Important Role In Many Applications Research Articles

Dual-polarized angle-selective surface based on double-layer frequency selective surface

In this Letter, we propose a dual-polarized angle-selective surface (ASS) based on a double-layer frequency selective surface (FSS) to achieve angular selectivity. By appropriately constructing the structure of the single-layer FSS, bandpass and band-stop are produced by the ASS under normal and oblique incidences, respectively, resulting in exceptional angular selectivity. Simulated results show that the proposed low-pass ASS achieves a bandpass (|S21| > −1 dB) from 0° to 10° and a band-stop (|S21| < −20 dB) from 31° to 85° with a profile thickness of 0.6 λ. A low-pass ASS prototype with an operating frequency of 10 GHz is fabricated to demonstrate the design concept. The measured results of the transmission coefficients are in good agreement with the simulated results. The proposed ASS can play an important role in many applications, as in minimizing the side lobes of the antenna.

A Nonlinear Local Approximation Approach for Catchment Classification.

Catchment classification plays an important role in many applications associated with water resources and environment. In recent years, several studies have applied the concepts of nonlinear dynamics and chaos for catchment classification, mainly using dimensionality measures. The present study explores prediction as a measure for catchment classification, through application of a nonlinear local approximation prediction method. The method uses the concept of phase-space reconstruction of a time series to represent the underlying system dynamics and identifies nearest neighbors in the phase space for system evolution and prediction. The prediction accuracy measures, as well as the optimum values of the parameters involved in the method (e.g., phase space or embedding dimension, number of neighbors), are used for classification. For implementation, the method is applied to daily streamflow data from 218 catchments in Australia, and predictions are made for different embedding dimensions and number of neighbors. The prediction results suggest that phase-space reconstruction using streamflow alone can provide good predictions. The results also indicate that better predictions are achieved for lower embedding dimensions and smaller numbers of neighbors, suggesting possible low dimensionality of the streamflow dynamics. The classification results based on prediction accuracy are found to be useful for identification of regions/stations with higher predictability, which has important implications for interpolation or extrapolation of streamflow data.

Compactness Property of the Linearized Boltzmann Collision Operator for a Mixture of Monatomic and Polyatomic Species

The linearized Boltzmann collision operator has a central role in many important applications of the Boltzmann equation. Recently some important classical properties of the linearized collision operator for monatomic single species were extended to multicomponent monatomic gases and polyatomic single species. For multicomponent polyatomic gases, the case where the polyatomicity is modelled by a discrete internal energy variable was considered lately. Here we consider the corresponding case for a continuous internal energy variable. Compactness results, stating that the linearized operator can be decomposed into a sum of a positive multiplication operator, the collision frequency, and a compact operator, bringing e.g., self-adjointness, is extended from the classical result for monatomic single species, under reasonable assumptions on the collision kernel. With a probabilistic formulation of the collision operator as a starting point, the compactness property is shown by a decomposition, such that the terms are, or at least are uniform limits of, Hilbert–Schmidt integral operators and therefore are compact operators. Moreover, bounds on—including coercivity of—the collision frequency are obtained for hard sphere like, as well as hard potentials with cutoff like, models, from which Fredholmness of the linearized collision operator follows, as well as its domain.

Criteria for what makes a local optional martingale a true martingale

What makes an optional stochastic exponential a true optional martingale in a probability space where the underlying filtration is not right continuous nor complete. In this paper, we are going to answer this fundamental question. True martingales play an important role in many applications, just consider the large field of the fair pricing of derivative contracts. Therefore, it is important to give a natural, simple and verifiable condition for having a true martingale property in the context of the theory of optional martingales. The way we have done this here is by extending the method of Beneš to local optional martingales, giving a list of conditions for when an optional stochastic exponential of a local optional martingale is a true optional martingale.

