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Abstract Optical pulling provides a new degree of freedom in optical manipulation. It is generally believed that long-range optical pulling forces cannot be generated by the gradient of the incident field. Here, we theoretically propose and numerically demonstrate the realization of a long-range optical pulling force stemming from a self-induced gradient field in the manipulated object. In analogy to potential barriers in quantum tunnelling, we use a photonic band gap design in order to obtain the intensity gradients inside a manipulated object placed in a photonic crystal waveguide, thereby achieving a pulling force. Unlike the usual scattering-type optical pulling forces, the proposed gradient-field approach does not require precise elimination of the reflection from the manipulated objects. In particular, the Einstein-Laub formalism is applied to design this unconventional gradient force. The magnitude of the force can be enhanced by a factor of up to 50 at the optical resonance of the manipulated object in the waveguide, making it insensitive to absorption. The developed approach helps to break the limitation of scattering forces to obtain long-range optical pulling for manipulation and sorting of nanoparticles and other nano-objects. The developed principle of using the band gap to obtain a pulling force may also be applied to other types of waves, such as acoustic or water waves, which are important for numerous applications.

Abstract Due to its unbounded and orthogonal modes, the orbital angular momentum (OAM) is regarded as a key optical degree of freedom (DoF) for future information processing with ultra-high capacity and speed. Although the manipulation of OAM based on metasurfaces has brought about great achievements in various fields, such manipulation currently remains at single-DoF level, which means the multiplexed manipulation of OAM with other optical DoFs is still lacking, greatly hampering the application of OAM beams and advancement of metasurfaces. In order to overcome this challenge, we propose the idea of multiplexed coherent pixel (MCP) for metasurfaces. This approach enables the manipulation of arbitrary complex-amplitude under incident lights of both plane and OAM waves, on the basis of which we have realized the multiplexed DoF control of OAM and wavelength. As a result, the MCP method expands the types of incident lights which can be simultaneously responded by metasurfaces, enriches the information processing capability of metasurfaces, and creates applications of information encryption and OAM demultiplexer. Our findings not only provide means for the design of high-security and high-capacity metasurfaces, but also raise the control and application level of OAM, offering great potential for multifunctional nanophotonic devices in the future.

Abstract Integrated switches play a crucial role in the development of reconfigurable optical add-drop multiplexers (ROADMs) that have greater flexibility and compactness, ultimately leading to robust single-chip solutions. Despite decades of research on switches with various structures and platforms, achieving a balance between dense integration, low insertion loss (IL), and polarization-dependent loss (PDL) remains a significant challenge. In this paper, we propose and demonstrate a 32 × 4 optical switch using high-index doped silica glass (HDSG) for ROADM applications. This switch is designed to route any of the 32 inputs to the express ports or drop any channels from 32 inputs to the target 4 drop ports or add any of the 4 ports to any of the 32 express channels. The switch comprises 188 Mach-Zehnder Interferometer (MZI) type switch elements, 88 optical vias for the 44 optical bridges, and 618 waveguide-waveguide crossings with three-dimensional (3D) structures. At 1550 nm, the fiber-to-fiber loss for each express channel is below 2 dB, and across the C and L bands, below 3 dB. For each input channel to all 4 drop/add channels at 1550 nm, the loss is less than 3.5 dB and less than 5 dB across the C and L bands. The PDLs for all express and input channels to the 4 drop/add channels are below 0.3 dB over the C band, and the crosstalk is under −50 dB for both the C and L bands.

Abstract Meningeal lymphatic vessels (mLVs) play a pivotal role in regulating metabolic waste from cerebrospinal fluid (CSF). However, the current limitations in field of view and resolution of existing imaging techniques impede understanding the stereoscopic morphology and dynamic behavior of mLVs in vivo. Here, we utilized dual-contrast functional photoacoustic microscopy to achieve wide-field intravital imaging of the lymphatic system, including mLVs and glymphatic pathways. The stereoscopic photoacoustic microscopy based on opto-acoustic confocal features has a depth imaging capability of 3.75 mm, facilitating differentiation between mLVs on the meninges and glymphatic pathways within the brain parenchyma. Subsequently, using this imaging technique, we were able to visualize the dynamic drainage of mLVs and identify a peak drainage period occurring around 20–40 min after injection, along with determining the flow direction from CSF to lymph nodes. Inspiringly, in the Alzheimer’s disease (AD) mouse model, we observed that AD mice exhibit a ~ 70% reduction in drainage volume of mLVs compared to wild-type mice. With the development of AD, there is be continued decline in mLVs drainage volume. This finding clearly demonstrates that the AD mouse model has impaired CSF drainage. Our study opens up a horizon for understanding the brain’s drainage mechanism and dissecting mLVs-associated neurological disorders.

Abstract The non-Hermitian skin effect describes the concentration of an extensive number of eigenstates near the boundaries of certain dissipative systems. This phenomenon has raised a huge interest in different areas of physics, including photonics, deeply expanding our understanding of non-Hermitian systems and opening up new avenues in both fundamental and applied aspects of topological phenomena. The skin effect has been associated to a nontrivial point-gap spectral topology and has been experimentally demonstrated in a variety of synthetic matter systems, including photonic lattices. In most of physical models exhibiting the non-Hermitian skin effect full or partial wave coherence is generally assumed. Here we push the concept of skin effect into the fully incoherent regime and show that rather generally (but not universally) the non-Hermitian skin effect persists under dephasing dynamics. The results are illustrated by considering incoherent light dynamics in non-Hermitian photonic quantum walks.

Abstract Bloch oscillations (BOs), an important transport phenomenon, have been studied extensively in static systems but remain mysterious in Floquet systems. Here, by harnessing notions from photonic analogy, we propose a generalization of the existing BOs in photonic Floquet lattices, namely the “photonic Floquet–Bloch oscillations”, which refer to rescaled photonic Bloch oscillations with a period of extended least common multiple of the modulation period and the Bloch oscillation period. Next, we report the first visual observation of such photonic Floquet–Bloch oscillations (FBOs) by employing waveguide fluorescence microscopy. Most significantly, the FBOs surpass the existing BOs in Floquet systems and exhibit exotic properties on their own, including fractal spectrum and fractional Floquet tunneling. This new transport mechanism offers an intriguing method of wave manipulation that may contribute to rapidly developing fields in photonics, condensed matter physics, and quantum physics.

Abstract Optical sensors with in-cell logic and memory capabilities offer new horizons in realizing machine vision beyond von Neumann architectures and have been attempted with two-dimensional materials, memristive oxides, phase-changing materials etc. Noting the unparalleled performance of superconductors with both quantum-limited optical sensitivities and ultra-wide spectrum coverage, here we report a superconducting memlogic long-wave infrared sensor based on the bistability in hysteretic superconductor-normal phase transition. Driven cooperatively by electrical and optical pulses, the device offers deterministic in-sensor switching between resistive and superconducting (hence dissipationless) states with persistence > 105 s. This results in a resilient reconfigurable memlogic system applicable for, e.g., encrypted communications. Besides, a high infrared sensitivity at 12.2 μm is achieved through its in-situ metamaterial perfect absorber design. Our work opens the avenue to realize all-in-one superconducting memlogic sensors, surpassing biological retina capabilities in both sensitivity and wavelength, and presents a groundbreaking opportunity to integrate visional perception capabilities into superconductor-based intelligent quantum machines.

