Formation of Fe-Ni Nanoparticle Strands in Macroscopic Polymer Composites: Experiment and Simulation

Magnetic-field-induced strand formation of ferromagnetic Fe-Ni nanoparticles in a PMMA-matrix is correlated with the intrinsic material parameters, such as magnetization, particle size, composition, and extrinsic parameters, including magnetic field strength and viscosity. Since various factors can influence strand formation, understanding the composite fabrication process that maintains the strand lengths of Fe-Ni in the generated structures is a fundamental step in predicting the resulting structures. Hence, the critical dimensions of the strands (length, width, spacing, and aspect ratio) are investigated in the experiments and simulated via different intrinsic and extrinsic parameters. Optimal parameters were found by optical microscopy measurements and finite-element simulations using COMSOL for strand formation of Fe50Ni50 nanoparticles. The anisotropic behavior of the aligned strands was successfully characterized through magnetometry measurements. Compared to the unaligned samples, the magnetically aligned strands exhibit enhanced conductivity, increasing the current by a factor of 1000.

Conception of a C-Arm Parametrization Device Evaluated by an Inverse Registration Approach: An application for 2D/3D Registration in Minimally Invasive Surgery

Camera resectioning is the process of finding the intrinsic and extrinsic parameters of the pinhole camera model. A mobile X-ray device (C-arm) can be represented with the pinhole camera model. The C-arm parameters can be determined with a parametrization device and the direct linear transformation (DLT) algorithm. We present the conception process of a parametrization device and further optimizations of the DLT algorithm. The evaluation of the parametrization quality is carried out with an inverse registration approach on a testing device. The seven landmarks in the testing device are compared on an X-ray image and a digitally reconstructed radiograph (DRR). The average distance of the corresponding landmarks between imaging modalities is 2.5 pixels. This deviation indicates an adequate design of the parametrization device for a 2D/3D registration in minimally invasive surgery.

Comprehensive analysis of regulation of DNA methyltransferase isoforms in human breast tumors

Significant reprogramming of epigenome is widely described during pathogenesis of breast cancer. Transformation of normal cell to hyperplastic cell and to neoplastic phenotype is associated with aberrant DNA (de)methylation, which, through promoter and enhancer methylation changes, activates oncogenes and silence tumor suppressor genes in variety of tumors including breast. DNA methylation, one of the major epigenetic mechanisms is catalyzed by evolutionarily conserved isoforms namely, DNMT1, DNMT3A and DNMT3B in humans. Over the years, studies have demonstrated intricate and complex regulation of DNMT isoforms at transcriptional, translational and post-translational levels. The recent findings of allosteric regulation of DNMT isoforms and regulation by other interacting chromatin modifying proteins emphasizes functional integrity and their contribution for the development of breast cancer and progression. DNMT isoforms are regulated by several intrinsic and extrinsic parameters. In the present review, we have extensively performed bioinformatics analysis of expression of DNMT isoforms along with their transcriptional and post-transcriptional regulators such as transcription factors, interacting proteins, hormones, cytokines and dietary elements along with their significance during pathogenesis of breast tumors. Our review manuscript provides a comprehensive understanding of key factors regulating DNMT isoforms in breast tumor pathology and documents unsolved issues.

Holographic Micromirror Array with Diffuse Areas for Accurate Calibration of 3D Light-Field Display

Light field 3D displays require a precise alignment between the display source and the micromirror-array screen for error free 3D visualization. Hence, calibrating the system using an external camera becomes necessary, before displaying any 3D contents. The inter-dependency of the intrinsic and extrinsic parameters of display-source, calibration-camera, and micromirror-array screen, makes the calibration process very complex and error-prone. Thus, several assumptions are made with regard to the display setup, in order to simplify the calibration. A fully automatic calibration method based on several such assumptions was reported by us earlier. Here, in this paper, we report a method that uses no such assumptions, but yields a better calibration. The proposed method adapts an optical solution where the micromirror-array screen is fabricated as a computer generated hologram with a tiny diffuser engraved at one corner of each elemental micromirror in the array. The calibration algorithm uses these diffusing areas as markers to determine the relation between the pixels of display source and the mirrors in the micromirror-array screen. Calibration results show that virtually reconstructed 3D scenes align well with the real world contents, and are free from any distortion. This method also eliminates the position dependency of display source, calibration-camera, and mirror-array screen during calibration, which enables easy setup of the display system.