Pedestrian Detection Based on Feature Enhancement in Complex Scenes

Pedestrian detection has always been a difficult and hot spot in computer vision research. At the same time, pedestrian detection technology plays an important role in many applications, such as intelligent transportation and security monitoring. In complex scenes, pedestrian detection often faces some challenges, such as low detection accuracy and misdetection due to small target sizes and scale variations. To solve these problems, this paper proposes a pedestrian detection network PT-YOLO based on the YOLOv5. The pedestrian detection network PT-YOLO consists of the YOLOv5 network, the squeeze-and-excitation module (SE), the weighted bi-directional feature pyramid module (BiFPN), the coordinate convolution (coordconv) module and the wise intersection over union loss function (WIoU). The SE module in the backbone allows it to focus on the important features of pedestrians and improves accuracy. The weighted BiFPN module enhances the fusion of multi-scale pedestrian features and information transfer, which can improve fusion efficiency. The prediction head design uses the WIoU loss function to reduce the regression error. The coordconv module allows the network to better perceive the location information in the feature map. The experimental results show that the pedestrian detection network PT-YOLO is more accurate compared with other target detection methods in pedestrian detection and can effectively accomplish the task of pedestrian detection in complex scenes.

LogicDT: a procedure for identifying response-associated interactions between binary predictors

Interactions between predictors play an important role in many applications. Popular and successful tree-based supervised learning methods such as random forests or logic regression can incorporate interactions associated with the considered outcome without specifying which variables might interact. Nonetheless, these algorithms suffer from certain drawbacks such as limited interpretability of model predictions and difficulties with negligible marginal effects in the case of random forests or not being able to incorporate interactions with continuous variables, being restricted to additive structures between Boolean terms, and not directly considering conjunctions that reveal the interactions in the case of logic regression. We, therefore, propose a novel method called logic decision trees (logicDT) that is specifically tailored to binary input data and helps to overcome the drawbacks of existing methods. The main idea consists of considering sets of Boolean conjunctions, using these terms as input variables for decision trees, and searching for the best performing model. logicDT is also accompanied by a framework for estimating the importance of identified terms, i.e., input variables and interactions between input variables. This new method is compared to other popular statistical learning algorithms in simulations and real data applications. As these evaluations show, logicDT is able to yield high prediction performances while maintaining interpretability.

PERFORMANCE ASSESSMENT OF OBJECT DETECTION FROM MULTI SATELLITES AND AERIAL IMAGES

Abstract. Object detection in remote sensing imagery plays an important role in many applications, such as tracking and change detection. With the development of deep learning algorithms and advancement in hardware systems, improved accuracies have been achieved in the detection of various objects from remote sensing images. However, object detection across heterogeneous remote sensing imagery remains an important issue, particularly for satellite and aerial imagery. The colour variation for the same ground objects, variable resolutions, different platform heights, the parallax effect, and image distortion brought on by diverse shooting angles are the biggest hurdles in satellite-aerial detection applications. The research aims to obtain successful model for detecting aircrafts from satellite and aerial images and reduce cost and the gap of revisit time between sensors. The networks were tested using aerial, GF-2, Jilin-1 (JL-1) and Pleiades satellites test sets after being trained individually using the RGB high-resolution aerial set and panchromatic low-resolution GF-2 satellite set to validate the efficiency of the trained models. Also, the aerial-trained model and GF-2 satellite-trained model as dedicated models were compared with each other, and model trained by all dataset for Object Detection in Aerial Images (DOTA). It is observed that the anchor sizes and augmentation methods can enhance the performance of detection models. k-means algorithm and data augmentation were applied to produce better anchor box selection and avoid overfitting, atmospheric conditions problems, respectively. The accuracy assessment results demonstrate that the aerial-trained model outperforms the GF-2 satellite-trained model. In addition, the results of two dedicated detection models show improved accuracy compared to the DOTA-trained model.

Design of low polarization off-axis three-mirror reflective optical systems.

Off-axis optical systems have several important advantages over on-axis ones. However, high polarization aberrations, which play important roles in many applications, become critical disadvantages of off-axis systems. Thanks to the seven free design parameters, three-mirror reflective systems have a good potential to achieve low polarization. A general method to design low polarization off-axis three-mirror reflective optical systems is proposed in this paper. Based on genetic algorithms, several off-axis three-mirror systems with both low polarization aberrations and good wave aberrations are designed. The method proposed in this paper is versatile and can be used to design other types of optical systems that demand low polarization aberrations.

Twisted Optical Micro/Nanofibers Enabled Detection of Subtle Temperature Variation.

