Two-dimensional phase unwrapping (2-D PU) is one of the key processes in reconstructing the topography or displacement of the Earth surface from its interferometric synthetic aperture radar (InSAR) ...data. Estimating the absolute phase gradient information is an unavoidable step utilized by almost all the 2-D PU methods. Traditionally, the gradient estimation step relies on the phase continuity assumption, which requests that the observed area has spatial continuity. However, the abrupt topographic changes and system noise usually results in the failure of the phase continuity assumption in reality. Under this condition, it is difficult for the traditional 2-D PU to provide the correct absolute phase over the area with abrupt interferometric fringe change or with strong system noise. To solve the issue, we propose a novel deep convolutional neural network (DCNN), abbreviated as PGNet, to estimate the phase gradient information instead of the phase continuity assumption in this article. The major advantage of PGNet lies in its deep architecture to learn the characteristics of phase gradients from enormous training images with different noise levels and topographic features. Subsequently, the <inline-formula> <tex-math notation="LaTeX">L^{1} </tex-math></inline-formula>-norm objective function is used to minimize the difference between unwrapped phase gradients and the gradients estimated by PGNet for obtaining the final PU result. Taking the phase gradient pattern of the TerraSAR-X-TanDEM-X interferogram as the learning object, experimental results demonstrate the absolute phase gradient estimated by PGNet is more credible than that from the phase continuity assumption such that the corresponding PU result outperforms those obtained by the traditional 2-D PU methods.
Multibaseline 2-D phase unwrapping (PU) is a critical step for the multibaseline synthetic aperture radar interferometry. Compared with the single-baseline PU, the multibaseline PU does not need to ...obey the phase continuity assumption, i.e., it is applicable to the terrain with the violent change. However, the performance of the multibaseline PU is directly related to noise level. In order to improve the noise robustness of the multibaseline PU, in this paper, we transplant the framework of the single-baseline PU into the multibaseline PU and propose a two-stage programming approach, referred to as TSPA, which makes use of the gradient information of the interferogram similar to how the conventional single-baseline PU method does. Fortunately, although the proposed method belongs to the integer programming (usually, the integer programming is an NP-hard problem which is hard to solve), the constraint of the optimization model of the TSPA method is unimodular, so it can be efficiently solved. Furthermore, interestingly, some useful and important concepts of the single-baseline PU, for example, residue and branch cut, are also transplanted into the multibaseline PU in this paper, and we discuss the potential of extending most of the representative single-baseline PU methods into the multibaseline domain as well. Finally, the experiment results show the effectiveness and noise robustness of the TSPA multibaseline PU method.
Temperature is one of the most fundamental physical properties to characterize various physical, chemical, and biological processes. Even a slight change in temperature could have an impact on the ...status or dynamics of a system. Thus, there is a great need for high-precision and large-dynamic-range temperature measurements. Conventional temperature sensors encounter difficulties in high-precision thermal sensing on the submicron scale. Recently, optical whispering-gallery mode (WGM) sensors have shown promise for many sensing applications, such as thermal sensing, magnetic detection, and biosensing. However, despite their superior sensitivity, the conventional sensing method for WGM resonators relies on tracking the changes in a single mode, which limits the dynamic range constrained by the laser source that has to be fine-tuned in a timely manner to follow the selected mode during the measurement. Moreover, we cannot derive the actual temperature from the spectrum directly but rather derive a relative temperature change. Here, we demonstrate an optical WGM barcode technique involving simultaneous monitoring of the patterns of multiple modes that can provide a direct temperature readout from the spectrum. The measurement relies on the patterns of multiple modes in the WGM spectrum instead of the changes of a particular mode. It can provide us with more information than the single-mode spectrum, such as the precise measurement of actual temperatures. Leveraging the high sensitivity of WGMs and eliminating the need to monitor particular modes, this work lays the foundation for developing a high-performance temperature sensor with not only superior sensitivity but also a broad dynamic range.
