Role In Many Applications Research Articles

Direct laser-written aperiodic photonic volume elements for complex light shaping with high efficiency: inverse design and fabrication

Light plays a central role in many applications. The key to unlocking its versatility lies in shaping it into the most appropriate form for the task at hand. Specifically tailored refractive index modifications, directly manufactured inside glass using a short pulsed laser, enable an almost arbitrary control of the light flow. However, the stringent requirements for quantitative knowledge of these modifications, as well as for fabrication precision, have so far prevented the fabrication of light-efficient aperiodic photonic volume elements (APVEs). Here, we present a powerful approach to the design and manufacturing of light-efficient APVEs. We optimize application-specific three-dimensional arrangements of hundreds of thousands of microscopic voxels and manufacture them using femtosecond direct laser writing inside millimeter-sized glass volumes. We experimentally achieve unprecedented diffraction efficiencies up to 80%, which is enabled by precise voxel characterization and adaptive optics during fabrication. We demonstrate APVEs with various functionalities, including a spatial mode converter and combined intensity shaping and wavelength multiplexing. Our elements can be freely designed and are efficient, compact, and robust. Our approach is not limited to borosilicate glass but is potentially extendable to other substrates, including birefringent and nonlinear materials, giving a preview of even broader functionalities, including polarization modulation and dynamic elements.

Millimeter-Wave Radar Monitoring for Elder’s Fall Based on Multi-View Parameter Fusion Estimation and Recognition

Human activity recognition plays a vital role in many applications, such as body falling surveillance and healthcare for elder’s in-home monitoring. Instead of using traditional micro-Doppler signals based on time-frequency distribution, we turn to another way and use the Relax algorithm to process the radar echo so as to obtain the required parameters. In this paper, we aim at the multi-view idea in which two radars at different views work synchronously and fuse the features extracted from each radar, respectively. Furthermore, we discuss the common estimated time-frequency features and time-varying spatial features of multi-view radar-echo and then formulate the parameters matrix via principal component analysis, and finally transform them into the machine learning classifiers to make further comparisons. Simulations and results show that our proposed multi-view parameter fusion idea could lead to relative-high accuracy and robust recognition performance, which would provide a feasible application for future human–computer monitoring scenarios.

Mannich Reaction: Review of Amine‐Functionalized Lignin Derivatives and Their Applications

AbstractIn recent years naturally resourced materials have played a significant role in many applications ranging from biomedical to automotive applications. Among them, lignin has received much attention towards these applications owing to its abundance and readily modified chemical structures. Recent research studies have focused on various chemical modifications of lignin, including the increasingly popular Mannich reaction that aminates the lignin structure to expand the applicability of lignin in advanced fields. This article reviews the recent Mannich reaction‐based lignin developments with different types of amines used in lignin modifications and discusses how the reaction enhances functionality in engineering, biomedical and environmental technology applications.

Silicon Radiation Detector Technologies: From Planar to 3D

Silicon radiation detectors, a special type of microelectronic sensor which plays a crucial role in many applications, are reviewed in this paper, focusing on fabrication aspects. After addressing the basic concepts and the main requirements, the evolution of detector technologies is discussed, which has been mainly driven by the ever-increasing demands for frontier scientific experiments.

A two-stage downscaling hydrological modeling approach via convolutional conditional neural process and geostatistical bias correction

Hydrological modeling plays an indispensable role in many practical applications, such as hydrology, water resources assessment, and environmental survey. Downscaling approach is able to obtain high-resolution hydrological models according to a prior reference model. In this work, we propose a downscaling hydrological modeling approach based on convolutional conditional neural processes (ConvCNP) and a novel geostatistical bias correction, named ConvCNP-GBC. A two-stage downscaling modeling workflow is presented. In the model building stage, to reduce the training consumption of the ConvCNP, low-resolution conditioning data are used for training. In the model generating stage, the ConvCNP is used to reconstruct coarse-scale realizations based on upscaling sampled conditioning data and to generate high-resolution realizations by downscaling. Besides, a geostatistical bias correction strategy is presented to refine large-scale realizations with high-resolution conditioning data constraints. The experimental results confirm that ConvCNP-GBC can reproduce heterogeneous patterns by using coarse-scale conditioning data and characterize hydrological structures. The downscaled realizations also have a high spatial correspondence with the coarse-scale realizations, which provide fine subsurface heterogeneous structures. The proposed method can be easily extended to reservoir modeling, geophysical inversion, etc.