The detection of subtle temperature variation plays an important role in many applications, including proximity sensing in robotics, temperature measurements in microfluidics, and tumor monitoring in healthcare. Herein, a flexible miniaturized optical temperature sensor is fabricated by embedding twisted micro/nanofibers in a thin layer of polydimethylsiloxane. Enabled by the dramatic change of the coupling ratio under subtle temperature variation, the sensor exhibits an ultrahigh sensitivity (-30 nm/°C) and high resolution (0.0012 °C). As a proof-of-concept demonstration, a robotic arm equipped with our sensor can avoid undesired collisions by detecting the subtle temperature variation caused by the existence of a human. Moreover, benefiting from the miniaturized and engineerable sensing structure, real-time measurement of subtle temperature variation in microfluidic chips is realized. These initial results pave the way toward a category of optical sensing devices ranging from robotic skin to human-machine interfaces and implantable healthcare sensors.

3D target detection and spectral classification for single-photon LiDAR data.

3D single-photon LiDAR imaging has an important role in many applications. However, full deployment of this modality will require the analysis of low signal to noise ratio target returns and very high volume of data. This is particularly evident when imaging through obscurants or in high ambient background light conditions. This paper proposes a multiscale approach for 3D surface detection from the photon timing histogram to permit a significant reduction in data volume. The resulting surfaces are background-free and can be used to infer depth and reflectivity information about the target. We demonstrate this by proposing a hierarchical Bayesian model for 3D reconstruction and spectral classification of multispectral single-photon LiDAR data. The reconstruction method promotes spatial correlation between point-cloud estimates and uses a coordinate gradient descent algorithm for parameter estimation. Results on simulated and real data show the benefits of the proposed target detection and reconstruction approaches when compared to state-of-the-art processing algorithms.

Online continual learning for human activity recognition

Sensor-based human activity recognition (HAR), with the ability to recognise human activities from wearable or embedded sensors, has been playing an important role in many applications including personal health monitoring, smart home, and manufacturing. The real-world, long-term deployment of these HAR systems drives a critical research question: how to evolve the HAR model automatically over time to accommodate changes in an environment or activity patterns. This paper presents an online continual learning (OCL) scenario for HAR, where sensor data arrives in a streaming manner which contains unlabelled samples from already learnt activities or new activities. We propose a technique, OCL-HAR, making a real-time prediction on the streaming sensor data while at the same time discovering and learning new activities. We have empirically evaluated OCL-HAR on four third-party, publicly available HAR datasets. Our results have shown that this OCL scenario is challenging to state-of-the-art continual learning techniques that have significantly underperformed. Our technique OCL-HAR has consistently outperformed them in all experiment setups, leading up to 0.17 and 0.23 improvements in micro and macro F1 scores.

A Q-Learning-Based Routing Approach for Energy Efficient Information Transmission in Wireless Sensor Network

Nowadays, wireless sensor networks have played an important role in many applications. In these applications, a large number of wireless sensors are deployed in an environment to collect information and form network to transmit collected information to the base station or sink. Because wireless sensors have to work for a long period of time without maintenance, the energy consumption of wireless sensors has a great impact on the lifetime of wireless sensor network. To reduce and balance the energy consumption of wireless sensors, many information transmission routing approaches have been proposed. However, most of them do not consider all energy consumption factors of wireless sensors. To this end, an innovative information transmission routing approach based on Q-learning is proposed in this paper, which enables wireless sensors to adaptively select suitable neighboring sensors to achieve energy efficient information transmission in a decentralized manner. Based on the proposed approach, a wireless sensor first collects state and action information of its neighboring sensors, which includes information transmission direction and distance, the remaining energy, the energy consumption for information transmission and information transmission action. Then, all this information is used to update the Q-values of neighboring sensors, so as to enable the wireless sensor to select suitable neighboring sensor to transmit information according to Q-values. From simulation experiments, it can be seen that the proposed approach enables wireless sensors to reduce and balance the energy consumption of wireless sensors and extend the lifetime of the entire wireless sensor network.

