STNet: A Time-Frequency Analysis-Based Intrusion Detection Network for Distributed Optical Fiber Acoustic Sensing Systems.

Distributed optical fiber acoustic sensing (DAS) is promising for long-distance intrusion-anomaly detection tasks. However, realistic settings suffer from high-intensity interference noise, compromising the detection performance of DAS systems. To address this issue, we propose STNet, an intrusion detection network based on the Stockwell transform (S-transform), for DAS systems, considering the advantages of the S-transform in terms of noise resistance and ability to detect disturbances. Specifically, the signal detected by a DAS system is divided into space-time data matrices using a sliding window. Subsequently, the S-transform extracts the time-frequency features channel by channel. The extracted features are combined into a multi-channel time-frequency feature matrix and presented to STNet. Finally, a non-maximum suppression algorithm (NMS), suitable for locating intrusions, is used for the post-processing of the detection results. To evaluate the effectiveness of the proposed method, experiments were conducted using a realistic high-speed railway environment with high-intensity noise. The experimental results validated the satisfactory performance of the proposed method. Thus, the proposed method offers an effective solution for achieving high intrusion detection rates and low false alarm rates in complex environments.

Label-Free Anomaly Detection Using Distributed Optical Fiber Acoustic Sensing.

Deep learning anomaly detection is important in distributed optical fiber acoustic sensing (DAS). However, anomaly detection is more challenging than traditional learning tasks, due to the scarcity of true-positive data and the vast imbalance and irregularity within datasets. Furthermore, it is impossible to catalog all types of anomalies, therefore, the direct application of supervised learning is deficient. To overcome these problems, an unsupervised deep learning method that only learns the normal data features from ordinary events is proposed. First, a convolutional autoencoder is used to extract DAS signal features. A clustering algorithm then locates the feature center of the normal data, and the distance to the new signal is used to determine whether it is an anomaly. The efficacy of the proposed method was evaluated in a real high-speed rail intrusion scenario, and considered all behaviors that may threaten the normal operation of high-speed trains as abnormal. The results show that the threat detection rate of this method reaches 91.5%, which is 5.9% higher than that of the state-of-the-art supervised network and, at 7.2%, the false alarm rate is 0.8% lower than the supervised network. Moreover, using a shallow autoencoder reduces the parameters to 1.34 K, which is significantly lower than the 79.55 K of the state-of-the-art supervised network.

Plasmon-Assisted Trapping of Single Molecules in Nanogap.

The manipulation of single molecules has attracted extensive attention because of their promising applications in chemical, biological, medical, and materials sciences. Optical trapping of single molecules at room temperature, a critical approach to manipulating the single molecule, still faces great challenges due to the Brownian motions of molecules, weak optical gradient forces of laser, and limited characterization approaches. Here, we put forward localized surface plasmon (LSP)-assisted trapping of single molecules by utilizing scanning tunneling microscope break junction (STM-BJ) techniques, which could provide adjustable plasmonic nanogap and characterize the formation of molecular junction due to plasmonic trapping. We find that the plasmon-assisted trapping of single molecules in the nanogap, revealed by the conductance measurement, strongly depends on the molecular length and the experimental environments, i.e., plasmon could obviously promote the trapping of longer alkane-based molecules but is almost incapable of acting on shorter molecules in solutions. In contrast, the plasmon-assisted trapping of molecules can be ignored when the molecules are self-assembled (SAM) on a substrate independent of the molecular length.

In Situ Adjustable Nanogaps and In-Plane Break Junctions.

The ability to precisely regulate the size of a nanogap is essential for establishing high-yield molecular junctions, and it is crucial for the control of optical signals in extreme optics. Although remarkable strategies for the fabrication of nanogaps are proposed, wafer-compatible nanogaps with freely adjustable gap sizes are not yet available. Herein, two approaches for constructing in situ adjustable metal gaps are proposed which allow Ångstrom modulation resolution by employing either a lateral expandable piezoelectric sheet or a stretchable membrane. These in situ adjustable nanogaps are further developed into in-plane molecular break junctions, in which the gaps can be repeatedly closed and opened thousands of times with self-assembled molecules. The conductance of the single 1,4-benzenediamine (BDA) and the BDA molecular dimer is successfully determined using the proposed strategy. The measured conductance agreeing well with the data by employing another well-established scanning tunneling microscopy break junction technique provides insight into the formation of molecule dimer via hydrogen bond at single molecule level. The wafer-compatible nanogaps and in-plane dynamical break-junctions provide a potential approach to fabricate highly compacted devices using a single molecule as a building block and supply a promising in-plane technique to address the dynamical properties of single molecules.