Possibility of fast, convenient and precise definition of wagons load mass allows enhancing transport safety and ensures assets accounting in railroad infrastructure. There are known three-dimensional solid models of railway track sector and approaches of simulation the deformation which emerge in rails by mechanical load effect transmitted through wagon wheels. In accordance with these approaches, emerging deformations are recomputed into wagons weight. The rail temperature influences on its mechanical properties and, consequently, on the deformation value. In this work, for the first time, a technique has been proposed that allows one to consider the deformation of the rail under the load influence, taking into account its temperature variance at different boundary conditions. According to the proposed approach, the wagon weight is defined by deformation values which are measured by strain gauges located on the rail web. The developed models include a rail wheel, ties and a rail fragment. The rail fragment corresponding to the railway track sector on which sensors are mounted geometrically replicates an existent rail type R50 and is situated on the ties fixed from bottom side. The wheel model complies with an existent solid-rolled wagon wheel type with tread diameter 920 mm, thereby correct contact patch retains in the model. According to the approach, finite-element mesh is generated on the developed solid models, connections between model fragments are established, and boundary and temperature conditions as well as acting forces are applied. Sequentially finite-element analysis is performed for all possible combination of wheel coordinate, load mass and temperature. For every case, deformation values are registered in four rail nodes corresponding to strain gauges placements. Comparison of finite-element analysis results for two developed solid models is carried out. The models differ by the way of the rail on the ties mounting and boundary condition setting on the end faces of the rail fragment, allowing to consider possibility of temperature stresses relaxation. In the model 1 the rail is connected with ties rigidly, in the model 2 the rail and the ties are connected by a contact allowing the rail motion along the tie with given friction coefficient. Besides that clamp bolts impact is imitated in the model 2. The approach is implemented within multiphysical simulation environment ANSYS for coupled three-dimensional problem using Static Structural and Steady-State Thermal modules. Simulation results showed that the deformation values determined by the temperature influence differ for the proposed models. Vertical deformations range of the rail fragment on which the strain gauges are fastened, at the mass 12,500 kg loaded on the wheel, is from –245 μm (bend down) to 15 μm (bend up) for the model 1 depending on the rail temperature (in the range from –20 °C to +50 °C) and from –225 μm to –100 μm for the model 2. This allows concluding that the model 2 reflects deformation process more correctly, and the temperature influence on the deformation is less relevant compared to mechanical load value. The proposed model in contrast to the known ones implies static weighing characterized by more accuracy, reliability and simplicity of use. In the future it is planned the executing of the more detail research of a model with two wheels and an axle for determining optimal simulation time and obtained results accuracy.

This work presents a new piecewise polynomial method of smooth analytic approximation for any dimension and variability of experimental data. Alternatives to this method are cubic and bicubic splines which have their advantages and disadvantages. There are many researches in the field of big data flexible approximation; however nothing similar was found to what is presented in the work, especially concerning multivariate dependencies. Experimental data frequently depend on many variables which for the purposes of compression, prediction, and transmission locally expressed by relatively simple analytic functions. It can be local polynomials, either on some intervals in onedimensional case or polygons — in two-dimensional cases. Presented in the work method of local functions smooth matching extends from the one-dimension piecewise polynomial approximation method to higher dimensions that has a variety of scientific and practical applications. Under this condition, it makes sense to store and transmit coefficients of local polynomials or other local functions rather than use raw data for those purposes, which frequently requires an unacceptably large amount of resources. In the method described, we use cellular subdivision of the area of interest, and define low-degree polynomials or other parametric functions on the cells. At the junctions between cells, there are overlapping transition zones where local functions match to each other. Their amount is defined by the index of the topological compact covering. As a result, the matching obtains a single double-differentiable analytic function on the entire compact. Defined in the work basic functions are second- and third-degree especial polynomials. The values of these functions smoothly transit from one to zero within a closed unit interval. Derivatives on the interval edges both are equal to zero. The matching is performed by the homotopy which maps a unit interval to the space of functions. Efficiency of the method is demonstrated for one-dimensional case by matching a set of approximating parabolas. We extend this method to the two-dimensional case by applying the known unit partitioning technique with topological maps coverage. The computational experiment demonstrates that even in this case local functions smoothly match making a double-differentiable function on the entire compact. First result is a development of a smooth matching method for experimental data approximation by local parametric functions on a large interval. Second result is development of a new method, based on a unit partitioning, for matching two-dimensional local functions making an approximation on the two-dimensional compact. Third result is a theoretical proof of the method extension from dimension of one and two to any dimension. Task of this study consisted in the development of a useful tool for efficient storage and transmission of experimental data.

The possibilities of simulation of viscous incompressible fluid flows with lattice Boltzmann method are considered. Unlike the traditional discretization approach based on the use of Navier–Stokes equations, the lattice Boltzmann method uses a mesoscopic model to simulate incompressible fluid flows. Macroscopic parameters of a fluid, such as density and velocity, are expressed through the moments of the discrete probability distribution function. Discretization of the lattice Boltzmann equation is carried out using schemes D2Q9 (two-dimensional case) and D3Q19 (three-dimensional case). To simulate collisions between pseudo-particles, the Bhatnaga r–Gross–Crooke approximation with one relaxation time is used. The specification of initial and boundary conditions (no penetration and no-slip conditions, outflow conditions, periodic conditions) is discussed. The patterns of formation and development of vortical flows in a square cavity and cubic cavities are computed. The results of calculations of flow characteristics in a square and cubic cavity at various Reynolds numbers are compared with data available in the literature and obtained based on the finite difference method and the finite volume method. The dependence of the numerical solution and location of critical points on faces of cubic cavity on the lattice size is studied. Computational time is compared with performance of fine difference and finite volume methods. The developed implementation of the lattice Boltzmann method is of interest for the transition to further modeling non-isothermal and high-speed compressible flows.

For continuous wavelet transformation, wavelets based on derivatives of the Gauss function are used, and for multiscale analysis, Daubechies wavelets are used. The development of algorithms for forward and inverse continuous wavelet transform in the frequency domain made it possible in this work to synthesize digital filters with a finite impulse response (FIR) different from existing methods. The quality of the synthesized filters was checked by decomposition and subsequent reconstruction of the signals. To do this, several filters were synthesized that completely cover the frequency range of the signal. Since wavelets are bandpass filters, the authors called the filters wavelets. The more precisely the reconstructed signal repeats the shape of the original signal, the better the wavelet constructed by one method or another. A comparison of the accuracy of signal reconstruction shows that the best conversion result is obtained by using synthesized wavelets. The impulse responses of the FIR filters are synthesized so that their frequency response are similar to the frequency responses of wavelets based on derivatives of the Gaussian function of a large order. The greater the filter order, the closer the frequency response is to a square-wave shape. Algorithms for forward and inverse wavelet transformation of a signal in the frequency domain using wavelets based on derivatives of the Gauss function are proposed. Profiling of the program shows that the time of the wavelet transforms using the fast Fourier transform is 15,000 times less than with the direct numerical integration for sampling the signal of 32,768 samples. These algorithms can be used for wavelets with a square-wave frequency response. At the same time, the numerical calculation time is halved. The accuracy of the reconstruction was compared for wavelets based on second-order derivatives, Daubechies wavelets, and wavelets with a square-wave frequency response. The reconstruction accuracy is highest for the latest wavelets. The use of the wavelet construction method is preferable since this method is relatively simple and it is easy to synthesize multiband filters with any form of frequency response. If, when synthesizing using existing methods, a short transition band can be obtained only for long impulse responses, while the transition band using the method of constructing wavelets is absent even for filters of very small orders. The paper presents the impulse responses of twoband, three-band digital filters and their frequency response. FIR digital filters with a square-wave frequency response have a higher delay-band attenuation coefficient compared to existing filters, do not have transition band, and can be used to process one-dimensional and two-dimensional signals.

