manipulations if exist. Different experiments have been conducted to evaluate the performance of the proposed technique which proved its efficiency not only in providing security but also in revealing any manipulations in the iris region.

3D Gaze Estimation Using RGB-IR Cameras.

In this paper, we present a framework for 3D gaze estimation intended to identify the user's focus of attention in a corneal imaging system. The framework uses a headset that consists of three cameras, a scene camera and two eye cameras: an IR camera and an RGB camera. The IR camera is used to continuously and reliably track the pupil and the RGB camera is used to acquire corneal images of the same eye. Deep learning algorithms are trained to detect the pupil in IR and RGB images and to compute a per user 3D model of the eye in real time. Once the 3D model is built, the 3D gaze direction is computed starting from the eyeball center and passing through the pupil center to the outside world. This model can also be used to transform the pupil position detected in the IR image into its corresponding position in the RGB image and to detect the gaze direction in the corneal image. This technique circumvents the problem of pupil detection in RGB images, which is especially difficult and unreliable when the scene is reflected in the corneal images. In our approach, the auto-calibration process is transparent and unobtrusive. Users do not have to be instructed to look at specific objects to calibrate the eye tracker. They need only to act and gaze normally. The framework was evaluated in a user study in realistic settings and the results are promising. It achieved a very low 3D gaze error (2.12°) and very high accuracy in acquiring corneal images (intersection over union-IoU = 0.71). The framework may be used in a variety of real-world mobile scenarios (indoors, indoors near windows and outdoors) with high accuracy.

Cell Phase Identification in a Three-Dimensional Engineered Tumor Model by Infrared Spectroscopic Imaging.

Cell cycle progression plays a vital role in regulating proliferation, metabolism, and apoptosis. Three-dimensional (3D) cell cultures have emerged as an important class of in vitro disease models, and incorporating the variation occurring from cell cycle progression in these systems is critical. Here, we report the use of Fourier transform infrared (FT-IR) spectroscopic imaging to identify subtle biochemical changes within cells, indicative of the G1/S and G2/M phases of the cell cycle. Following previous studies, we first synchronized samples from two-dimensional (2D) cell cultures, confirmed their states by flow cytometry and DNA quantification, and recorded spectra. We determined two critical wavenumbers (1059 and 1219 cm-1) as spectral indicators of the cell cycle for a set of isogenic breast cancer cell lines (MCF10AT series). These two simple spectral markers were then applied to distinguish cell cycle stages in a 3D cell culture model using four cell lines that represent the main stages of cancer progression from normal cells to metastatic disease. Temporal dependence of spectral biomarkers during acini maturation validated the hypothesis that the cells are more proliferative in the early stages of acini development; later stages of the culture showed stability in the overall composition but unique spatial differences in cells in the two phases. Altogether, this study presents a computational and quantitative approach for cell phase analysis in tissue-like 3D structures without any biomarker staining and provides a means to characterize the impact of the cell cycle on 3D biological systems and disease diagnostic studies using IR imaging.

An effective authentication scheme for a secured IRIS recognition system based on a novel encoding technique

Iris recognition systems are well known for their accuracy than any other biometric system and very popular due to the permanent nature of the iris image. Moreover, it is easy to acquire the iris image even from a very long distance without causing much degradation to the image. In terms of the security perspective, very few biometric systems comprehensively address the vulnerabilities occurring as a result of security attacks. Among the security vulnerabilities, the possibility of compromise of Iris Code during the exchange of Iris information between client and server is a serious threat. In this work, an innovative model which generates a One Time Iris Code (OTIC) for every user authentication is proposed with the help of a novel encoding scheme. In the proposed model, the Iris Code generated distorted using pseudo-random number generator, timestamp, and rotation parameter and it will be different for each authentication session. The proposed technique will also reduce the possibility of compromising the original Iris Code. This will eventually enhance the security of the Iris recognition system. Moreover, the proposed method has less time complexity and high execution speed than the conventional Iris recognition systems. The accuracy of this model is comparable with the existing systems. Various performance analysis methods such as histogram processing for showing the pixel intensity of iris images, and calculation of EER based on entropy measurement for evaluating the severity of attacks using Base Score calculation have been implemented in this work.