The spatial changes of China's environmental efficiency and driving factors from the perspective of circular economy.

This paper mainly uses the super-efficiency EBM model to measure the environmental efficiency of 30 provinces in China from 2005 to 2019, utilizes the Theil index to analyze the degree of differentiation, and investigate the stochastic convergence of environmental efficiency in different regions. At the same time, it focuses on exploring the direction, intensity, and changing trend of the internal driving factors of environmental efficiency including fiscal decentralization and tax competition, so as to measure and show the overall situation of circular economy development. The research results show that (1) from a national perspective, environmental efficiency shows a pattern of gradual convergence from east to west and from coast to inland. There is a significant stepped regional imbalance in the development level of circular economy in the eastern coastal areas and the central, western inland areas. (2) The differences in environmental efficiency among the four major economic regions were apparently significant while the differences inside each region itself were relatively minor, although in a trend of being gradually widened. There are differences in the development level of circular economy in different regions or within the same region. (3) The inter-provincial efficiency in the eastern, western and northeastern zones maintained relatively stable, while the inter-provincial differences in the central region were expanding. The environmental deficit problem caused by economic development has been alleviated and the basic development model of circular economy has been initially established. (4) Economic development has played a positive role in improving the environmental efficiency of the region. But the resident consumption level inhibited the improvement of the environmental efficiency level of the surrounding areas. The conclusion of this paper can provide a macroscopic reference for the government in finding effective countermeasures to improve environmental efficiency.

Scale-Adaptive Deep Model for Bacterial Raman Spectra Identification.

The combination of Raman spectroscopy and deep learning technology provides an automatic, rapid, and accurate scheme for the clinical diagnosis of pathogenic bacteria. However, the accuracy of existing deep learning methods is still limited because of the single and fixed scales of deep neural networks. We propose a deep neural network that can learn multi-scale features of Raman spectra by using the automatic combination of multi-receptive fields of convolutional layers. This model is based on the expert knowledge that the discrimination information of Raman spectra is composed of multi-scale spectral peaks. We enhance the interpretability of the model by visualizing the activated wavenumbers of the bacterial spectrum that can be used for reference in related work. Compared with existing state-of-the-art methods, the proposed method achieves higher accuracy and efficiency for bacterial identification on isolate-level, empiric-treatment-level, and antibiotic-resistance-level tasks. The clinical bacterial identification task requires significantly fewer patient samples to achieve similar accuracy. Therefore, this method has tremendous potential for the identification of clinical pathogenic bacteria, antibiotic susceptibility testing, and prescription guidance.

Plasmonic phenomena in molecular junctions: principles and applications.

Molecular junctions are building blocks for constructing future nanoelectronic devices that enable the investigation of a broad range of electronic transport properties within nanoscale regions. Crossing both the nanoscopic and mesoscopic length scales, plasmonics lies at the intersection of the macroscopic photonics and nanoelectronics, owing to their capability of confining light to dimensions far below the diffraction limit. Research activities on plasmonic phenomena in molecular electronics started around 2010, and feedback between plasmons and molecular junctions has increased over the past years. These efforts can provide new insights into the near-field interaction and the corresponding tunability in properties, as well as resultant plasmon-based molecular devices. This Review presents the latest advancements of plasmonic resonances in molecular junctions and details the progress in plasmon excitation and plasmon coupling. We also highlight emerging experimental approaches to unravel the mechanisms behind the various types of light-matter interactions at molecular length scales, where quantum effects come into play. Finally, we discuss the potential of these plasmonic-electronic hybrid systems across various future applications, including sensing, photocatalysis, molecular trapping and active control of molecular switches.

Crystal Size Effect on Carrier Transport of Microscale Perovskite Junctions via Soft Contact.

To reduce the size of optoelectronic devices, it is essential to understand the crystal size effect on the carrier transport through microscale materials. Here, we show a soft contact method to probe the properties of irregularly shaped microscale perovskite crystals by employing a movable liquid metal electrode to form a self-adaptative deformable electrode-perovskite-electrode junction. Accordingly, we demonstrate that (1) the photocurrents of perovskite quantum dot films and microplatelets show profound differences regarding both the on/off ratio and the response time upon light illumination; and (2) small-size perovskite (<50 µm) junctions may show negative differential resistance (NDR) behavior, whereas the NDR phenomenon is absent in large-size perovskite junctions within the same bias regime. Our studies provide a method for studying arbitrary-shaped crystals without mechanical damage, assisting the understanding of the photogenerated carriers transport through microscale crystals.