CeO2–x Nanoparticles Heated to 900 °C Afford Nanomaterials without Thermally Induced Mismatches: Implications for Catalyst Carriers

Nowadays, CeO2–x (0 < x < 0.5) nanoparticles are used for a wide range of applications, most of them using high temperatures. Since the phase diagram of nanoparticles differs from that of the corresponding bulk material and plays a pivotal role in many applications, the phase transition of CeO2–x nanoparticles exposed to a CO atmosphere at 900 °C was probed by in situ XRD measurements. The results show the presence of the fluorite and Cu2Se-like phases at this temperature. During the cooling process to room temperature in the He atmosphere, the Cu2Se-like phase turns metastable and transforms back to the fluorite phase. The phase transition occurs in 1D, with the crystallization mechanism controlled by volume diffusion. The Cu2Se-like phase presents a negative thermal expansion coefficient (α) for the temperature range of 580–300 °C due to the O diffusion from the Cu2Se-like to the fluorite crystal structure. Consequently, tuning the amounts of the fluorite and the Cu2Se-like phase allows reaching a tunable thermal expansion coefficient of the CeO2–x nanoparticles. In particular, the condition for a null thermal expansion coefficient was successfully achieved, which allows the use of smart CeO2–x nanoparticles without inducing thermal mismatches for applications like catalyst carriers.

MIMO radar design with joint constant-modulus and any p-norm similarity constraints

MIMO radar plays a key role in many applications, e.g., autonomous driving, smart parking, and gesture recognition. The similarity waveform constraint is an important constraint for radar waveform design. However, the joint constant-modulus and similarity constraints pose a big challenge in MIMO radar design. Only the special case with ∞-norm similarity and constant-modulus constraints is tackled by the semidefinite relaxation (SDR) and the successive quadratic refinement (SQR) methods. In this paper, the joint constant-modulus and any p-norm (1≤p≤∞) similarity constraints are tackled by the proposed relax-and-retract algorithm. In particular, the nonconvex constant-modulus constraint is first relaxed to a convex constraint, and then the retract operation is guaranteed to recover a constant-modulus solution within a fixed iteration number. Extensive simulation results show that full range similarity control and constant-modulus constraints are satisfied under different p-norms. For the special case with 1-norm, it is firstly found to be a constant-modulus-inducing norm. For the special case with ∞-norm, the proposed relax-and-retract method has less computational complexity than the SDR and SQR approaches.

Noisy stabilizer formalism

Despite the exponential overhead to describe general multiqubit quantum states and processes, efficient methods for certain state families and operations have been developed and utilized. The stabilizer formalism and the Gottesman-Knill theorem, where pure stabilizer or graph states are manipulated by Clifford operations and Pauli measurements, are prominent examples, and these states play a major role in many applications in quantum technologies. Here we develop a noisy stabilizer formalism, i.e., a method that allows one to efficiently describe and follow not only pure states under Clifford operations and Pauli measurements but also Pauli noise processes acting on such stabilizer states, including uncorrelated and correlated dephasing and single- or multiple-qubit depolarizing noise. The method scales linearly in the number of qubits of the initial state, but exponentially in the size of the target state. Thus, whenever a noisy stabilizer state is manipulated by means of local Pauli measurements such that a multipartite entangled state of a few qubits is generated, one can efficiently describe the resulting state.

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.