The problem of missed release deadlines under conditions of systematic changes in market requirements for a software product has been studied. The analysis of the causes of disruptions at all stages of software product development is carried out and an approach is proposed aimed at finding a compromise between the quality and the implementation period of the product being developed in order to reduce the release time. The results of the presented method are analyzed using the example of development in a startup at the early stages of development and confirmed the possibility of reducing the time for updating software products by at least 15 %.

The censoring of training datasets is considered taking into account the specific implementation of the nearest neighbor method algorithms. The censoring process is associated with the use of a set of boundary objects of classes according to a given metric for the purpose of: searching and removing noise objects and analyzing the cluster structure of the training sample in relation to connectivity. Special conditions for removing noise objects and forming a precedent base for training algorithms are explored. Recognition of objects using such a database should provide higher accuracy with minimal computational resources relative to the original dataset. Necessary and sufficient conditions for selecting noise objects from a set of boundary ones have been developed. The necessary condition for a boundary object to belong to the noise set is specified in the form of a restriction (threshold) on the ratio of the distances to the nearest object from its class and its complement. The search for the minimum coverage of the training dataset with standards is carried out based on the analysis of the cluster structure. The standards are represented by sample objects. The structure of the connectivity relations of objects according to the hypersphere system is used to group them. The composition of the groups is formed from centers (dataset objects) for hyperspheres the intersection of which contains boundary objects. The value of the compactness measure is calculated as the average number of objects in the training dataset, excluding noise, pulled in by one standard of minimum coverage. An analysis is carried out of the connection between the generalizing ability of algorithms in machine learning and the value of the compactness measure. The presence of a connection is justified by a criterion (regularizer) for selecting the number and composition of a set of noise objects. Optimal regularization coefficients are defined as threshold values for removing noise objects. The relationship between the value of the training dataset compactness measure and the generalizing ability of recognition algorithms is shown. The connection was identified using the standards of minimum sample coverage from which the precedent base was formed. It was found that the recognition accuracy using the precedent base is higher than that using the original dataset. The minimum composition of the precedent base includes descriptions of standards and parameters of local metrics. When using data normalization procedures, additional parameters are required. Analysis of the values of the compactness measure is in demand to detect overfitting of algorithms associated with the dimension of the feature space. Recognition based on precedents minimizes the cost of computing resources using nearest neighbor algorithms. Recommendations are given for the development of models in the field of information security for processing and interpreting sociological research data. For use in information security, a precedent base is being formed to identify DDOS attacks. It is proposed to obtain new knowledge from the field of sociology through the analysis of the values of indicators of noise objects and the interpretation of the results of dividing respondents into non-overlapping groups in relation to the connectedness of objects. The configurations of groups in relation to connectivity are not initially known. There is no point in calculating their centers which can be located outside the configurations. To explain the contents of groups, it is proposed to use standards of minimum coverage.

The results of a current study of the perception of clinical decision support systems (CDSS) in the framework of preventive screening by dentists in schools of the Russian Ministry of Defense (cadet corps) are presented. Using the example of the scenario under consideration, a prototype of the CDSS based on machine learning was evaluated. To assess perception, a survey was conducted demonstrating the results of the prototype and assessing the perceived characteristics of the provided predictive modeling results. A model was built based on a Bayesian network to evaluate the considered indicators, which demonstrated an increase in the quality of prediction of perceived indicators, taking into account the influence of latent states of the operator’s subjective perception. The proposed approach is planned to be used in the future to increase the efficiency of doctor-CDSS interaction.

Optoelectronic wear monitoring system of rotor blades of steam turbine low-pressure cylinders provide an assessment of the chord value of the working blade in static conditions on a closed cylinder. However, these systems do not allow the operator to assess the wear with the necessary error during shaft rotation. The control process is complicated by the fact that the output edge of the blade is overlapped by the input edge of the next blade; therefore it is necessary to set a scanning direction for each section that will ensure the formation of blade video frames, including both the input and output edges. The shaft rotation mode requires the use of pulsed illumination of the edges of the working blades to reduce the amount of image smudge; therefore it is necessary to select the focal length of the camera lens, the diameter of the entrance pupil of the lens and the power of pulsed radiation sources. The development of a methodology for selecting system parameters will help to reduce the complexity of designing systems for various turbine models and application technologies. Therefore, this is an important task. A methodology has been developed for selecting the parameters of the wear control systems of the working blades, which is based on the criterion of equality of the main components of the total error of the chord value. The analytical studies used the relationship of the parameters of the matrix receiver of optical radiation, illumination sources and the optical circuit with the required characteristics of the system. Computer modeling of the information conversion process in the system under study took into account the relationship between the parameters of the moving blades and the parameters of the optical circuit. The experimental estimation of the system error in statics and dynamics is based on multiple measurements after calibration of the system according to known parameters of the blades. When using the developed methodology, it is possible to achieve the required field of view and a given error in controlling the chord value, due to the choice of: matrix optical radiation receiver, focal length of the camera lens, diameter of the lens entrance pupil, and power of radiation sources. Using the example of the fifth stage of the vane device of the K-1200 high unit power turbine, which is most susceptible to wear, it is shown that for maximum values of the rotation angles of the video probe is 19° and the delay time of frame synchronization is up to 0.18 s, the focal length of the camera lens should be less than 2.4 mm with a pulse illumination time of 0.05 s. Computer modeling has shown that the marginal error of the system can reach 0.011 mm, which illustrates the possibility of reducing the total error. Using the developed methodology, the main elements were selected and a layout of the system was created. The requirements for exposure time and delay time of frame synchronization are formulated. The effectiveness of the parameter selection methodology was confirmed by experimental studies of the system layout, which showed that the estimate of the standard deviation of the random component of the chord control error in dynamics was 0.26 mm, which is three times less than that of the previously developed system and meets the requirements for evaluating the operability of the rotor blades of steam turbines during operation and repair. The proposed technique can be used by developers of other optoelectronic means of contactless control of linear dimensions of parts oriented non-perpendicular to the line of sight.

The results of a study of methods for processing optoelectronic images of the Earth’s surface are presented. The application of fractal transformations to solve the problems of automated and automatic analysis of terrain images, ensuring the separation of natural and anthropogenic objects without the use of machine learning, is shown. The analysis of existing works has shown the absence of studies linking the result of fractal transformation with the image quality recorded in real conditions of optoelectronic photography. There is no justification for choosing a specific fractal transformation for the applied processing of images with certain typical distortions. The purpose of this work was to identify the dependence of the signal-to-noise ratio of fractal dimension on the quality of the source images, to determine the type of fractal transformation that is most resistant to the effects of the considered negative factors. Methods of fractal transformations for thematic image processing are defined, which include the prism method and the differential cube counting method, and their description is presented. To study the selected methods, real images of the Earth’s surface were used, simulating distorted images of the terrain. Image distortions determined by the instability of shooting conditions and the properties of the optoelectronic complex are considered: defocusing, smudging and noise. The mathematical models used to describe them are summarized. A technique for analyzing the signal-to-noise ratio of fractal transformation is described, involving the processing of reference and distorted images of the terrain. The aspects of distortion modeling and indicators characterizing the level of image distortion are indicated. To implement the experiment, images of the area were selected characterized by various plots. For each plot, the dependences of the signal-to-noise ratio on the indicators characterizing the studied distortions are obtained. By estimating the signal-tonoise ratio, the analysis of the influence of distorting factors on the fractal dimension field being formed was performed. The results of the experiment confirmed the possibility of using fractal transformations for thematic processing of distorted optoelectronic images. It is shown that the dependence of the signal-to-noise ratio on the distortion index has a pronounced nonlinear character. It is established that for distortions of the defocusing and smearing type, the prism method is more stable, and in the presence of noise, the differential cube method is more stable. For processing images of an area represented mainly by images of forest vegetation, the best result is shown by using the differential cube counting method.