Fiber distributed acoustic sensing using convolutional long short-term memory network: a field test on high-speed railway intrusion detection.

This paper presents a novel and general distributed acoustic sensing (DAS) signal recognition framework aimed at real-time detection and classification of intrusion in the space-time domain. The framework is based on the combination of a convolution neural network (CNN) and a long short-term memory network (LSTM). The convolutional structure extracts the spatial features from multi-channel signals of the DAS system, while the LSTM network analyzes the temporal relationships over time. The framework can be deployed on high-speed railways for real-time intrusion threat detection, which is one of the most urgent and challenging problems that needs to be resolved as there is an increasing demand for high detection and low false alarm rates, and short response time. The alarm sensitivity and specificity of the framework are controlled by user-set parameters. A real field experiment is conducted in a strong background noise scenario and an intrusion threat detection rate of 85.6%, with only 8.0% false alarm rate is achieved. For threat classification, the average threat detection rate is 69.3%, and the average false alarm rate is 13.2%. Owing to the high detection accuracy of the framework, the average detection response time is shortened to 8.25 s.

Atomic switches of metallic point contacts by plasmonic heating.

Electronic switches with nanoscale dimensions satisfy an urgent demand for further device miniaturization. A recent heavily investigated approach for nanoswitches is the use of molecular junctions that employ photochromic molecules that toggle between two distinct isoforms. In contrast to the reports on this approach, we demonstrate that the conductance switch behavior can be realized with only a bare metallic contact without any molecules under light illumination. We demonstrate that the conductance of bare metallic quantum contacts can be reversibly switched over eight orders of magnitude, which substantially exceeds the performance of molecular switches. After the switch process, the gap size between two electrodes can be precisely adjusted with subangstrom accuracy by controlling the light intensity or polarization. Supported by simulations, we reveal a more general and straightforward mechanism for nanoswitching behavior, i.e., atomic switches can be realized by the expansion of nanoelectrodes due to plasmonic heating.

Shaping the Atomic-Scale Geometries of Electrodes to Control Optical and Electrical Performance of Molecular Devices.

A straightforward method to generate both atomic-scale sharp and atomic-scale planar electrodes is reported. The atomic-scale sharp electrodes are generated by precisely stretching a suspended nanowire, while the atomic-scale planar electrodes are obtained via mechanically controllable interelectrodes compression followed by a thermal-driven atom migration process. Notably, the gap size between the electrodes can be precisely controlled at subangstrom accuracy with this method. These two types of electrodes are subsequently employed to investigate the properties of single molecular junctions. It is found, for the first time, that the conductance of the amine-linked molecular junctions can be enhanced ≈50% as the atomic-scale sharp electrodes are used. However, the atomic-scale planar electrodes show great advantages to enhance the sensitivity of Raman scattering upon the variation of nanogap size. The underlying mechanisms for these two interesting observations are clarified with the help of density functional theory calculation and finite-element method simulation. These findings not only provide a strategy to control the electron transport through the molecule junction, but also pave a way to modulate the optical response as well as to improve the stability of single molecular devices via the rational design of electrodes geometries.

An on-chip hybrid plasmonic light steering concentrator with ∼96% coupling efficiency.

We, for the first time, propose and theoretically study a plasmonic light steering concentrator (PLSC) that is based on a hybrid photonic-plasmonic sandwich structure. In this device, a transverse electric (TE) polarization guided mode supported by a silicon-on-insulator (SOI) waveguide is vertically coupled to a metal-dielectric-metal sandwich structure, while the structure steers the light to a perpendicular metal taper and focuses the light on the apex of the taper with a small radius of 15 nm. Based on the coupled-mode theory, the two supermodes (quasi-TM modes) are clarified to illustrate the coupling mechanism of the device. We numerically obtain over 96% coupling efficiency at the 1500 nm telecommunication wavelength, and the mode width supported by the apex is limited laterally within the range of ∼110 nm, where the field enhancement calculated is found to be more than 107 compared to that of light in the silicon waveguide.