An efficient algorithm for approximate Voronoi diagram construction on triangulated surfaces

Voronoi diagrams on triangulated surfaces based on the geodesic metric play a key role in many applications of computer graphics. Previous methods of constructing such Voronoi diagrams generally depended on having an exact geodesic metric. However, exact geodesic computation is time-consuming and has high memory usage, limiting wider application of geodesic Voronoi diagrams (GVDs). In order to overcome this issue, instead of using exact methods, we reformulate a graph method based on Steiner point insertion, as an effective way to obtain geodesic distances. Further, since a bisector comprises hyperbolic and line segments, we utilize Apollonius diagrams to encode complicated structures, enabling Voronoi diagrams to encode a medial-axis surface for a dense set of boundary samples. Based on these strategies, we present an approximation algorithm for efficient Voronoi diagram construction on triangulated surfaces. We also suggest a measure for evaluating similarity of our results to the exact GVD. Although our GVD results are constructed using approximate geodesic distances, we can get GVD results similar to exact results by inserting Steiner points on triangle edges. Experimental results on many 3D models indicate the improved speed and memory requirements compared to previous leading methods.

Water Pumping System Supplied by a PV Generator and with a Switched Reluctance Motor Using a Drive Based on a Multilevel Converter with Reduced Switches

Pumping systems play a fundamental role in many applications. One of the applications in which these systems are very important is to pump water. However, in the real world context, the use of renewable energies to supply this kind of system becomes essential. Thus, this paper proposes a water pumping system powered by a photovoltaic (PV) generator. In addition, due to its interesting characteristics, such low manufacturing cost, free of rare-earth elements, simple design and robustness for pumping systems, a switched reluctance motor (SRM) is used. The power electronic system to be used in the PV generator and to control the SRM consists of a DC/DC converter with a bipolar output and a multilevel converter. The adopted DC/DC converter uses only one switch, so its topology can be considered as a derivation of the combination of a Zeta converter with a buck–boost converter. Another important aspect is that this converter allows continuous input current, which is desirable for PV panels. The topology selected to control the SRM is a multilevel converter. This proposed topology was adopted with the purpose of reducing the number of power semiconductors. A maximum power point algorithm (MPPT) associated with the DC/DC converter to obtain the maximum power of the PV panels is also proposed. This MPPT will be developed based on the concept of the time derivative of the power and voltage. It will be verified that with the increase in solar irradiance, the generated power will also increase. From this particular case study, it will be verified that changes in the irradiance from 1000 W/m2 to 400 W/m2 will correspond to a change in the motor speed from 1220 rpm to 170 rpm. The characteristics and operation of the proposed system will be verified through several simulation and experimental studies.

MASS: A Multisource Domain Adaptation Network for Cross-Subject Touch Gesture Recognition

Touch gesture recognition (TGR) plays a pivotal role in many applications, such as socially assistive robots and embodied telecommunication. However, one obstacle to practicality of existing TGR methods is the individual disparities across subjects. Moreover, a deep neural network trained with multiple existing subjects can easily lead to overfitting for a new subject. Hence, how to mitigate the discrepancies between the new and existing subjects and establish a generalized network for TGR is a significant task to realize reliable human–robot tactile interaction. In this article, a novel framework for Multisource domain Adaptation via Shared-Specific feature projection (MASS) is proposed, which incorporates intradomain discriminant, multidomain discriminant, and cross-domain consistency into a deep learning network for cross-subject TGR. Specifically, the MASS method first extracts the shared features in the common feature space of training subjects, with which a domain-general classifier is built. Then, the specific features of each pair of training and testing subjects are mapped and aligned in their common feature space, and multiple domain-specific classifiers are trained with the specific features. Finally, the domain-general classifier and domain-specific classifiers are ensembled to predict the label for the touch samples of a new subject. Experimental results performed on two datasets show that our proposed MASS method achieves remarkable results for cross-subject TGR. The code of MASS is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/AI-touch/MASS</uri> .