Modern neural network technologies are actively used for Unmanned Aerial Vehicles (UAVs). Convolutional Neural Networks (CNN), are mostly used for object detection, classification, and tracking tasks, for example, for such objects as fires, deforestations, buildings, cars, or people. However, to improve effectiveness of CNNs it is necessary to perform their fine-tuning on new flight data periodically. Such training data should be labeled, which increases total CNN finetuning time. Nowadays, the common approach to decrease labeling time is to apply auto-labeling and labeled objects tracking. These approaches are not effective enough for labeling of 8 hours’ huge aerial sensed datasets that are common for long-endurance USVs. Thus, reducing data labeling time is an actual task nowadays. In this research, we propose a fast aerial data labeling pipeline especially for videos gathered by long-endurance UAVs cameras. The standard labeling pipeline was supplemented with several steps such as overlapped frames pruning, final labeling spreading over video frames. The other additional step is to calculate a Potential Information Value (PIV) for each frame as a cumulative estimation of frame anomality, frame quality, and auto-detected objects. Calculated PIVs are used than to sort out frames. As a result, an operator who labels video gets informative frames at the very beginning of the labeling process. The effectiveness of proposed approach was estimated on collected datasets of aerial sensed videos obtained by longendurance UAVs. It was shown that it is possible to decrease labeling time by 50 % in average in comparison with other modern labeling tools. The percentage of average number of labeled objects was 80 %, with them being labeled for 40 % of total pre-ranged frames. Proposed approach allows us to decrease labeling time for a new long-endurance flight video data significantly. This makes it possible to speed up neural network fine-tuning process. As a result, it became possible to label new data during the inter-flight time that usually takes about two or three hours and is too short for other labeling instruments. Proposed approach is recommended to decrease UAVs operators working time and labeled dataset creating time that could positively influence on the time necessary for the fine-tuning a new effective CNN models.

A suboptimal cross-border problem is considered about nonlinear dynamic controlled systems under deterministic, uniformly bounded external unknown disturbances. The problem is solved by applying the methods of classical variations calculus for the case when the time interval of adaptation and optimization is not set in advance. The necessary conditions for the choice of extreme motion are determined due to the proper formation of a closed suboptimal adaptive control system. The theoretical analysis is supplemented with computer calculations using a specific model example, which showed effectiveness of the considered approach. The proposed scheme of suboptimal adaptive synthesis can be used in the calculations and design of nonlinear controlled dynamic systems.

A dynamic system is considered where the regulating impact is the product of the control signal on the output variable of a linear dynamic system driven by the same applied control. The essence of the proposed method consists in the dynamic linearization of a nonlinear control operator, which makes it possible to guarantee a desired regulating impact. In a particular case, this approach corresponds to vector (field-oriented) control. It is shown that dynamic linearization based on the internal model method makes it possible to decompose a nonlinear system into a cascade of two subsystems. The proposed regulator consists of two blocks connected in series where the first block solves the problem of regulation with the Luenberger observer, and the second block compensates for a nonlinear dynamic operator. To demonstrate the effectiveness of the proposed approach, an example of numerical modeling of a neutrally stable plant with an output adaptive control is given. In practice, this method may be in demand in the tasks of controlling induction and synchronous motors and multi-link robotic manipulators.

Personalization is one of the keyways to improve the performance of conversational agents. It improves the quality of user interaction with a conversational agent and increases user satisfaction by increasing the consistency and specificity of responses. The dialogue with the agent becomes more consistent, the inconsistency of responses is reduced, and the responses become more specific and interesting. Training and testing personalized conversational agents requires specific datasets containing facts about a persona and texts of persona’s dialogues where replicas use those facts. There are several datasets in English and Chinese containing an average of five facts about a persona where the dialogues are composed by crowdsourcing users who repeatedly imitate different personas. This paper proposes a methodology for collecting an original dataset containing an extended set of facts about a persona and natural dialogues between personas. The new RuPersonaChat dataset is based on three different recording scenarios: an interview, a short conversation, and a long conversation. This is the first dataset for dialogue agent personalization collected which includes both natural dialogues and extended persona’s descriptions. Additionally, in the dataset, the persona’s replicas are annotated with the facts about the persona from which they are generated. The methodology for collecting an original corpus of test data proposed in this paper allows for testing language models for various tasks within the framework of personalized dialogue agent development. The collected dataset includes 139 dialogues and 2608 replicas. This dataset was used to test answer and question generation models and the best results were obtained using the Gpt3-large model (perplexity is equal to 15.7). The dataset can be used to test the personalized dialogue agents’ ability to talk about themselves to the interlocutor, to communicate with the interlocutor utilizing phatic speech and taking into account the extended context when communicating with the user.

The goal of this study is to predict a software error using Long Short-Term Memory (LSTM). The suggested system is an LSTM taught using the Whale Optimization Algorithm to save training time while improving deep learning model efficacy and detection rate. MATLAB 2022a was used to develop the enhanced LSTM model. The study relied on 19 open-source software defect databases. These faulty datasets were obtained from the tera-PROMISE data collection. However, in order to evaluate the model performance to other traditional approaches, the scope of this study is limited to five (5) of the most highly ranked benchmark datasets (DO1, DO2, DO3, DO4, and DO5). The experimental results reveal that the quality of the training and testing data has a significant impact on fault prediction accuracy. As a result, when we look at the DO1 to DO5 datasets, we can see that prediction accuracy is significantly dependent on training and testing data. Furthermore, for DO2 datasets, the three deep learning algorithms tested in this study had the highest accuracy. The proposed method, however, outperformed Li’s and Nevendra’s two classical Convolutional Neural Network algorithms which attained accuracy of 0.922 and 0.942 on the DO2 software defect data, respectively.

A method for stabilizing structural anomaly detection under additive noise conditions as well as an algorithm for formal selection of the parameters of the solver rule in the structural anomaly detector based on the Robust Random Cut Forest (RRCF) method are proposed. In the framework of the developed approach, in order to stabilize the process of structural anomaly detection under the influence of additive noise, it is proposed to feed to the input of the RRCF-detector a data stream which is pre-processed by one of the digital filtering methods. In this case, the decision rule for anomaly detection is strictly formalized and transparently interpreted. The selection of parameters of the RRCF-based anomaly detector stabilized by pre-filtering methods of the input data stream is formalized. The RRCF-detector parameters choice within the proposed scheme guarantees a predetermined upper bound for the false alarm probability when deciding to detect a structural anomaly. This property is rigorously proved and formalized as a theorem. The performance of the stabilized RRCF-detector is investigated numerically. The achieved results confirm the performance of the proposed approach provided that the detection threshold is selected in the way proposed in this paper. An example of practical application of the proposed method is presented. The developed approach is promising for the detection of structural anomalies in conditions of observation additive noise, in a situation where it is important to guarantee an upper bound for the probability of false alarm. In particular, the approach can find application in monitoring technological regimes of liquid pumping in pipeline systems or in systems for detecting pre-failure states of technological equipment.