Realization of a source-device-independent quantum random number generator secured by nonlocal dispersion cancellation

Quantum random number generators (QRNGs) can provide genuine randomness by exploiting the intrinsic probabilistic nature of quantum mechanics, which play important roles in many applications. However, the true randomness acquisition could be subjected to attacks from untrusted devices involved or their deviations from the theoretical modeling in real-life implementation. We propose and experimentally demonstrate a source-device-independent QRNG, which enables one to access true random bits with an untrusted source device. The random bits are generated by measuring the arrival time of either photon of the time–energy entangled photon pairs produced from spontaneous parametric downconversion, where the entanglement is testified through the observation of nonlocal dispersion cancellation. In experiment, we extract a generation rate of 4 Mbps by a modified entropic uncertainty relation, which can be improved to gigabits per second by using advanced single-photon detectors. Our approach provides a promising candidate for QRNGs with no characterization or error-prone source devices in practice.

Robust noise hybrid active contour model for infrared image segmentation using orientation column filters

Infrared (IR) image segmentation plays an important role in many applications of night vision, including pedestrian detection, security monitoring, etc. However, the precision is constrained by edge blur and noise interference from the original infrared imaging. In order to achieve robust segmentation results under noise interference, a hybrid active contour model for the segmentation of targets in images using local feature information and global information is proposed. Based on the concept of orientation columns in the primary visual cortex, orientation column filters are defined, which can effectively extract local feature information with noise robustness. Then a global term with noise robustness is defined, and the adaptive weight matrix is adopted to combine the two to construct a complete signed pressure force (SPF) function. Several experiments demonstrate that the proposed algorithm performs more accurately and robustly on noisy infrared images segmentation compared to typical algorithms.

Efficient Broadband Light-Trapping Structures on Thin-Film Silicon Fabricated by Laser, Chemical and Hybrid Chemical/Laser Treatments

Light-trapping structures formed on surfaces of various materials have attracted much attention in recent years due to their important role in many applications of science and technology. This article discusses various methods for manufacturing light-trapping "black" silicon, namely laser, chemical and hybrid chemical/laser ones. In addition to the widely explored laser texturing and chemical etching methods, we develop a hybrid chemical/laser texturing method, consisting in laser post-texturing of pyramidal structures obtained after chemical etching. After laser treatments the surface morphology was represented by a chaotic relief of microcones, while after chemical treatment it acquired a chaotic pyramidal relief. Moreover, laser texturing of preliminarily chemically microtextured silicon wafers is shown to take five-fold less time compared to bare flat silicon. In this case, the chemically/laser-treated samples exhibit average total reflectance in the spectral range of 250-1100 nm lower by 7-10% than after the purely chemical treatment.

High-sensitivity Goos–Hänchen shift gas sensor based on subwavelength hyperbolic metamaterials

Optical gas sensors play an increasingly important role in many applications, particularly for the detection of toxic gases. A novel Goos–Hänchen (GH) shift optical gas sensing scheme based on subwavelength hyperbolic metamaterials (HMMs) is proposed. The GH shift intensity, direction and the critical wavelength characteristics were revealed. By virtue of the GH shift and subwavelength HMMs characteristics, we designed an ultra-sensitive gas sensor to detect helium (He), hydrogen (H2), carbon monoxide (CO) and methane (CH4). The study shows that the sensitivities of the gas sensor can reach as high as [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]. With proper surface chemical modification, this GH shift gas sensor would be a powerful tool for high-sensitive gas sensing applications.

How Does Electronic Polarizability or Scaled-Charge Affect the Interfacial Properties of Room Temperature Ionic Liquids?

The room temperature ionic liquid (RTIL) air-liquid interface plays an important role in many applications. Herein, we present molecular dynamics simulation results for the air-liquid interface of a common RTIL, 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl) imide, [C4mim][NTf2]. To elucidate the effects of electronic polarizability and scaled-charge ions on the properties of the RTIL air-liquid interface, we employ three different kinds of force fields: a nonpolarizable force field (FF) with united ion charges (FixQ), a nonpolarizable FF with scaled-charge by 0.8 (ScaleQ), and a polarizable FF (Drude). To identify whether the ions reside at the interface or not, the method of identification of the truly interfacial molecules is used. The structural and dynamical properties in the interfacial, subinterfacial, and central layers are evaluated. In general for bulk liquids, the FixQ model predicts too-ordered structures and too-sluggish dynamics, while the ScaleQ model can serve as a simple cure. However, the ScaleQ model cannot reproduce the results of the Drude model at the interface, due to an inappropriate scaled-down charge near the interface.

