Improved interactive color visualization approach for hyperspectral images

Hyperspectral images (HSIs) have become increasingly prominent as they can maintain the subtle spectral differences of the imaged objects. Designing approaches and tools for analyzing HSIs presents a unique set of challenges due to their high-dimensional characteristics. An improved color visualization approach is proposed in this article to achieve communication between users and HSIs in the field of remote sensing. Under the real-time interactive control and color visualization, this approach can help users intuitively obtain the rich information hidden in original HSIs. Using the dimensionality reduction (DR) method based on band selection, high-dimensional HSIs are reduced to low-dimensional images. Through drop-down boxes, users can freely specify images that participate in the combination of RGB channels of the output image. Users can then interactively and independently set the fusion coefficient of each image within an interface based on concentric circles. At the same time, the output image will be calculated and visualized in real time, and the information it reflects will also be different. In this approach, channel combination and fusion coefficient setting are two independent processes, which allows users to interact more flexibly according to their needs. Furthermore, this approach is also applicable for interactive visualization of other types of multi-layer data.

A Flexible Global GCRO-DR Method for Shifted Linear Systems and General Coupled Matrix Equations

In this paper, we mainly focus on the development and study of a new global GCRO-DR method that allows both the flexible preconditioning and the subspace recycling for sequences of shifted linear systems. The novel method presented here has two main advantages: firstly, it does not require the right-hand sides to be related, and, secondly, it can also be compatible with the general preconditioning. Meanwhile, we apply the new algorithm to solve the general coupled matrix equations. Moreover, by performing an error analysis, we deduce that a much looser tolerance can be applied to save computation by limiting the flexible preconditioned work without sacrificing the closeness of the computed and the true residuals. Finally, numerical experiments demonstrate that the proposed method illustrated can be more competitive than some other global GMRES-type methods.

Dimensionality Reduction of Hyperspectral Image Based on Local Constrained Manifold Structure Collaborative Preserving Embedding

Graph learning is an effective dimensionality reduction (DR) manner to analyze the intrinsic properties of high dimensional data, it has been widely used in the fields of DR for hyperspectral image (HSI) data, but they ignore the collaborative relationship between sample pairs. In this paper, a novel supervised spectral DR method called local constrained manifold structure collaborative preserving embedding (LMSCPE) was proposed for HSI classification. At first, a novel local constrained collaborative representation (CR) model is designed based on the CR theory, which can obtain more effective collaborative coefficients to characterize the relationship between samples pairs. Then, an intraclass collaborative graph and an interclass collaborative graph are constructed to enhance the intraclass compactness and the interclass separability, and a local neighborhood graph is constructed to preserve the local neighborhood structure of HSI. Finally, an optimal objective function is designed to obtain a discriminant projection matrix, and the discriminative features of various land cover types can be obtained. LMSCPE can characterize the collaborative relationship between sample pairs and explore the intrinsic geometric structure in HSI. Experiments on three benchmark HSI data sets show that the proposed LMSCPE method is superior to the state-of-the-art DR methods for HSI classification.

Manufacturing Polypropylene (PP)/Waste EPDM Thermoplastic Elastomers Using Ultrasonically Aided Twin-Screw Extrusion

