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In terms of the structural health inspection of storage tanks by ultrasonic guided wave technology, many scholars are currently focusing on the tanks’ floor and walls, while little research has been conducted on storage tank roofs. However, the roof of a storage tank is prone to corrosion because of its complex structure and unique working environment. For this purpose, this paper proposes a reflection/transmission signal amplitude ratio (RTAR) coefficient method for corrosion depth assessment. We studied the relationship between the RTAR coefficient, the corrosion depth, and the guided wave frequency to establish a depth assessment model. More importantly, unlike the traditional reflection coefficient method, the characteristics of guided wave signals, including the propagation and attenuation, are introduced in this model for accurate assessment. To eliminate the interference of residual vibration and improve the detection accuracy of defects, we built a corrosion detection system by using piezoelectric sensors and carried out field tests to verify the performance of the proposed method. We demonstrate that corrosion defects with a minimum depth of 0.2 mm can be quantitatively evaluated.

Polarization is one of the basic properties of electromagnetic waves conveying valuable information in signal transmission and sensitive measurements. Conventional methods for advanced polarization control impose demanding requirements on material properties and attain only limited performance. We demonstrated ultrathin, broadband, and highly efficient metamaterial-based terahertz polarization converters that are capable of rotating a linear polarization state into its orthogonal one. On the basis of these results, we created metamaterial structures capable of realizing near-perfect anomalous refraction. Our work opens new opportunities for creating high-performance photonic devices and enables emergent metamaterial functionalities for applications in the technologically difficult terahertz-frequency regime.

The use of quantum-mechanically entangled light to illuminate objects can provide substantial enhancements over unentangled light for detecting and imaging those objects in the presence of high levels of noise and loss. Each signal sent out is entangled with an ancilla, which is retained. Detection takes place via an entangling measurement on the returning signal together with the ancilla. This paper shows that for photodetection, quantum illumination with m bits of entanglement can in principle increase the effective signal-to-noise ratio by a factor of 2m, an exponential improvement over unentangled illumination. The enhancement persists even when noise and loss are so great that no entanglement survives at the detector.

Imagers that use their own illumination can capture three-dimensional (3D) structure and reflectivity information. With photon-counting detectors, images can be acquired at extremely low photon fluxes. To suppress the Poisson noise inherent in low-flux operation, such imagers typically require hundreds of detected photons per pixel for accurate range and reflectivity determination. We introduce a low-flux imaging technique, called first-photon imaging, which is a computational imager that exploits spatial correlations found in real-world scenes and the physics of low-flux measurements. Our technique recovers 3D structure and reflectivity from the first detected photon at each pixel. We demonstrate simultaneous acquisition of sub-pulse duration range and 4-bit reflectivity information in the presence of high background noise. First-photon imaging may be of considerable value to both microscopy and remote sensing.

The seismic geological conditions in shallow-surface igneous rich areas are poor, resulting in complex original data wave fields and low signal-to-noise ratios, making seismic processing challenging. Although the research at home and abroad has made some progress over the years, the seismic imaging in this area is still poor, which affects the effect of seismic exploration. This paper analyzes the main factors influencing processing imaging, further examines the signal-to-noise ratio and static correction, and proposes targeted processing strategies and suggestions for data acquisition.

Biological communication by means of structural color has existed for at least 500 million years. Structural color is commonly observed in the animal kingdom, but has been little studied in plants. We present a striking example of multilayer-based strong iridescent coloration in plants, in the fruit of Pollia condensata . The color is caused by Bragg reflection of helicoidally stacked cellulose microfibrils that form multilayers in the cell walls of the epicarp. We demonstrate that animals and plants have convergently evolved multilayer-based photonic structures to generate colors using entirely distinct materials. The bright blue coloration of this fruit is more intense than that of any previously described biological material. Uniquely in nature, the reflected color differs from cell to cell, as the layer thicknesses in the multilayer stack vary, giving the fruit a striking pixelated or pointillist appearance. Because the multilayers form with both helicoidicities, optical characterization reveals that the reflected light from every epidermal cell is polarized circularly either to the left or to the right, a feature that has never previously been observed in a single tissue.