Coaxiality Evaluation of Coaxial Imaging System with Concentric Silicon–Glass Hybrid Lens for Thermal and Color Imaging

Thermal imaging is useful for tasks such as detecting the presence of humans and recognizing surrounding objects in the operation of several types of robots, including service robots and personal mobility robots, which assist humans. Because the number of pixels on a thermal imager is generally smaller than that on a color imager, thermal images are more useful when combined with color images, assuming that the correspondence between points in the images captured by the two sensors is known. In the literature, several types of coaxial imaging systems have been reported that can capture thermal and color images, simultaneously, from the same point of view with the same optical axis. Among them, a coaxial imaging system using a concentric silicon–glass hybrid lens was devised. Long-wavelength infrared and visible light was focused using the hybrid lens. The focused light was subsequently split using a silicon plate. Separate thermal and color images were then captured using thermal and color imagers, respectively. However, a coaxiality evaluation of the hybrid lens has not been shown. This report proposes an implementation and coaxiality evaluation for a compact coaxial imaging system incorporating the hybrid lens. The coaxiality of the system was experimentally demonstrated by estimating the intrinsic and extrinsic parameters of the thermal and color imagers and performing 2D mapping between the thermal images and color images.

The Influence of Light Irradiation on the Photocatalytic Degradation of Organic Pollutants

The design of a photocatalytic process must consider intrinsic and extrinsic parameters affecting its overall efficiency. This study aims to outline the importance of balancing several factors, such as radiation source, total irradiance, photon flux, catalyst substrate, and pollutant type in order to optimize the photocatalytic efficiency. Titanium oxide was deposed by the doctor blade technique on three substrates (microscopic glass (G), flour-doped tin oxide (FTO), and aluminum (Al)), and the photocatalytic properties of the samples were tested on two pollutants (tartrazine (Tr) and acetamiprid (Apd)). Seven irradiation scenarios were tested using different ratios of UV-A, UV-B + C, and Vis radiations. The results indicated that the presence of a conductive substrate and a suitable ratio of UV-A and Vis radiations could increase the photocatalytic efficiency of the samples. Higher efficiencies were obtained for the sample Ti_FTO (58.3% for Tr and 70.8% for Apd) and the sample Ti_Al (63.8% for Tr and 82.3% for Apd) using a mixture of three UV-A and one Vis sources (13.5 W/m2 and 41.85 μmol/(m2·s)). A kinetic evaluation revealed two different mechanisms of reaction: (a) a one-interval mechanism related to Apd removal by Ti_FTO, Ti_Al (scenarios 1, 4, 5, and 7), and Ti_G samples (scenario 7) and (b) a two-interval mechanism in all other cases.

An Accurate Calibration Means for the Phase Measuring Deflectometry System

Calibration is a critical step for the phase measuring deflectometry system. Existing calibration methods are mainly optimizing the calibration parameters with respect to the 2D re-projection error criterion. However, such a procedure cannot reduce metric errors in the practical application. Therefore, an accurate and practical calibration method is proposed. In which, conventional calibration means is first applied for the primary calibration. Then, a precise square planar mirror is used for the optimization of system calibration parameters. All the intrinsic and extrinsic parameters are considered as a global multi-objective optimization problem. Three metric error criteria are introduced to evaluate the 3D reconstruction accuracy of the reference mirror. Compared with classical calibration means, which apply the parameter optimization in 2D image space to minimize the re-projection errors, the proposed optimization approach is executed in 3D space directly. An experiment and comparison are conducted to verify that the proposed optimal calibration approach can effectively reduce the system deviation and to improve the system measurement accuracy.

CLUBS: An RGB-D dataset with cluttered box scenes containing household objects

With the progress of machine learning, the demand for realistic data with high-quality annotations has been thriving. In order to generalize well, considerable amounts of data are required, especially realistic ground-truth data, for tasks such as object detection and scene segmentation. Such data can be difficult, time-consuming, and expensive to collect. This article presents a dataset of household objects and box scenes commonly found in warehouse environments. The dataset was obtained using a robotic setup with four different cameras. It contains reconstructed objects and scenes, as well as raw RGB and depth images, camera poses, pixel-wise labels of objects directly in the RGB images, and 3D bounding boxes with poses in the world frame. Furthermore, raw calibration data are provided, together with the intrinsic and extrinsic parameters for all the sensors. By providing object labels as pixel-wise masks, 3D, and 2D object bounding boxes, this dataset is useful for both object recognition and instance segmentation. The realistic scenes provided will serve for learning-based algorithms applied to scenarios where boxes of objects are often found, such as in the logistics sector. Both the dataset and the tools for data processing are published and available online.