Two-dimensional (2-D) phase unwrapping (PU) is a critical processing step for many synthetic aperture radar (SAR) interferometry (InSAR) applications. As is well known, the traditional 2-D PU is an ...ill-posed inverse problem, which means that regardless of how skillful the PU algorithm designer is, it is impossible to design an algorithm that can correctly process all the 2-D PU situations, i.e., we can only design the best PU algorithm in the statistical sense. Therefore, accumulating PU processing experience from different study cases is important for PU algorithm design. Currently, the deep learning (DL) technique provides a potential framework to accumulate processing experience, and a flood of valuable data coming from different InSAR sensors provides the ability to enable the learning-based PU technique outside the traditional model-based technique. In this article, we transform the 2-D PU problem into a learnable image semantic segmentation problem and propose a DL-based branch-cut deployment method (abbreviated as BCNet). To start, we propose the optimal branch-cut connection criterion (referred to as OPT-BC) with the reference unwrapped phase given. Next, using the relationship between the residue and branch-cut as the learning objective, BCNet is trained using the samples provided by OPT-BC to produce the branch-cut result. Finally, the traditional branch-cut method is utilized to perform the postprocessing procedure to obtain the final PU result. The experimental results demonstrate that the proposed BCNet-based PU method is a near-real-time 2-D PU algorithm, and its accuracy outperforms the traditional model- and learning-based 2-D PU methods.
Optical whispering-gallery-mode microresonators with ultrahigh quality factors and small mode volumes have played an important role in modern physics. They have been demonstrated as a diverse ...platform for a wide range of applications in photonics, such as nonlinear optics, optomechanics, quantum optics, and information processing. Thermal behaviors induced by power build-up in the resonators or environmental perturbations are ubiquitous in high-quality-factor whispering-gallery-mode resonators and have played an important role in their operation for various applications. In this review, we discuss the mechanisms of laser-field-induced thermal nonlinear effects, including thermal bistability and thermal oscillation. With the help of the thermal bistability effect, optothermal spectroscopy and optical nonreciprocity have been demonstrated. By tuning the temperature of the environment, the resonant mode frequency will shift, which can also be used for thermal sensing/tuning applications. The thermal locking technique and thermal imaging mechanisms are discussed briefly. Finally, we review some techniques employed to achieve thermal stability in a high-quality-factor resonator system.
This study develops a power‐average‐operator‐based hybrid multiattribute online product recommendation model that considers the consumer's risk attitude to rank categoric product options as a ...complement to existing recommender systems. Online production recommendation plays a key role in the development of e‐commerce, and can greatly improve consumers' shopping experiences. However, few online shopping sites provide interactive decision aids for consumers such that they can articulate their preferences towards multiple selection attributes with the purpose of mitigating choice difficulty and improving decision quality. Additionally, consumers' risk attitudes to online shopping dramatically impact their product choices. In the model proposed in this paper, the risk attitude‐based power average (RAPA) operator is used to integrate the risk attitude of the decision‐maker into the information fusion process of multiple attribute decision‐making. Subsequently, the risk attitude function, with several basic types, is introduced to quantify the risk attitude of the decision‐maker for use in the RAPA operator. A proportional hesitant fuzzy 2‐tuple linguistic term set (PHF2TLTS) is constructed by incorporating a binary of linguistic information aiming to comprehensively analyze the hybrid product information. With a focus on the information fusion process, the proportional hesitant 2‐tuple linguistic RAPA operator and weighted proportional hesitant 2‐tuple linguistic RAPA operator are introduced to aggregate a given set of PHF2TLTSs. The validity of the proposed model is demonstrated using an illustrative example, a comparison with existing approaches and detailed explanations of the performance differences.
Through the incorporation of various halogen‐substituted chiral organic cations, the effects of chiral molecules on the chiroptical properties of hybrid organic–inorganic perovskites (HOIPs) are ...investigated. Among them, the HOIP having a Cl‐substituted chiral cation exhibits the highest circular dichroism (CD) and circular polarized luminescence (CPL) intensities, indicating the existence of the largest rotatory strength, whereas the F‐substituted HIOP shows the weakest intensities. The observed modulation can be correlated to the varied magnetic transition dipole of HOIPs, which is sensitive to the d‐spacing between inorganic layers and the halogen–halogen interaction between organic cations and the inorganic sheets. These counteracting effects meet the optimal CD and CPL intensity with chlorine substitution, rendering the rotatory strength of HOIPs arranged in the order of (ClMBA)2PbI4>(BrMBA)2PbI4>(IMBA)2PbI4>(MBA)2PbI4>(FMBA)2PbI4.
Through the incorporation of Cl‐substituted chiral organic cations, the chiroptical properties of 2D chiral perovskites can be significantly enhanced. The observed circular dichroism and circular polarized luminescence intensities are found to be associated with the d‐spacing of hybrid organic–inorganic perovskites and the strength of the halogen–halogen interaction within the system.