With the increasing amount of data in railway communication system, the conventional wireless high-frequency communication technology cannot meet the requirements of modern communication and needs to be improved. In order to meet the requirements of high-speed signal processing, a high-speed communication signal processing method based on visible light is developed and studied. This method combines the adaptive equalization algorithm with deep learning and is applied to railway communication signal processing. In this research, the wavelength division multiplexing (WDM) and orthogonal frequency division multiplexing (OFDM) techniques are used, and fuzzy C equalization algorithm is used to softly divide the received signals, reduce signal distortion and interference suppression. The experimental results showed that increasing the step size could reduce the equalization effect, while increasing the modulation parameter will increase the bit error rate. Through deep learning to achieve channel equalization, visible light communication could effectively mitigate multi-path transmission and reflection interference, thereby reducing the bit error rate to the level of 0.0001. Under various signal-to-noise ratios, the system using the channel compensation method achieved the lowest bit error rate. This outcome was achieved by implementing hybrid modulation scheme, including Wavelength division multiplexing (WDM) and direct current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) techniques. It has been proved that this method can effectively reduce the channel distortion when the receiver is moving. This study develops a dependable communication system, which enhances signal recovery, reduces interference, and improves the quality and transmission efficiency of railway communication. The system has practical application value in the field of railway communication signal processing.

Due to environmental degradation and external intrusions, trees may develop various defects that diminish their stability and survival rates, leading to significant losses. Ground penetrating radar (GPR), characterized by its rapid and non-destructive advantages, has been regarded as a potentially effective method for assessing tree health conditions. However, existing GPR-based tree imaging techniques are predominantly two-dimensional (2D), making it difficult to diagnose three-dimensional (3D) structural features of defects. Therefore, this paper introduces, for the first time, 3D reverse time migration (RTM) into trunk defect detection. Compared to 2D RTM, 3D RTM can simultaneously back-propagate in-line and cross-line signals, accurately locate reflection waves, and focus diffracted waves, reconstructing the true interfaces and defect positions within the tree trunk. Experimental results on irregular trunk models containing a cavity demonstrate the feasibility of 3D RTM in trunk defect detection, providing specific and comprehensive guidance for formulating tree protection and restoration measures.

Ultra-reliable and low-latency communication (URLLC) is central to fifth-generation (5G) communication systems, but the fundamentals of URLLC remain elusive. New immersive and high-stake control applications with stricter reliability, latency and scalability requirements are now also creating unprecedented challenges for URLLC. Here we examine the limitations of 5G URLLC and propose key research directions for the next generation of URLLC, which we term extreme ultra-reliable and low-latency communication (xURLLC). xURLLC is underpinned by three concepts: the leveraging of recent advances in machine learning for faster and more reliable data-driven predictions; complementing radiofrequency signal transmission with non-radiofrequency data and passive signal reflection to combat rare events at scale; emphasizing joint communication and control co-design, as opposed to the communication-centric approach of 5G URLLC. For each of these concepts, we consider the challenges and opportunities, and illustrate the effectiveness of the proposed solutions through selected use cases. This Perspective examines the limitations of ultra-reliable and low-latency communication (URLLC) used in fifth-generation (5G) communication systems and proposes key research directions for the next generation of URLLC, termed extreme ultra-reliable and low-latency communication.

Nature offers exciting examples for functional wetting properties based on superhydrophobicity, such as the self-cleaning surfaces on plant leaves and trapped air on immersed insect surfaces allowing underwater breathing. They inspire biomimetic approaches in science and technology. Superhydrophobicity relies on the Cassie wetting state where air is trapped within the surface topography. Pressure can trigger an irreversible transition from the Cassie state to the Wenzel state with no trapped air—this transition is usually detrimental for nonwetting functionality and is to be avoided. Here we present a new type of reversible, localized and instantaneous transition between two Cassie wetting states, enabled by two-level (dual-scale) topography of a superhydrophobic surface, that allows writing, erasing, rewriting and storing of optically displayed information in plastrons related to different length scales.