HBN-based regulation of near-field radiative heat transfer between planar structures

Nanoscale thermal regulation plays a vital role in many applications, especially in highly-integrated electronics. In this study, we theoretically analyze the near-field radiation heat transfer within hBN-based planar systems, including bare hBN plates, SiC/hBN plates, graphene/hBN plates and Au/hBN plates. We predict that tailoring the shift frequency of phonon could alter the hyperbolic modes (HMs) of hBN, thus allowing for the coupling and decoupling of HMs with SPhPs of SiC and GSPPs of graphene. Therefore, the radiative heat flux in hBN-based systems except for Au/hBN plates can be greatly regulated, and the regulation ratio can reach 159.6 for bare hBN plates, and above 2 for SiC/hBN plates and for graphene/hBN plates. Results may facilitate understanding of the principles of hBN-based evanescent wave coupling and nanoscale thermal management.

HRRNet: Hierarchical Refinement Residual Network for Semantic Segmentation of Remote Sensing Images

Semantic segmentation of high-resolution remote sensing images plays an important role in many practical applications, including precision agriculture and natural disaster assessment. With the emergence of a large number of studies on convolutional neural networks, the performance of the semantic segmentation model of remote sensing images has been dramatically promoted. However, many deep convolutional network models do not fully refine the segmentation result maps, and, in addition, the contextual dependencies of the semantic feature map have not been adequately exploited. This article proposes a hierarchical refinement residual network (HRRNet) to address these issues. The HRRNet mainly consists of ResNet50 as the backbone, attention blocks, and decoders. The attention block consists of a channel attention module (CAM) and a pooling residual attention module (PRAM) and residual structures. Specifically, the feature map output by the four blocks of Resnet50 is passed through the attention block to fully explore the contextual dependencies of the position and channel of the semantic feature map, and, then, the feature maps of each branch are fused step by step to realize the refinement of the feature maps, thereby improving the segmentation performance of the proposed HRRNet. Experiments show that the proposed HRRNet improves segmentation result maps compared with various state-of-the-art networks on Vaihingen and Potsdam datasets.

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.

Leaky Partial Update LMS Algorithms in Application to Structural Active Noise Control.

Adaptive signal processing algorithms play an important role in many practical applications in diverse fields, such as telecommunication, radar, sonar, multimedia, biomedical engineering and noise control. Recently, a group of adaptive filtering algorithms called partial update adaptive algorithms (partial updates) has gathered considerable attention in both research and practical applications. This paper is a study of the application of PUs to very demanding, structural active noise control (ANC) systems, which are of particular interest due to their ability to provide for a global noise reduction. However, such systems are multichannel, with very high computational power requirements, which may be reduced by the application of partial updates. The paper discusses the modifications necessary to apply PUs in structural ANC systems and the potential computational power savings offered by this application. As a result, leaky versions of the PU LMS algorithms are introduced to the general public. The paper also presents two simulation examples, based on real laboratory setups, confirming high performance of the proposed algorithms.

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.

Improving detectability of auditory interfaces for medical alarms through temporal variation in amplitude envelope

BackgroundAuditory interfaces play a vital role in many applications, informing users about both urgent and routine information critical to safety. Unfortunately, problems related to high alarm rates, low reliability, and sound annoyance create barriers to optimising the quality of patient care in perioperative medicine and critical care. Here, we explore how to reduce annoyance and improve detection by manipulating a sound's temporal envelope or the way its energy changes over time. MethodsIn the first experiment, participants were asked to detect a series of percussive and flat tones presented at six signal-to-noise ratios while performing a concurrent speech comprehension task. In the second experiment, different participants were asked to evaluate the relative annoyance of these same sounds. ResultsRelative to industry-standard flat tones, percussive tones were significantly less annoying and more detectable. Although more detectable, percussive tones did not impair concurrent speech comprehension. ConclusionsTemporal variation in amplitude envelope represents a promising path towards improving auditory interfaces for patient monitoring. By using temporally variable sounds, auditory interfaces can be more effective in alerting users. This is important for safety-critical areas, such as medical alarms, where annoyance often limits efficacy. As this manipulation can preserve the pitch and rhythm of tone sequences, it is compatible with users' pre-existing knowledge of current alarms.

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.