Cross-modality consistency learning for visible-infrared person re-identification

Visible-infrared (IR) person re-identification is a technology that matches the identity of the same person in two modalities. The main challenge is to discover the differentiations between different identities and the similarities between the two modalities. To solve this problem, we propose a cross-modality consistency learning network, which jointly considers cross-modal learning and distillation learning. It consists of two associated components: the feature adaptation network (FANet) and the modality learning module (MLM). The FANet combines global and local information to extract more discriminative features on the same identity images, and MLM is used to alleviate modal differences between visible and IR images. Our model could adaptively select the high-quality person image according to the potential contribution of each image, to avoid negative knowledge transfer. Extensive experiments on the public SYSU-MM01 and RegDB datasets demonstrate the superiority of our approach over the current state-of-the-art technologies.

AFHIRIS: African Human Iris Dataset (Version 1)

Biometric systems remain the most widely used methods for identification and authentication purposes. Their wide acceptability has opened up more research into new application areas of biometric systems. However, biometric research requires an appropriate biometric dataset to validate the proposed technique. This dataset could be privately owned or publicly available for research purposes. In the field of iris biometric research, the iris dataset produced by the Chinese Academy of Sciences (CASIA) is the first, most popular, and widely used publicly available iris dataset. However, the increasing popularity and acceptability of human iris-related research have called for additional benchmarks, and therefore, new publicly available databases of human iris images. Existing publicly available human iris datasets have been collected from non-African subjects; therefore, this dataset is the first publicly available human iris dataset of African descent. Three categories of images were collected from 1028 volunteers that participated in the data collection task. The first category was made up of four iris images that were captured when the volunteers used spectacles, while the second category includes four iris images that were captured when the volunteers wore no spectacles. However, the third category of iris images was obtained from eight volunteers that used print-patterned contact lenses. Only four images were captured from volunteers in this category as they were not asked to put on spectacles. In addition to the iris images captured, soft biometric features such as age, gender, state of origin, weight, and height of the volunteers were also captured. It is strongly believed that this unique collection of iris datasets of African descent will open up new research in the study of the human iris.

Data Augmentation for Small Sample Iris Image Based on a Modified Sparrow Search Algorithm

Training convolutional neural networks (CNN) often require a large amount of data. However, for some biometric data, such as fingerprints and iris, it is often difficult to obtain a large amount of data due to privacy issues. Therefore, training the CNN model often suffers from specific problems, such as overfitting, low accuracy, poor generalization ability, etc. To solve them, we propose a novel image augmentation algorithm for small sample iris image in this article. It is based on a modified sparrow search algorithm (SSA) called chaotic Pareto sparrow search algorithm (CPSSA), combined with contrast limited adaptive histogram equalization (CLAHE). The CPSSA is used to search for a group of clipping limit values. Then a set of iris images that satisfies the constraint condition is produced by CLAHE. In the fitness function, cosine similarity is used to ensure that the generated images are in the same class as the original one. We select 200 categories of iris images from the CASIA-Iris-Thousand dataset and test the proposed augmentation method on four CNN models. The experimental results show that, compared with the some standard image augmentation methods such as flipping, mirroring and clipping, the accuracy and Equal Error Rate (EER)of the proposed method have been significantly improved. The accuracy and EER of the CNN models with the best recognition performance can reach 95.5 and 0.6809 respectively. This fully shows that the data augmentation method proposed in this paper is effective and quite simple to implement.

Surface engineering of mixed conjugated/polyelectrolyte brushes – Tailoring interface structure and electrical properties