The development of methods for automatic recognition of objects in a video stream, in particular, recognition of sign language, requires large amounts of video data for training. An established method of data enrichment for machine learning is distortion and noise. The difference between linguistic gestures and other gestures is that small changes in posture can radically change the meaning of a gesture. This imposes specific requirements for data variability. The novelty of the method lies in the fact that instead of distorting frames using affine image transformations, vectorization of the sign language speaker’s pose is used, followed by noise in the form of random deviations of skeletal elements. To implement controlled gesture variability using the MediaPipe library, we convert to a vector format where each vector corresponds to a skeletal element. After this, the image of the figure is restored from the vector representation. The advantage of this method is the possibility of controlled distortion of gestures, corresponding to real deviations in the postures of the sign language speaker. The developed method for enriching video data was tested on a set of 60 words of Indian Sign Language (common to all languages and dialects common in India), represented by 782 video fragments. For each word, the most representative gesture was selected and 100 variations were generated. The remaining, less representative gestures were used as test data. The resulting word-level classification and recognition model using the GRU-LSTM neural network has an accuracy above 95 %. The method tested in this way was transferred to a corpus of 4364 videos in Vietnamese Sign Language for all three regions of Northern, Central and Southern Vietnam. Generated 436,400 data samples, of which 100 data samples represent the meaning of words that can be used to develop and improve Vietnamese sign language recognition methods by generating many variations of gestures with varying degrees of deviation from the standards. The disadvantage of the proposed method is that the accuracy depends on the error of the MediaPipe library. The created video dataset can also be used for automatic sign language translation.

The dependence of request servicing delay on the number of deployed containers is investigated for computer systems with container virtualization. The sought-after dependency is due to the allocation of limited computational resources of the computer system between active and inactive containers loaded in the system. The conducted research proposes a comprehensive combination of analytical queuing model, simulation modeling, and natural experiments. The studied computer system is interpreted as a multi-channel queuing system with an unlimited queue. The peculiarity of the proposed approach is the study of the influence of the number of containers formed in the system on queue delays and request servicing rate. Each container is associated with a service channel, and for the operation of a container in active and inactive states, the use of part of the common resources of the computing system is required. When constructing the model, it is assumed that the input flow is simple, and the service is exponential. The service rate depends on the number of deployed containers and the number of requests in the system. The experimental dependence of service rate on the number of active containers has been established. The experimental study was carried out on a platform based on Proxmox virtualization technology with fixed resources. To study the influence of the number of active containers on service rate within the experiment, a single-threaded web server was deployed in the form of several containers managed using the portable extensible Kubernetes k3s platform. The results of calculations using the analytical model are confirmed by the results of simulation modeling implemented using the SimPy modeling library in the Python programming language. Based on the conducted research, the need to solve the optimization problem of the number of deployable containers in a computer system regarding the influence of this number on request servicing delays is shown. The conducted research can find application in the design of real-time cluster systems critical to acceptable wait service delays, ensuring the continuity of the computational process, and preserving unique data accumulated during the system operation. The proposed approaches can be applied in the creation of fault-tolerant distributed computer systems, including those operating with failure accumulation and system reconfiguration with load (request) redistribution during dynamic container migration and replication.

Modern artificial intelligence (AI) technologies are being used in a variety of fields, from science to everyday life. However, the widespread use of AI-based systems has highlighted a problem with their vulnerability to adversarial attacks. These attacks include methods of fooling or misleading an artificial neural network, disrupting its operations, and causing it to make incorrect predictions. This study focuses on protecting image recognition models against adversarial evasion attacks which have been recognized as the most challenging and dangerous. In these attacks, adversaries create adversarial data that contains minor perturbations compared to the original image, and then send it to a trained model in an attempt to change its response to the desired outcome. These distortions can involve adding noise or even changing a few pixels. In this paper, we consider the most relevant methods for generating adversarial data: the Fast Gradient Sign Method (FGSM), the Square Method (SQ), the predicted gradient descent method (PGD), the Basic Iterative Method (BIM), the Carlini-Wagner method (CW) and Jacobian Saliency Map Attack (JSMA). We also study modern techniques for defending against evasion attacks through model modification, such as adversarial training and pre-processing of incoming data, including spatial smoothing, feature squeezing, jpeg compression, minimizing total variance, and defensive distillation. While these methods are effective against certain types of attacks, to date, there is no single method that can be used as a universal defense. Instead, we propose a new method that combines adversarial learning with image pre-processing. We suggest that adversarial training should be performed on adversarial samples generated from common attack methods which can then be effectively defended against. The image preprocessing aims to counter attacks that were not considered during adversarial training. This allows to protect the system from new types of attacks. It is proposed to use jpeg compression and feature squeezing on the pre-processing stage. This reduces the impact of adversarial perturbations and effectively counteracts all types of considered attacks. The evaluation of image recognition model (based on convolutional neural network) performance metrics based was conducted. The experimental data included original images and adversarial images created using attack FGSM, PGD, BIM, SQ, CW, and JSMA methods. At the same time, adversarial training of the model was performed in experiments on data containing only adversarial examples for the FGSM, PGD, and BIM attack methods. Dataset used in experiments was balanced. The average accuracy of image recognition was estimated with crafted adversarial imaged datasets. It was concluded that adversarial training is effective only in countering attacks that were used during model training, while methods of pre-processing incoming data are effective only against more simple attacks. The average recognition accuracy using the developed method was 0.94, significantly higher than those considered methods for countering attacks. It has been shown that the accuracy without using any counteraction methods is approximately 0.19, while with adversarial learning it is 0.79. Spatial smoothing provides an accuracy of 0.58, and feature squeezing results in an accuracy of 0.88. Jpeg compression provides an accuracy of 0.37, total variance minimization — 0.58 and defensive distillation — 0.44. At the same time, image recognition accuracy provided by developed method for FGSM, PGD, BIM, SQ, CW, and JSMA attacks is 0.99, 0.99, 0.98, 0.98, 0.99 and 0.73, respectively. The developed method is a more universal solution for countering all types of attacks and works quite effectively against complex adversarial attacks such as CW and JSMA. The developed method makes it possible to increase accuracy of image recognition model for adversarial images. Unlike adversarial learning, it also increases recognition accuracy on adversarial data generated using attacks not used on training stage. The results are useful for researchers and practitioners in the field of machine learning.

The work develops the theory of stable M-estimators belonging to the class of redescending estimators, having the property of resistance to asymmetric contamination. Many well-known redescending estimators can be obtained within the framework of the locally stable approach of A.M. Shurygin, based on the analysis of the estimator instability functional (L2-norm of the influence function), or his approach based on the model of a series of samples with random point contamination (point Bayesian contamination model). These approaches are convenient for constructing various stable M-estimators and, in comparison with classical robust procedures, provide wider opportunities. The family of conditionally optimal estimators proposed by A.M. Shurygin within the framework of the first of the listed approaches can be defined as optimizing the asymptotic dispersion under a constraint on the value of instability. The corresponding problem can be represented in the form of optimization of the weighted L2-norm of the influence function. The second approach considers a specially formed nonparametric neighborhood of the model distribution, and it can also be reduced to the analysis of the weighted L2-norm of the influence function. Thus, this estimation quality criterion is quite general and useful for constructing robust estimators. The theory of estimators that are optimal in terms of weighted L2-norm of the influence function is currently underdeveloped. Specifically, for the corresponding families of estimators, the question of the uniqueness of family members remains unresolved. The question comes down to studying the convexity (concavity) of the optimized functional depending on the parameter defining the family. In the presented work, an expression is obtained in general form for the derivative with respect to the parameter of the quality functional of the optimal estimator. Inequalities are obtained for the second derivative necessary to establish its convexity (concavity) with respect to the parameter. Corollaries from these results are applied to describe the properties of a conditionally optimal family. The influence functions of a number of conditionally optimal estimators for the shift and scale parameters of the normal model are constructed. The characteristics of these estimators are studied. The stability of most of the considered estimators is shown, which is important for their practical application. The theoretical results obtained can be useful in studying the properties of compromise estimators based on two criteria as well as in studying minimax contamination levels within the framework of A.M. Shurygin’s point Bayesian contamination model. The results of the work can be used in situations of purposed data corruption by an adversary including the problems related to adversarial machine learning.