The compounding of waste EPDM from postindustrial scrap with polypropylene (PP) is a possible way to manufacture thermoplastic elastomers to solve a significant environmental problem. Accordingly, the present study considers the one-step (OS), two-step (TS), and dynamic revulcanization (DR) compounding methods for the manufacturing of PP/EPDM blends at different ratios of components with the aid of an ultrasonic twin-screw extruder (TSE) at various ultrasonic amplitudes. In the OS method, PP and waste EPDM particles were directly compounded using TSE with and without ultrasonic treatment. In the TS and DR methods, the waste EPDM particles were fed into the TSE and devulcanized without and with ultrasonic treatment. Then, in the TS method the devulcanized EPDM was compounded with PP using TSE without the imposition of ultrasound. In the DR method, the devulcanized EPDM after compounding with curatives was mixed with PP and dynamically revulcanized without the imposition of ultrasound in TSE. The die pressure during compounding was recorded and correlated with the rheological properties of compounds. The mechanical properties of the PP/EPDM blends obtained in the OS and TS methods did not show any improvement with ultrasonic treatment. In the DR method, all the PP/EPDM blends showed a significant increase in the tensile strength and elongation with ultrasonic amplitude and a slight decrease in the Young’s modulus. In particular, a tensile strength of 30 MPa and an elongation at break of 400% were achieved at an ultrasonic amplitude of 13 μm for the PP/EPDM blend at a ratio of 75/25. The complex viscosity, storage, and loss moduli of dynamically revulcanized PP/EPDM blends increased with the ultrasonic amplitude while the loss tangent decreased. At the same time, the results for the blends obtained by the OS and TS methods showed an opposite trend in the dynamic property behavior with the ultrasonic amplitude. Optical micrographs indicated that the blends obtained by the DR method at an ultrasonic treatment at 13 μm showed the lowest sizes of dispersed revulcanized EPDM particles in the PP matrix, leading to the excellent performance of these thermoplastic elastomers.

Optimizing Teacher-Student Collaborative Assessment in the Production-Oriented Approach: A Dialectical Research

Teacher-student collaborative assessment (TSCA) aims to address the challenges of responding to students’ work in the Production-Oriented Approach: low efficiency and poor effectiveness. As part of a bigger project carried out in a Chinese university over a period of three years, the present study explored how the teacher prepared and implemented TSCA in class, especially with a focus on how she determined the assessing objective and worked collaboratively with her students in class to achieve it, using the students’ written and translated texts as examples. By adopting the dialectical research (DR) method, this paper collected qualitative data such as teaching plans, classroom recordings, and reflective journals of the teacher-researcher (the author), along with students’ written drafts and translated texts. TSCA theory and classroom practice have been refined simultaneously by means of putting theory into practice and reflecting upon it. The optimized pre-class and in-class procedures may shed some light on applying TSCA to L2 classrooms.

Biomechanical comparison between single-row with triple-loaded suture anchor and suture-bridge double-row rotator cuff repair

Background Numerous biomechanical and clinical studies comparing different techniques for rotator cuff repair have been reported, yet universal consensus regarding the superior technique has not achieved. A medially-based single-row with triple-loaded suture anchor (also referred to as the Southern California Orthopedic Institute [SCOI] row) and a suture-bridge double-row (SB-DR) with Push-Locks have been shown to result in comparable improvement in treating rotator cuff tear, yet the biomechanical difference is unknown. The purpose of the current study was to determine whether a SCOI row repair had comparable initial biomechanical properties to a SB-DR repair. Methods Six matched pairs of fresh-frozen cadaveric shoulders with full-thickness supraspinatus tendon tears we created were included. Two different repairs were performed for each pair (SCOI row and SB-DR methods). Specimens were mounted on a material testing machine to undergo cyclic loading, which was cycled from 10 to 100 N at 1 Hz for 500 cycles. Construct gap formation was recorded at an interval of 50 cycles. Samples were then loaded to failure and modes of failure were recorded. Repeated-measures analysis of variance and pair-t test were used for statistical analyses. Results The construct gap formation did not differ between SCOI row and SB-DR repairs (P = 0.056). The last gap displacement was 1.93 ± 0.37 mm for SCOI row repair, and 1.49 ± 0.55 mm for SB-DR repair. The tensile load for 5 mm of elongation and ultimate failure were higher for SCOI row repair compared to SB-DR repair (P = 0.011 and 0.028, respectively). The ultimate failure load was 326.34 ± 11.52 N in the SCOI row group, and 299.82 ± 27.27 N in the SB-DR group. Rotator cuff repair with the SCOI row method failed primarily at the suture- tendon interface, whereas pullout of the lateral row anchors was the primary mechanism of failure for repair with the SB-DR method. Conclusion Rotator cuff repair with the SCOI row method has superior biomechanical properties when compared with the SB-DR method. Therefore, SCOI row repair using a medially-based single-row technique with triple-loaded suture anchor is recommended to improve the initial strength in treating full-thickness rotator cuff tears.