HypothesisMixed polymer brushes (MPBs) could be synthesized by surface dilution of homopolymer brushes and subsequent grafting of other type of chains in the formed voids. Nanophase separation and dynamics of surface-grafted chains could be tailored by modification of their molecular architecture. Mixed polyelectrolyte and conjugated chains contribute synergistically to tailor properties of the coating. ExperimentsA new synthetic strategy that allowed spatially controlled grafting of poly(sodium 4-styrenesulfonate) chains (PSSNa) in close neighborhood of poly(3-methylthienyl methacrylate) (PMTM) brushes (precursors of the conjugated chains) using surface-initiated polymerizations was developed. The final mixed conjugated/polyelectrolyte brushes were prepared by template polymerization of pendant thiophene groups in PMTM chains. Surface dynamics and nanophase separation of MPBs were studied by nanoscale resolution IR imaging, SIMS profiling and AFM mapping in selective solvents. FindingsUnconjugated MPBs were shown to undergo vertical, and horizontal nanophase separation, while the size and shape of the nanodomains were dependent on molar ratio of the mixed chains and their relative lengths. Generation of the conjugated chains led to diminishing of nanophase separation thanks to stronger mutual interactions of conjugated PMTM and PSSNa (macromolecular mixing). The obtained systems demonstrated tunable interfacial structure and resistance switching phenomenon desired in construction of smart surfaces or memristive devices.

Surface Functionalization of Face Masks with Cold Plasma and Its Effect in Anchoring Polyphenols Extracted from Agri-Food

To improve the capability of non-woven polypropylene-based fabric (NWF-PP) used for face mask production to retain active biomolecules such as polyphenols, the surface functionalization of NWF-PP-directly cut from face masks-was carried out by employing cold plasma with oxygen. The nature/structure of the functional groups, as well as the degree of functionalization, were evaluated by ATR-FTIR and XPS by varying the experimental conditions (generator power, treatment time, and oxygen flow). The effects of plasma activation on mechanical and morphological characteristics were evaluated by stress-strain measurements and SEM analysis. The ability of functionalized NWF-PP to firmly anchor polyphenols extracted from cloves was estimated by ATR-FTIR analysis, IR imaging, extractions in physiological solution, and OIT analysis (before and after extraction), as well as by SEM analysis. All the results obtained converge in showing that, although the plasma treatment causes changes-not only on the surface-with certain detriment to the mechanical performance of the NWF-PP, the incorporated functionalities are able to retain/anchor the active molecules extracted from the cloves, thus stabilizing the treated surfaces against thermo-oxidation even after prolonged extraction.

Fusion of Gabor filter and steerable pyramid to improve iris recognition system

<span>Iris recognition system is a technique of identifying people using their distinctive features. Generally, this technique is used in security, because it offers a good reliability. Different researchers have proposed new methods for iris recognition system to increase its effectiveness. In this paper, we propose a new method for iris recognition based on Gabor filter and steerable pyramid decomposition. It’s an efficient and accurate linear multi-scale, multi-orientation image decomposition to capture texture details of an image. At first, the iris image is segmented, normalized and decomposed by Gabor filter and steerable pyramid method. Multiple sub-band are generated by applying steerable pyramid on the input image. High frequency sub-band is ignored to eliminate noise and increase the accuracy. The method was validated using CASIA-v4 (Chinese Academy of Sciences Institute of Automation), IITD (</span><span>Indian Institute of Technology Delhi) and UPOL (University of Phoenix Online) databases. The performance of the proposed method is better than the most methods in the literature. The proposed algorithm provides accuracy of 99.99%. False acceptance rate (FAR), equal error rate (EER) and genuine acceptance rate (GAR) have also been improved.</span>

A Symmetrical Encoder-Decoder Network with Transformer for Noise-Robust Iris Segmentation

Iris images captured in less-constrained environments are susceptible to the interference of noise such as specular reflections, eyelash and hair occlusions, motion and defocus blur, which makes it hard to accurately segment the valid iris region. To solve the problem, a symmetrical encoder-decoder network with Transformer is proposed for noise-robust iris segmentation. First, Swin Transformer is employed as the encoder to feed a sequence of the input image patches into hierarchical Transformer modules, so as to model the long-range dependencies of image pixels through self-attention mechanism, and enhance the interaction of contextual information. Secondly, a Transformer decoder which is symmetrical with the encoder is constructed, where the high-order context features extracted earlier are decoded in multiple layers. Besides, the skip connections are introduced to fuse the multi-scale features from the encoder with the up-sampled decoded features, which reduces the loss of spatial position information caused by down-sampling. Finally, supervised learning is carried out on each stage of the decoder, which improves the quality of extracted different-scaled features. A comparative experiment is carried out on three challenging noisy near-infrared (NIR) and visible (VIS) iris datasets, i.e., NICE.I, CASIA.v4-distance, and MICHE-I. Results show that the proposed method achieves better segmentation performance than several benchmark methods including traditional methods, convolutional neural network-based methods and existing Transformer-based methods on multiple evaluation metrics like <italic>E</italic>₁, <italic>E</italic>₂, <italic>F</italic>₁, and MIOU, and particularly demonstrates significant advantages in reducing the interference of adverse noise. The iris recognition experiment on the CASIA.v4-distance dataset also shows that the proposed method can effectively improve the performance of iris recognition, suggesting a good application potential.

