Nanocrystals of Metal Halide Perovskites and Their Analogues as Scintillators for X-ray detection

Scintillators are widely used for X-ray detection in various fields, such as medical diagnostics, industrial inspection and homeland security. Nanocrystals of metal halide perovskites and their analogues showed great advantages as X-ray scintillators due to their cheap manufacturing, fast decay time, and room temperature scintillation from quantum confinement effect. However, there are still many challenges unsolved for further industrialization. Herein, it is necessary to summarize the progress of scintillators based on nanocrystals of metal halide perovskites and their analogues. In first section, the scintillation mechanism and key parameters are outlined. Then, various nanocrystals of metal halide perovskites and their analogues used as scintillators are reviewed. Finally, the challenges and outlook are discussed. It is believed that nanocrystals of metal halide perovskites and their analogues are favorable for large-area and flexible X-ray detectors.

Study of large axial space rebinning algorithm in γ-photon industrial tomography image reconstruction

The accuracy of the existing single slice and Fourier rebinning algorithms depends on the projection angle of the line of response. The increase of such projection angle with the detector size, typical in the large axial space of γ-photon industrial detection, and the loss of some projection data after rebinning, result in the degradation of the image quality. In addition, those algorithms consider the probability of positron annihilation equally distributed along the line of response, which prevents to estimate accurately the positions of the annihilation point, and can originate artifacts and noise in the reconstructed image. In this work, we propose an alternative large axial space rebinning algorithm. In that algorithm, initially the line of response is divided into transverse and axial components. Then, each line of response is uniformly rebinned into all the 2D sinogram data intersecting with it. To improve the accuracy of the estimate of the annihilation point location and suppress the noise effectively, we assign a Gaussian weight coefficient to the projection data, and optimise the rebinning algorithm with it. Finally, we reconstruct the image on the basis of the 2D sinograms with the optimised weights. On the computational side, the algorithm is also accelerated by making use of parallel computing. Both simulation and experimental results show that the proposed method improves the contrast and spatial resolution of 2D reconstructed images. Furthermore, the reconstruction time is not affected by the new method, which is therefore expected to meet the demand of γ-photon industrial inspection imaging.

Multi-operator feature enhancement methods for industrial defect detection

Deep learning based object detection algorithms have been gradually applied to industrial defect detection, but the resulted accuracy does not fully meet the needs of industrial inspection. In order to enhance image features, this paper proposes a series of image preprocessing schemes based on edge detection operators, using a single-operator preprocessing scheme, a multi-operator serial preprocessing scheme and a multi-operator parallel preprocessing scheme for image preprocessing of data to enhance the edge features of images. The validation experiment of the SSD based object detection algorithm is performed on dataset used for industrial inspection, to verify the effectiveness of the processing schemes above. The result shows that the multi-operator based image preprocessing method is effective in improving the accuracy of surface defect detection in the field of industrial defect detection.

A Study of Terahertz-Wave Cylindrical Super-Oscillatory Lens for Industrial Applications

This paper describes the design and development of a cylindrical super-oscillatory lens (CSOL) for applications in the sub-terahertz frequency range, which are especially ideal for industrial inspection of films using terahertz (THz) and millimeter waves. Product inspections require high resolution (same as inspection with visible light), long working distance, and long depth of focus (DOF). However, these are difficult to achieve using conventional THz components due to diffraction limits. Here, we present a numerical approach in designing a 100 mm × 100 mm CSOL with optimum properties and performance for 0.1 THz (wavelength λ = 3 mm). Simulations show that, at a focal length of 70 mm (23.3λ), the focused beam by the optimized CSOL is a thin line with a width of 2.5 mm (0.84λ), which is 0.79 times the diffraction limit. The DOF of 10 mm (3.3λ) is longer than that of conventional lenses. The results also indicate that the generation of thin line-shaped focal beam is dominantly influenced by the outer part of the lens.

A FRAMEWORK FOR DESIGNING AN AUGMENTED REALITY APPLICATION FOCUSING ON OBJECT FUNCTION FOR CHILDREN WITH AUTISM

Augmented Reality (AR) is the evolution of the concept of Virtual Reality (VR). Its goal is to enhance a person's perception of the surrounding world. Augmented reality techniques are often applied to facilitate understanding and create attractive educational and health tools. As such, augmented reality is deemed suitable to be implemented as one of the potential intervention methods as the treatment of autism in a fun environment. Hence, this study is aimed to develop a conceptual framework to design augmented reality applications based on object function to help in the communication of children with autism. The study framework will be developed based on vision-based object recognition. Object recognition has been used in many applications, especially in bio-imaging, industrial inspection, and robotic vision. The findings of this study will benefit autistic children in visual communication and indirectly help them to effectively link objects with their functions. This framework will then help designers to develop augmented reality applications suited to be an intervention tool that fits the need of autistic children.

A Tutorial Review on Point Cloud Registrations: Principle, Classification, Comparison, and Technology Challenges

A point cloud as a collection of points is poised to bring about a revolution in acquiring and generating three-dimensional (3D) surface information of an object in 3D reconstruction, industrial inspection, and robotic manipulation. In this revolution, the most challenging but imperative process is point could registration, i.e., obtaining a spatial transformation that aligns and matches two point clouds acquired in two different coordinates. In this survey paper, we present the overview and basic principles, give systematical classification and comparison of various methods, and address existing technical problems in point cloud registration. This review attempts to serve as a tutorial to academic researchers and engineers outside this field and to promote discussion of a unified vision of point cloud registration. The goal is to help readers quickly get into the problems of their interests related to point could registration and to provide them with insights and guidance in finding out appropriate strategies and solutions.

Supervised Anomaly Detection with Highly Imbalanced Datasets Using Capsule Networks

Detecting anomalous patterns in data is a relevant task in many practical applications, such as defective items detection in industrial inspection systems, cancer identification in medical images, or attacker detection in network intrusion detection systems. This paper focuses on detection of anomalous images, this is images that visually deviate from a reference set of regular data. While anomaly detection has been widely studied in the context of classical machine learning, the application of modern deep learning techniques in this field is still limited. We here propose a capsule-based network for anomaly detection in an extremely imbalanced fully supervised context: we assume that anomaly samples are available, but their amount is limited if compared to regular data. By using a variant of the standard CapsNet architecture, we achieved state-of-the-art results on the MNIST, F-MNIST and K-MNIST datasets.