The symmetrical buckling modes of a rectangular Kirchhoff plate with two clamped and two free parallel faces (CFCF-plate) under the action of a distributed compressive load applied to the clamped faces have been studied. The function of plate deflections due to loss of stability is represented by two hyperbolic-trigonometric series with indefinite coefficients which are found when all conditions of the boundary value problem are exactly satisfied. The problem is reduced to solving a homogeneous infinite system of linear algebraic equations with respect to one sequence of uncertain coefficients which contain the desired critical load as a parameter. To obtain nontrivial solutions, the determinant of the system must be equal to zero. This eigenvalue problem has countless solutions. It is proposed to find non-trivial solutions of the system using the method of successive approximations with enumeration of the load parameter. Using computer calculations, the first four critical loads (including the Euler load) were found applied to the clamped parallel faces of a square plate and giving symmetrical forms of buckling. The influence on the accuracy of calculations of the number of terms retained in the series and the number of iterations is studied. 3D images of the found buckling modes are presented. A comparison with known solutions is provided. The results obtained can be used in the design of various flat rectangular elements in microelectronics and nanotechnology.

Abstract Luminescence constitutes a unique source of insight into hot carrier processes in metals, including those in plasmonic nanostructures used for sensing and energy applications. However, being weak in nature, metal luminescence remains poorly understood, its microscopic origin strongly debated, and its potential for unraveling nanoscale carrier dynamics largely unexploited. Here, we reveal quantum-mechanical effects in the luminescence emanating from thin monocrystalline gold flakes. Specifically, we present experimental evidence, supported by first-principles simulations, to demonstrate its photoluminescence origin (i.e., radiative emission from electron/hole recombination) when exciting in the interband regime. Our model allows us to identify changes to the measured gold luminescence due to quantum-mechanical effects as the gold film thickness is reduced. Excitingly, such effects are observable in the luminescence signal from flakes up to 40 nm in thickness, associated with the out-of-plane discreteness of the electronic band structure near the Fermi level. We qualitatively reproduce the observations with first-principles modeling, thus establishing a unified description of luminescence in gold monocrystalline flakes and enabling its widespread application as a probe of carrier dynamics and light-matter interactions in this material. Our study paves the way for future explorations of hot carriers and charge-transfer dynamics in a multitude of material systems.

Abstract Tunable optical materials are indispensable elements in modern optoelectronics, especially in integrated photonics circuits where precise control over the effective refractive index is essential for diverse applications. Two-dimensional materials like transition metal dichalcogenides (TMDs) and graphene exhibit remarkable optical responses to external stimuli. However, achieving distinctive modulation across short-wave infrared (SWIR) regions while enabling precise phase control at low signal loss within a compact footprint remains an ongoing challenge. In this work, we unveil the robust electro-refractive response of multilayer ferroionic two-dimensional CuCrP2S6 (CCPS) in the near-infrared wavelength range. By integrating CCPS into silicon photonics (SiPh) microring resonators (MRR), we enhance light-matter interaction and measurement sensitivity to minute phase and absorption variations. Results show that electrically driven Cu ions can tune the effective refractive index on the order of 2.8 × 10−3 RIU (refractive index unit) while preserving extinction ratios and resonance linewidth. Notably, these devices exhibit low optical losses and excellent modulation efficiency of 0.25 V.cm with a consistent blue shift in the resonance wavelengths among all devices for either polarity of the applied voltage. These results outperform earlier findings on phase shifters based on TMDs. Furthermore, our study demonstrates distinct variations in electro-optic tuning sensitivity when comparing transverse electric (TE) and transverse magnetic (TM) modes, revealing a polarization-dependent response that paves the way for diverse applications in light manipulation. The combined optoelectronic and ionotronic capabilities of two-terminal CCPS devices present extensive opportunities across several domains. Their potential applications range from phased arrays and optical switching to their use in environmental sensing and metrology, optical imaging systems, and neuromorphic systems in light-sensitive artificial synapses.

Abstract The examination of entanglement across various degrees of freedom has been pivotal in augmenting our understanding of fundamental physics, extending to high dimensional quantum states, and promising the scalability of quantum technologies. In this paper, we demonstrate the photon number path entanglement in the frequency domain by implementing a frequency beam splitter that converts the single-photon frequency to another with 50% probability using Bragg scattering four-wave mixing. The two-photon NOON state in a single-mode fiber is generated in the frequency domain, manifesting the two-photon interference with two-fold enhanced resolution compared to that of single-photon interference, showing the outstanding stability of the interferometer. This successful translation of quantum states in the frequency domain will pave the way toward the discovery of fascinating quantum phenomena and scalable quantum information processing.

Abstract Laser wakefield acceleration, as an advanced accelerator concept, has attracted great attentions for its ultrahigh acceleration gradient and the capability to produce high brightness electron bunches. The three-dimensional (3D) density serves as an evaluation metric for the particle bunch quality and is intrinsically related to the applications of an accelerator. Despite its significance, this parameter has not been experimentally measured in the investigation of laser wakefield acceleration. We report on an electro-optic 3D snapshot of a laser wakefield electron bunch at a position outside the plasma. The 3D shape of the electron bunch was detected by simultaneously performing optical transition radiation imaging and electro-optic sampling. Detailed 3D structures to a few micrometer levels were reconstructed using a genetic algorithm. The electron bunch possessed a transverse size of less than 30 micrometers. The current profile shows a multi-peak structure. The main peak had a duration of < 10 fs and a peak current > 1 kA. The maximum electron 3D number density was ~ 9 × 1021 m -3. This research demonstrates a feasible way of 3D density monitoring on femtosecond kilo-ampere electron bunches, at any position of a beam transport line for relevant applications.

Abstract Spectroscopic ellipsometry is a potent method that is widely adopted for the measurement of thin film thickness and refractive index. Most conventional ellipsometers utilize mechanically rotating polarizers and grating-based spectrometers for spectropolarimetric detection. Here, we demonstrated a compact metasurface array-based spectroscopic ellipsometry system that allows single-shot spectropolarimetric detection and accurate determination of thin film properties without any mechanical movement. The silicon-based metasurface array with a highly anisotropic and diverse spectral response is combined with iterative optimization to reconstruct the full Stokes polarization spectrum of the light reflected by the thin film with high fidelity. Subsequently, the film thickness and refractive index can be determined by fitting the measurement results to a proper material model with high accuracy. Our approach opens up a new pathway towards a compact and robust spectroscopic ellipsometry system for the high throughput measurement of thin film properties.

Abstract The analysis of optical spectra—emission or absorption—has been arguably the most powerful approach for discovering and understanding matter. The invention and development of many kinds of spectrometers have equipped us with versatile yet ultra-sensitive diagnostic tools for trace gas detection, isotope analysis, and resolving hyperfine structures of atoms and molecules. With proliferating data and information, urgent and demanding requirements have been placed today on spectrum analysis with ever-increasing spectral bandwidth and frequency resolution. These requirements are especially stringent for broadband laser sources that carry massive information and for dispersive devices used in information processing systems. In addition, spectrum analyzers are expected to probe the device’s phase response where extra information is encoded. Here we demonstrate a novel vector spectrum analyzer (VSA) that is capable of characterizing passive devices and active laser sources in one setup. Such a dual-mode VSA can measure loss, phase response, and dispersion properties of passive devices. It also can coherently map a broadband laser spectrum into the RF domain. The VSA features a bandwidth of 55.1 THz (1260–1640 nm), a frequency resolution of 471 kHz, and a dynamic range of 56 dB. Meanwhile, our fiber-based VSA is compact and robust. It requires neither high-speed modulators and photodetectors nor any active feedback control. Finally, we employ our VSA for applications including characterization of integrated dispersive waveguides, mapping frequency comb spectra, and coherent light detection and ranging (LiDAR). Our VSA presents an innovative approach for device analysis and laser spectroscopy, and can play a critical role in future photonic systems and applications for sensing, communication, imaging, and quantum information processing.