Image Quality of Submillimeter High-Spatial-Resolution 2D Late Gadolinium-enhanced Images in Cardiac MRI: A Feasibility Study.

To evaluate the image quality of high-spatial-resolution two-dimensional (2D) late gadolinium enhancement (LGE) cardiac MRI compared with conventional normal-resolution LGE MRI. This prospective study included participants suspected of having cardiomyopathy who underwent cardiac MRI between March 2021 and December 2021. Normal-resolution and high-resolution 2D LGE sequences (inversion recovery [IR] and phase-sensitive inversion recovery [PSIR]) were performed at 3 T. Resolution was compared between normal-resolution and high-resolution images obtained in a quality assurance phantom. In vivo image quality and resolution were evaluated qualitatively using a five-point scoring system. Receiver operating characteristic curve analysis was used for LGE detection performance. Border sharpness was assessed with profile curve measurement. The contrast-to-noise ratio (CNR) between hyperenhancement and remote myocardium and LGE detection performance were calculated using normal-resolution IR images as the reference. In total, 120 participants were evaluated (mean age, 56 years ± 17 [SD]; 72 men). Features smaller than 1 mm were detectable only on high-resolution images of the phantom. In vivo, the image resolution score with high-resolution LGE was 4.14-4.24, which was higher than the normal-resolution LGE reference score of 2.99 (P < .05). Border sharpness was higher in high-resolution images (P < .001). Receiver operating characteristic curve analysis revealed no evidence of a difference in LGE detection between normal-resolution and high-resolution images. There was also no evidence of a change in CNR of LGE in IR and PSIR magnitude compared with reference images. Comparison of image quality in 2D high-resolution and normal-resolution LGE cardiac MRI demonstrated the highest resolution for high-resolution IR and high-resolution PSIR magnitude sequences.Keywords: Cartilage Imaging, MRI, Cardiac, Heart, Imaging Sequences, Comparative Studies Supplemental material is available for this article. © RSNA, 2022.

자가 진단을 위한 스마트폰 적용 LED 홍채 렌즈 연구

Digital health services tend to go beyond simple health management and auxiliary means to advanced disease monitoring. The use of apps related to particular diseases such as diabetes and the circulatory system is a large increasing every year. In particular, there is connected to the brain and nervous system tissues so it is possible to diagnose the disease state of the related tissue from all the changes in the iris image. Although an iris glasses camera module was developed in the previous study, there was a limit in user convenience in the method of acquiring images using the USB communication method. In addition, if the disease is read with an opaque iris image when taking an iris photograph with a smartphone, the patient’s disease may be missed as a result of no disease, so it is important to obtain a clear iris image. This research proposed an iris recognition lens implementation of a small and can be easily carried and designed considering the safety of patients with high-definition iris image capture. In this study, an LED iris lens was designed and manufactured can iris recognition to show the crown of the autonomic nervous system. It is necessary to verify whether the developed LED iris lens can perform to show the crown of the autonomic nervous system. Measurements are classfied in two ways iris clarity through smartphones, iris clarity through Test Tool, and the number of attempts to secure a clear iris image. The development lange of the Test Tool is Python, and the Average Square Shrinking algorithm was applied to detect autonomic neuropathy in the iris. When verifying the iris, the iris data set provided by CASIA(China’s Biological Research Institute)was used as reference data. As a result of the test, The conventional app for iris recognition can obtain at most 12% of clarity which is not enough to diagnosis the diseases while using the LED iris lens on the smartphone gives 91% of the iris brightness. Additionally, the number of trials for iris recognition was only once while the conventional method needs at least 8 times to get a clear iris image. As a result of verifying the iris image by developing an LED iris lens through this study, it was possible to secure a clear iris image at the level of disease diagnosis. A clear iris reduces diagnostic errors and contributes sufficiently to patient safety. There is a lot of interest in mobile health apps for self-diagnosis. However, it is difficult to find useful App. for self-diagnosis. After applying the LED iris lens developed through this study, it is judged that it will be of practical help to disease prevention if the health status of the iris is checked periodically.