Characterizing excited states of single donor-acceptor molecule by high-resolution Raman images

The excited states of charge transfer (CT), local donor (LD) and local acceptor (LA) for donor-acceptor molecules play an important role in many applications. However, these excited quantum states are still impossible to be characterized by conventional experimental techniques. Optical spectroscopy with the supplement of density functional theory calculations is widely adopted to investigate excited states, but is often inefficient for the donor-acceptor molecule owing to substantial errors of functionals in describing CT states. Our calculations show that the resonance tip-enhanced Raman scattering images that utilizes the confined plasmonic field for CT, LD and LA states possess their exclusive imaging characterizations, giving unambiguous identification of different excited-state properties. In addition, the resonance Raman images provide a benchmark to reasonably estimate the accuracy of different functionals in describing excited states of the charge-transfer system. These findings extend the emerging Raman images to the characterization and improvement of functional donor-acceptor molecular materials.

Spectral comparison of nanoporous silica-adsorbed organic molecules with gaseous and liquid states using a new waveguide technology

Nanoporous materials play an important role in many applications including separations and sensing, therefore understanding surface interactions, molecular adsorption, and nanoconfinement phenomena are important. Here we use near-infrared spectroscopy to measure spectra of volatile organic compounds (VOCs) adsorbed inside a nanoporous silica waveguide and compare to the spectral features of the VOCs’ gas and liquid phase spectra. The 10 mm long nanoporous waveguide has a transparent optical window between 1200 and 2200 nm that allows sensitive near-infrared measurements of a range of polar and non-polar VOCs. For the non-polar compounds, the spectra of the molecules adsorbed in the nanopores have similar spectral features to those of the gas molecules in free space. However, for the polar compounds, there are more prominent spectral similarities between the adsorbed molecules and their corresponding liquid spectra. Interestingly, we also observed that the 10 mm path length porous waveguide was capable of sensing gas concentration at least one order of magnitude lower compared to a 400 mm path length conventional gas-cell. The novel optical waveguide allows for sensitive measurement in the near-infrared, demonstrating a strong potential to be used as a tool to study adsorption in nanoporous materials and as a gas sensor.

An AlN Piezoelectric Micromachined Ultrasonic Transducer-Based Liquid Density Sensor

As an important physical parameter of liquids, the liquid density plays an important role in many applications. As a result, the development of liquid density sensors is critical. However, current liquid density sensors have drawbacks, such as large size of conventional liquid density sensors, low quality factor of microcantilever beam structures, low sensitivity of quartz crystal tuning forks, and high operating frequency requirement of surface-acoustic-wave Love-mode resonators. In this article, we report an aluminum nitride (AlN) piezoelectric micromachined ultrasonic transducer (PMUT)-based liquid density sensor to mitigate the aforementioned drawbacks of traditional liquid density sensors. The PMUT sensor utilizes CMOS-compatible AlN as the piezoelectric material. This sensor operates by measuring the change in resonant frequency of the PMUT to calculate the liquid density. In an experiment, the resonance of the PMUT sensor is measured by a digital holographic microscope (DHM), which allows direct measurement of the PMUT surface displacement, providing intuitive vibration amplitude and mode. Moreover, the DHM-based optical measurement approach has a higher sensitivity than the conventional electrical impedance-based measurement approach, allowing measurement of weak PMUT vibrations in dense liquids. The quality factor ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> ) value of the PMUT sensor is measured to be 49.38 in water. A high sensitivity of 12.71 kHz/( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{g}\cdot $ </tex-math></inline-formula> cm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{-{3}}$ </tex-math></inline-formula> ) was determined for this sensor by measuring the resonant frequencies immersed in different glycerol aqueous solutions. We measured 12 PMUTs and the maximum deviation of resonant frequency between different PMUTs is 9.85%. For the first time, the resolution of the PMUT density sensor is determined to be <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.9\times 10^{-{4}}\,\,\text{g}\cdot $ </tex-math></inline-formula> cm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{-{3}}$ </tex-math></inline-formula> .