Abstract Crystalline white organic light-emitting diodes (C-WOLEDs) are promising candidates for lighting and display applications. It is urgently necessary, however, to develop energy-saving and high-efficiency C-WOLEDs that have stable and powerful emission to meet commercial demands. Here, we report a crystalline host matrix (CHM) with embedded nanoaggregates (NA) structure for developing high-performance C-WOLEDs by employing a thermally activated delayed fluorescence (TADF) material and orange phosphorescent dopants (Phos.-D). The CHM-TADFNA-D WOLED exhibit a remarkable EQE of 12.8%, which is the highest performance WOLEDs based on crystalline materials. The device has a quick formation of excitons and a well-designed energy transfer process, and possesses a fast ramping of luminance and current density. Compared to recently reported high-performance WOLEDs based on amorphous material route, the C-WOLED achieves a low series-resistance Joule-heat loss ratio and an enhanced photon output, demonstrating its significant potential in developing the next-generation WOLEDs.

Abstract A type of multifunctional erbium (Er3+) and ytterbium (Yb3+) codoped NaY(WO4)2 phosphors, with simultaneous photon upconversion, photon quantum cutting, and luminescence ratiometric temperature sensing abilities, have been developed, opening up new possibilities for high-performance silicon solar cells.

Abstract Colloidal quantum dots (CQDs) are of interest for optoelectronic devices because of the possibility of high-throughput solution processing and the wide energy gap tunability from ultraviolet to infrared wavelengths. People may question about the upper limit on the CQD wavelength region. To date, although the CQD absorption already reaches terahertz, the practical photodetection wavelength is limited within mid-wave infrared. To figure out challenges on CQD photoresponse in longer wavelength, would reveal the ultimate property on these nanomaterials. What’s more, it motivates interest in bottom-up infrared photodetection with less than 10% cost compared with epitaxial growth semiconductor bulk. In this work, developing a re-growth method and ionic doping modification, we demonstrate photodetection up to 18 μm wavelength on HgTe CQD. At liquid nitrogen temperature, the responsivity reaches 0.3 A/W and 0.13 A/W, with specific detectivity 6.6 × 108 Jones and 2.3 × 109 Jones for 18 μm and 10 μm CQD photoconductors, respectively. This work is a step toward answering the general question on the CQD photodetection wavelength limitation.

Abstract Organic mechanoluminescence materials, featuring dual emission and ultralong phosphorescence characteristics, exhibit significant potential for applications in real-time stress sensing, pressure-sensitive lighting, advanced security marking techniques, and material breakage monitoring. However, due to immature molecular design strategies and unclear luminescence mechanisms, these materials remain rarely reported. In this study, we propose a valuable molecular design strategy to achieve dual-channel mechano-phosphorescence. By introducing the arylphosphine oxide group into a highly twisted molecular framework, enhanced intra- and intermolecular interactions could be achieved within rigid structures, leading to dual-channel mechanoluminescence with greatly promoted ultralong phosphorescence. Further investigations reveal the substantial boosting effect of intra- and intermolecular interactions on mechanoluminescence and ultralong phosphorescence properties by locking the highly twisted molecular skeleton. This work provides a concise and guiding route to develop novel smart responsive luminescence materials for widespread applications in material science.

Abstract We have developed a Yb-doped fiber amplifier (YDFA) using fusion splicing, and characterized its performance with numerical simulation, achieving a continuous output of above 10 W $10~{\mathrm{W}}$ of 1064 nm $1064~{\mathrm{nm}}$ light. The device has been conceptualized to have a configuration as simple as possible; a strategy for using fiber amplifiers in harsh environments where quick repairs and replacements may become necessary at unexpected times.

Abstract In contrast to conventional E-mode resonance accelerators, H-mode DTLs provide for compact linac sections and have been established as highly efficient resonators during the last decades. Thus, H-mode structures are widely applied for heavy-ion acceleration with medium beam energies because of their outstanding capability to provide high acceleration gradients with relatively low energy consumption. To build upon those advantages, an alternating phase focusing beam dynamics layout has been applied to provide for a resonance accelerator design without internal lenses, which allows for eased commissioning, routine operation, maintenance, and potential future upgrades. The features of such a channel are going to be demonstrated on the example of two interdigital H-mode cavities, separated by an external quadrupole triplet. This setup provides for heavy ion (mass-to-charge ratio A / z ≤ 6 $A/z\le 6$ ) acceleration from 300 keV/u to 1400 keV/u and is used as an injector part of the superconducting continuous wave accelerator HELIAC. Hence, this promising approach generally enables effective and compact routine operation for various applications, such as super heavy ion research, material science, and radio biological applications such as heavy-ion tumor therapy.

Abstract HVE-400 ion implanter is special ion implantation equipment for semiconductor materials boron and phosphorus doping. The ion source and extraction deflection system are at high voltage platform, while the corresponding control system is at ground voltage position. The control signals and measurement signals of various parameters at the high-voltage end need to be transmitted between ground voltage and high voltage through optical fibers to isolate high voltage. Upgrading is carried out due to the aging of the optical fiber transmission control and monitoring system, which cannot work stably. The transformation replaces the original distributed single-point control method with an advanced distributed centralized control method, and integrates all control and monitoring functions into an industrial control computer for digital operation and display. In the computer software, two kinds of automatic calculation of ion mass number are designed. After upgrading, the implanter high-voltage platform control and monitoring system features digitalization, centralized control, high reliability, strong anti-interference, fast communication speed, and easy operation.

Abstract Broadband electroluminescence based on environment-friendly emitters is promising for healthy lighting yet remains an unprecedented challenge to progress. The copper halide-based emitters are competitive candidates for broadband emission, but their high-performance electroluminescence shows inadequate broad emission bandwidth of less than 90 nm. Here, we demonstrate efficient ultra-broadband electroluminescence from a copper halide (CuI) nanocluster single emitter prepared by a one-step solution synthesis-deposition process, through dedicated design of ligands and subtle selection of solvents. The CuI nanocluster exhibits high rigidity in the excitation state as well as dual-emissive modes of phosphorescence and temperature-activated delayed fluorescence, enabling the uniform cluster-composed film to show excellent stability and high photoluminescent efficiency. In consequence, ultra-broadband light-emitting diodes (LEDs) present nearly identical performance in an inert or air atmosphere without encapsulation and outstanding high-temperature operation performance, reaching an emission full width at half maximum (FWHM) of ~120 nm, a peak external quantum efficiency of 13%, a record maximum luminance of ~50,000 cd m−2, and an operating half-lifetime of 137 h at 100 cd m−2. The results highlight the potential of copper halide nanoclusters for next-generation healthy lighting.

Abstract Laser state active controlling is challenging under the influence of inherent loss and other nonlinear effects in ultrafast systems. Seeking an extension of degree of freedom in optical devices based on low-dimensional materials may be a way forward. Herein, the anisotropic quasi-one-dimensional layered material Ta2PdS6 was utilized as a saturable absorber to modulate the nonlinear parameters effectively in an ultrafast system by polarization-dependent absorption. The polarization-sensitive nonlinear optical response facilitates the Ta2PdS6-based mode-lock laser to sustain two types of laser states, i.e., conventional soliton and noise-like pulse. The laser state was switchable in the single fiber laser with a mechanism revealed by numerical simulation. Digital coding was further demonstrated in this platform by employing the laser as a codable light source. This work proposed an approach for ultrafast laser state active controlling with low-dimensional material, which offers a new avenue for constructing tunable on-fiber devices.