Adaptive IR- and Water-Gating Textiles Based on Shape Memory Fibers.

Thermoregulation is an essential function of the human body for adapting to the surrounding temperature. Stimuli-responsive smart textiles can provide effective protection of the human skin temperature from a continuously changing environment. Herein, we develop a smart textile based on shape memory polymer (SMP) fibers for adaptive regulation of IR and water transmission on human skin. An SMP textile is fabricated with hierarchical micro/nanoporous structures to enhance thermal insulation performance, and silver nanowires are coated on one side to provide asymmetric IR reflectivity and hydrophilicity. The porous SMP textile shows great tunability of thermal insulation and asymmetric wettability by deformation and recovery of the shape and structure in response to stimuli. The degree of thermal insulation is controlled by 65.7% of the original value, and the surface temperature of the SMP textile on a hot plate is successfully controlled in the IR images due to adaptive IR reflectivity. Additionally, the directional transportation of water droplets can be switched on/off according to the shape of the SMP textiles, which can be employed for sweat removal from the human skin. This IR- and water-gating smart textile can provide a feasible strategy for protecting the human skin from external environmental changes.

Применение инфракрасных изображений для обнаружения и визуализации источников радиоактивного излучения

The application of thermal images for visualization of small-sized, masked sources of radioactive radiation by the heating of objects caused by them as a result of heat release during radioactive decay is considered. The acquisition of IR images of a thermal imitator of a radioactive source is simulated. Known applications of thermal imagers for remote visualization of radioactive sources are analyzed. IR images of fragments of irradiated fuel elements are presented.

Random area pixel variation and random area transform for visible-infrared cross-modal pedestrian re-identification

In this paper, we propose the Random Area Pixel Variation (RAPV) which is a straightforward and enormously effective data enhancement method for visual-infrared cross-modal pedestrian re-identification (VI-REID) and extend this method as the Random Area Transform (RAT). The difficulty of the VI-REID task is to correlate features of heterogeneous data from both modalities. RGB images and IR images contain three channels and one channel, respectively. We add or subtract a random value to the pixel values for each channel of the two types of images, which enables them to retain the image structure information even with random color changes. Also, this allows more correspondence to be established between the two modal images. In this way, the models can find correlative features with better robustness. In addition, to control the noise generated by random variations, we apply the method only to a random rectangular area of arbitrary size in the image, which is RAPV. Simultaneously, we found that global data augmentation methods provide better results when used locally, so we make these methods applicable to a random rectangular region of arbitrary size in the image, which is RAT. Both RAPV and RAT use very few computational resources and can be flexibly integrated into existing models, outperforming many existing methods when applied only to baseline networks.

Research on strabismus iris segmentation model based on deep snake multitask learning

Biometrics is widely used in various fields as a means of biological individual identification, and iris recognition is recognized as an efficient, stable, and secure biometric method. However, when the cooperation with users is reduced under complex scenes, the collected iris images are prone to squint or defocus, which causes serious difficulties for iris preprocessing, especially iris location, and segmentation. To solve this problem, we propose a multitask deep learning model based on deep snake. Using the closed-loop prior conditions of the inner and outer boundaries of the iris itself, the active contour method is used to achieve accurate iris location, and semantic segmentation is carried out in the effective region. First, the initial iris location and segmentation results are obtained using the target detection module based on mask regions with convolutional neural networks, then the initial contour is located by poles. The iris boundary is represented by a set of ordered point coordinates through equal spacing sampling. The initial contour is transferred as input to the active contour fitting network based on deep snake to obtain accurate iris location results. Finally, the segmentation results are obtained by the internal and external boundary constraint models. In the segmentation process, efficient channel attention mechanism is introduced to improve the segmentation accuracy and make the network focus on the effective iris region. The SoftPool layer is used to reduce the loss of image detail information caused by pooling, so that the whole network is differentiable. The performance improvements of 3.26%, 3.9%, and 3.45% are obtained on the three squint iris datasets, respectively, which proves the effectiveness of the model in solving the squint iris problem and improves the accuracy and robustness of iris recognition in complex scenes.