Experimental Studies of Typical Defects on Large Capacity Hoop-Wrapped Composite Cylinder of Steel Liner Based on X-Ray Digital Radiography Test

Tube trailers assembled with large capacity hoop-wrapped composite cylinder of steel liner (i.e., large capacity type 2 tube (LCT2T)) have shown an increasing trend in China. It is an urgent issue to detect nondestructively the defects of cuts, scratches and voids on the composite overwrap, and corrosion, cracks or other defects on the steel liner during their use and manufacturing processes. In this paper, the double-wall single-image technique of X-ray digital radiography (DR) method was studied for the typical defects on the LCT2T by making specimens of cracks and pitting corrosion on the steel liner, as well as cuts, scratches and void defects on the composite overwrap. The optimal penetration parameter was selected based on the identification of image quality indicators (IQI), and the detection sensitivity of the DR method for the typical defects on the LCT2T was obtained. The results showed that the above-mentioned artificial defects were effectively detected with double-wall single-image technique, and this technique had a higher detection sensitivity to longitudinal defects on the composite overwrap of the LCT2T than that to circumferential defects, as well as the detection sensitivity of steel liner defects was higher than that of composite overwrap defects.

Dimensionality Reduction of Hyperspectral Image Using Spatial-Spectral Regularized Sparse Hypergraph Embedding

Many graph embedding methods are developed for dimensionality reduction (DR) of hyperspectral image (HSI), which only use spectral features to reflect a point-to-point intrinsic relation and ignore complex spatial-spectral structure in HSI. A new DR method termed spatial-spectral regularized sparse hypergraph embedding (SSRHE) is proposed for the HSI classification. SSRHE explores sparse coefficients to adaptively select neighbors for constructing the dual sparse hypergraph. Based on the spatial coherence property of HSI, a local spatial neighborhood scatter is computed to preserve local structure, and a total scatter is computed to represent the global structure of HSI. Then, an optimal discriminant projection is obtained by possessing better intraclass compactness and interclass separability, which is beneficial for classification. Experiments on Indian Pines and PaviaU hyperspectral datasets illustrated that SSRHE effectively develops a better classification performance compared with the traditional spectral DR algorithms.

Weighted Neighborhood Preserving Ensemble Embedding

Neighborhood preserving embedding (NPE) is a classical and very promising supervised dimensional reduction (DR) technique based on a linear graph, which preserves the local neighborhood relations of the data points. However, NPE uses the K nearest neighbor (KNN) criteria for constructing an adjacent graph which makes it more sensitive to neighborhood size. In this article, we propose a novel DR method called weighted neighborhood preserving ensemble embedding (WNPEE). Unlike NPE, the proposed WNPEE constructs an ensemble of adjacent graphs with the number of nearest neighbors varying. With this graph ensemble building, WNPEE can obtain the low-dimensional projections with optimal embedded graph pursuing in a joint optimization manner. WNPEE can be applied in many machine learning fields, such as object recognition, data classification, signal processing, text categorization, and various deep learning tasks. Extensive experiments on Olivetti Research Laboratory (ORL), Georgia Tech, Carnegie Mellon University-Pose and Illumination Images (CMU PIE) and Yale, four face databases demonstrate that WNPEE achieves a competitive and better recognition rate than NPE and other comparative DR methods. Additionally, the proposed WNPEE achieves much lower sensitivity to the neighborhood size parameter as compared to the traditional NPE method while preserving more of the local manifold structure of the high-dimensional data.