Abstract With the fast development of artificial intelligence (AI), Internet of things (IOT), etc, there is an urgent need for the technology that can efficiently recognize, store and process a staggering amount of information. The AlScN material has unique advantages including immense remnant polarization, superior temperature stability and good lattice-match to other III-nitrides, making it easy to integrate with the existing advanced III-nitrides material and device technologies. However, due to the large band-gap, strong coercive field, and low photo-generated carrier generation and separation efficiency, it is difficult for AlScN itself to accumulate enough photo-generated carriers at the surface/interface to induce polarization inversion, limiting its application in in-memory sensing and computing. In this work, an electro-optic duplex memristor on a GaN/AlScN hetero-structure based Schottky diode has been realized. This two-terminal memristor shows good electrical and opto-electrical nonvolatility and reconfigurability. For both electrical and opto-electrical modes, the current on/off ratio can reach the magnitude of 104, and the resistance states can be effectively reset, written and long-termly stored. Based on this device, the “IMP” truth table and the logic “False” can be successfully reproduced, indicating the huge potential of the device in the field of in-memory sensing and computing.

This work is devoted to the scientific and technical aspects of individual stages of active optical fibers preforms’ optical-geometric parameters metrological control. The concept of a system presented makes it possible to carry out a study of a rare earth element distribution in the preform of an active optical fiber and to monitor geometric parameters, and also to study the evolution of the refractive index profile along the length of the sample at a qualitative level. As far as it is known, it is the first description of the preform optical, geometric, and luminescent properties measurement within a single automated laboratory bench. Also, the novelty of the approach lies in the fact that the study of the refractive index profile variation along the length of the preform is, for the first time, conducted using the “dry” method, that is, without immersing the sample in synthetic oil, which makes the process less labor-intensive and safer.

Artificial intelligence paradigms hold significant potential to advance nanophotonics. This study presents a novel approach to designing a plasmonic absorber using an artificial neural network as a surrogate model in conjunction with a genetic algorithm. The methodology involved numerical simulations of multilayered metal–dielectric plasmonic structures to establish a dataset for training an artificial neural network (ANN). The results demonstrate the proficiency of the trained ANN in predicting reflectance spectra and its ability to generalize intricate relationships between desired performance and geometric configurations, with values of correlation higher than 98% in comparison with ground-truth electromagnetic simulations. Furthermore, the ANN was employed as a surrogate model in a genetic algorithm (GA) loop to achieve target optical behaviors. The proposed methodology provides a powerful means of inverse designing multilayered metal–dielectric devices tailored for visible band wavelength filtering. This research demonstrates that the integration of AI-driven approaches in nanophotonics leads to efficient and effective design strategies.

Photoacoustic imaging (PAI) is a cutting-edge biomedical imaging modality, providing detailed anatomical and functional information about the area beneath the skin surface. Its light energy deposition is such that PAI typically provides clear images of the skin with high signal-to-noise ratios. Specifically, the rich optical contrast of PAI allows biological information related to lesion growth, malignancy, treatment response, and prognosis to be seen. Given its significant advantages and emerging role in imaging skin lesions, we summarize and comment on representative studies of skin PAI, such as the guidance of skin cancer biopsies and surgical excisions, and the accurate diagnosis of psoriasis. We conclude with our insights about the clinical significance of skin PAI, showing how its use to identify biological characteristics in lesion microenvironments allows early diagnosis and prognosis of disease.

The selection of an affordable method to fabricate plasmonic metasurfaces needs to guarantee complex control over both tunability and reproducibility of their spectral and morphological properties, making plasmonic metasurfaces suitable for integration into different sensing devices. Displacement Talbot lithography could be a valid solution thanks to the limited fabrication steps required, also providing the highly desired industrial scalability. Fabricated plasmonic metasurfaces are represented by a gold nanohole array on a glass substrate based on a triangular pattern. Scanning electron microscopy measurements have been recorded, showing the consistency of the surface features with the optimized design parameters. Reflectance and transmittance measurements have also been carried out to test the reliability and standardization of the metasurface’s optical response. Furthermore, these plasmonic metasurfaces have also been successfully tested for probing refractive index variations in a microfluidic system, paving the way for their use in sensitive, real-time, label-free, and multiplexing detection of bio-molecular events.

With the advancement of China’s wind power industry, research into full-scale structural testing of wind turbine blades, including static testing and fatigue testing, has shown increasing significance. Static testing measures the deflection at fixed points, using pull-wire sensors in industrial practice. However, the demerits of this method involve single dimension, excessive deviation, costly experiment, and complex installment. Given the advantages that lidar provides, correspondingly, high data density, precision, and convenience, we proposed a simple and efficient spatial large deflection measurement system for wind turbine blades with multi lidars. For point clouds collected from lidar scanners, registration based on point primitives and geometric primitives, dynamic radius DBSCAN clustering, spatial line clustering, and line integrals are applied to calculate the 3D coordinates of measured points on the blade. Experimentally validated, the proposed method demonstrates its effectiveness in serving as a viable alternative to the traditional pull-wire sensor measurement approach. In the minimum oscillation direction test, the measurement error is controlled within 3% compared to the theoretical value. Simultaneously, in the maximum swing direction test, the 3D coordinates of the measured point remain consistent with the changing trend observed under small deformation. These results confirm the feasibility of the system and its potentials to be generalized.

Plasmonic arrays are grating-like structures able to couple an incoming electromagnetic field into either localized or propagating surface plasmonic modes. A triangular array of elliptical holes in a gold layer were realized resorting to displacement Talbot lithography. Scanning electron microscopy was used to evaluate the geometrical features and finite time domain simulations were performed to verify the consistency of the design. The optical response was characterized by angle-resolved reflectance and transmittance measurements. The results demonstrate the good quality and uniformity of the array. Furthermore, the study on the dependence of the optical response on both the hexagonal lattice and the elliptical hole-defined symmetry properties was conducted allowing the distinction of their effects on both the localized and propagating plasmonic modes. The results indicate that the localized component of the plasmonic modes is mainly affected by the elliptical shape, while the propagating part is influenced by the hexagonal lattice symmetry.

Abstract The extension of dual-comb spectroscopy (DCS) to all wavelengths of light along with its ability to provide ultra-large dynamic range and ultra-high spectral resolution, renders it extremely useful for a diverse array of applications in physics, chemistry, atmospheric science, space science, as well as medical applications. In this work, we report on an innovative technique of quartz-enhanced multiheterodyne resonant photoacoustic spectroscopy (QEMR-PAS), in which the beat frequency response from a dual comb is frequency down-converted into the audio frequency domain. In this way, gas molecules act as an optical-acoustic converter through the photoacoustic effect, generating heterodyne sound waves. Unlike conventional DCS, where the light wave is detected by a wavelength-dependent photoreceiver, QEMR-PAS employs a quartz tuning fork (QTF) as a high-Q sound transducer and works in conjunction with a phase-sensitive detector to extract the resonant sound component from the multiple heterodyne acoustic tones, resulting in a straightforward and low-cost hardware configuration. This novel QEMR-PAS technique enables wavelength-independent DCS detection for gas sensing, providing an unprecedented dynamic range of 63 dB, a remarkable spectral resolution of 43 MHz (or ~0.3 pm), and a prominent noise equivalent absorption of 5.99 × 10-6 cm-1·Hz-1/2.