Dynamic iris inner edge location method based on distance-regularized level set

Iris localization is an important part of iris recognition. Aiming at the influence of irregular pupil circle and illumination inequality in dynamic iris localization in actual scene, a dynamic iris inner edge location method based on distance regularized level set model was proposed. According to the geometric features of iris, the pupil and iris are coarsely located through the secondary projection of binary image, and the initial contour of evolution of horizontal set is determined. Accurate positioning of the inner edge of iris is achieved by the improved distance regularized level set model of edge indicator function. Iris images Database were respectively experimented with this paper and another improved distance regularized level set model, and the positioning effect was evaluated by using the Jakard similarity coefficient. The results show that the distance regularized level set model based dynamic iris internal edge localization algorithm proposed in this paper can significantly reduce the number of iterations and positioning time of the localization algorithm on the basis of ensuring the positioning accuracy,which provides a theoretical basis and technical reference for the application of iris recognition technology.

Biometric identification of Black Bengal goat: unique iris pattern matching system vs deep learning approach.

Iris pattern recognition system is well developed and practiced in human, however, there is a scarcity of information on application of iris recognition system in animals at the field conditions where the major challenge is to capture a high-quality iris image from a constantly moving non-cooperative animal even when restrained properly. The aim of the study was to validate and identify Black Bengal goat biometrically to improve animal management in its traceability system. Forty-nine healthy, disease free, 3 months±6 days old female Black Bengal goats were randomly selected at the farmer's field. Eye images were captured from the left eye of an individual goat at 3, 6, 9, and 12 months of age using a specialized camera made for human iris scanning. iGoat software was used for matching the same individual goats at 3, 6, 9, and 12 months of ages. Resnet152V2 deep learning algorithm was further applied on same image sets to predict matching percentages using only captured eye images without extracting their iris features. The matching threshold computed within and between goats was 55%. The accuracies of template matching of goats at 3, 6, 9, and 12 months of ages were recorded as 81.63%, 90.24%, 44.44%, and 16.66%, respectively. As the accuracies of matching the goats at 9 and 12 months of ages were low and below the minimum threshold matching percentage, this process of iris pattern matching was not acceptable. The validation accuracies of resnet152V2 deep learning model were found 82.49%, 92.68%, 77.17%, and 87.76% for identification of goat at 3, 6, 9, and 12 months of ages, respectively after training the model. This study strongly supported that deep learning method using eye images could be used as a signature for biometric identification of an individual goat.

Chemical imaging of cellular ultrastructure by null-deflection infrared spectroscopic measurements

Nearfield spectroscopic imaging techniques can be a powerful tool to map both cellular ultrastructure and molecular composition simultaneously but are currently limited in measurement capability. Resonance enhanced (RE) atomic force microscopy infrared (AFM-IR) spectroscopic imaging offers high-sensitivity measurements, for example, but probe-sample mechanical coupling, nonmolecular optical gradient forces, and noise overwhelm recorded chemical signals. Here, we analyze the key factors limiting AFM-IR measurements and propose an instrument design that enables high-sensitivity nanoscale IR imaging by combining null-deflection measurements with RE sensitivity. Our developed null-deflection scanning probe IR (NDIR) spectroscopic imaging provides ∼24× improvement in signal-to-noise ratio (SNR) compared with the state of the art, enables optimal signal recording by combining cantilever resonance with maximum laser power, and reduces background nonmolecular signals for improved analytical accuracy. We demonstrate the use of these properties for high-sensitivity, hyperspectral imaging of chemical domains in 100-nm-thick sections of cellular acini of a prototypical cancer model cell line, MCF-10A. NDIR chemical imaging enables facile recording of label-free, chemically accurate, high-SNR vibrational spectroscopic data from nanoscale domains, paving the path for routine studies of biomedical, forensic, and materials samples.