Engineering conventional hydrogels with muscle‐like anisotropic structures can efficiently increase the fatigue threshold over 1000 J m−2 along the alignment direction; however, the fatigue threshold ...perpendicular to the alignment is still as low as ≈100–300 J m−2, making them nonsuitable for those scenarios where isotropic properties are desired. Here, inspired by the distinct structure–properties relationship of heart valves, a simple yet general strategy to engineer conventional hydrogels with unprecedented yet isotropic fatigue resistance, with a record‐high fatigue threshold over 1,500 J m−2 along two arbitrary in‐plane directions is reported. The two‐step process involves the formation of preferentially aligned lamellar micro/nanostructures through a bidirectional freeze‐casting process, followed by compression annealing, synergistically contributing to extraordinary resistance to fatigue crack propagation. The study provides a viable means of fabricating soft materials with isotropically extreme properties, thereby unlocking paths to apply these advanced soft materials toward applications including soft robotics, flexible electronics, e‐skins, and tissue patches.
2D fatigue‐resistant hydrogels are fabricated through synergistically engineering the 2D lamellar microstructures and nanocrystalline domains, involving the formation of preferentially aligned lamellar micro/nanostructures through the bidirectional freeze‐casting process, followed by compression annealing. Their application as load‐bearing components in a jellyfish‐inspired underwater robot is investigated.
Flexible, stretchable, and sensitive multidirectional sensing systems that can decouple different mechanical inputs and identify multidirectional signals are crucial for dynamic human signal ...perception and intelligent human–computer interaction. Most reported multidirectional sensors are suitable for discriminating in‐plane deformation directions, and the sensing materials are difficult to balance between stretchability and mechanical strength. Here, a segmented embedded structure strategy inspired by the interlaced structure of cartilage is proposed. This strategy combines soft and hard materials in a topological and zipper‐shear chain manner and balances the performance of reinforced composites with flexibility and high toughness. In the case of segmented embedded hydrogels (SEHs), a wearable multidirectional sensing system that can decouple and identify planar strain/pressure is constructed. The multidirectional sensing system exploits the inherent anisotropy and layered structure design of composites to decouple the sensing functions. Supported by machine learning algorithms, the high accuracy demonstration of the multidirectional sensors in typical multidirectional motion joint posture monitoring and recognition confirms their potential in practical applications such as personal health sensing and human–computer interaction.
Inspired by the mechanism of the natural biomaterial cartilage, the segmented embedded strategy, which combines soft and hard materials in a topological and zipper‐shear chain manner, is designed to enhance the performance of polymers, and based on this strategy, wearable multidirectional sensing devices are constructed.
The maternal-to-zygotic transition (MZT) is a conserved and fundamental process during which the maternal environment is converted to an environment of embryonic-driven development through dramatic ...reprogramming. However, how maternally supplied transcripts are dynamically regulated during MZT remains largely unknown. Herein, through genome-wide profiling of RNA 5-methylcytosine (m5C) modification in zebrafish early embryos, we found that m5C-modified maternal mRNAs display higher stability than non-m5C-modified mRNAs during MZT. We discovered that Y-box binding protein 1 (Ybx1) preferentially recognizes m5C-modified mRNAs through π-π interactions with a key residue, Trp45, in Ybx1’s cold shock domain (CSD), which plays essential roles in maternal mRNA stability and early embryogenesis of zebrafish. Together with the mRNA stabilizer Pabpc1a, Ybx1 promotes the stability of its target mRNAs in an m5C-dependent manner. Our study demonstrates an unexpected mechanism of RNA m5C-regulated maternal mRNA stabilization during zebrafish MZT, highlighting the critical role of m5C mRNA modification in early development.
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•RNA-BisSeq revealed a dynamic RNA m5C landscape during zebrafish embryogenesis•Ybx1 preferentially recognizes m5C-modified mRNAs•Ybx1 deficiency leads to early gastrulation defects in zebrafish embryos•Ybx1 and Pabpc1a coordinately regulate m5C-modified maternal mRNA stability
RNA modifications exert important effects in many critical physiological processes. Using RNA-BisSeq, Yang et al. provide a comprehensive view of the RNA m5C landscape in zebrafish early embryos and show that m5C-modified maternal mRNAs are stabilized by Ybx1 and Pabpc1a during zebrafish MZT.
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