Dynamic Filter Research Articles

Video Chatting Platform with Video Enhancing Feature Using Super Resolution

Abstract: This paper describes the process of creating a Multi-User Video Chatting Platform with Agora Video SDK and a Django Backend. The Video Super Resolution technique is being used to improve the quality of video calls. VSR is the process of reconstructing high-resolution videos from low-resolution ones. We employ the Dynamic Up sampling Filters Without Explicit Motion Compensation technique for VSR. We reconstruct an HR image directly from the input image using dynamic Up sampling filters, and the fine details are added using the computed residual.

Dynamic Ensemble Bayesian Filter for Robust Control of a Human Brain-Machine Interface.

Brain-machine interfaces (BMIs) aim to provide direct brain control of devices such as prostheses and computer cursors, which have demonstrated great potential for motor restoration. One major limitation of current BMIs lies in the unstable performance due to the variability of neural signals, especially in online control, which seriously hinders the clinical availability of BMIs. We propose a dynamic ensemble Bayesian filter (DyEnsemble) to deal with the neural variability in online BMI control. Unlike most existing approaches using fixed models, DyEnsemble learns a pool of models that contains diverse abilities in describing the neural functions. In each time slot, it dynamically weights and assembles the models according to the neural signals in a Bayesian framework. In this way, DyEnsemble copes with variability in signals and improves the robustness of online control. Online BMI experiments with a human participant demonstrate that, compared with the velocity Kalman filter, DyEnsemble significantly improves the control accuracy (increases the success rate by 13.9% in the random target pursuit task) and robustness (performs more stably over different experiment days). Experimental results demonstrate the superiority of DyEnsemble in online BMI control. DyEnsemble frames a novel and flexible dynamic decoding framework for robust BMIs, beneficial to various neural decoding applications.

Optimizing the Perceptual Quality of Time-Domain Speech Enhancement with Reinforcement Learning

In neural speech enhancement, a mismatch exists between the training objective, i.e., Mean-Square Error (MSE), and perceptual quality evaluation metrics, i.e., perceptual evaluation of speech quality and short-time objective intelligibility. We propose a novel reinforcement learning algorithm and network architecture, which incorporate a non-differentiable perceptual quality evaluation metric into the objective function using a dynamic filter module. Unlike the traditional dynamic filter implementation that directly generates a convolution kernel, we use a filter generation agent to predict the probability density function of a multivariate Gaussian distribution, from which we sample the convolution kernel. Experimental results show that the proposed reinforcement learning method clearly improves the perceptual quality over other supervised learning methods with the MSE objective function.

A hybrid multi-stage methodology for remaining useful life prediction of control system: Subsea Christmas tree as a case study

With the improvement of control system composition and operation process complexity, the uncertainty in its operation process increases and real-time observation data is difficult to obtain, and the influence of noise also exists in the process of signal acquisition, which brings more difficulties to the prediction of the remaining useful life (RUL). To solve these problems, a hybrid multi-stage methodology for RUL prediction of control system is proposed. The variant of unscented Kalman filter (UKF) utilizes dynamic Bayesian networks (DBNs) for uncertainty analysis in the process of prediction using UKF, to analyze RUL of nonlinear degenerate systems. In the prophase of prediction, the dynamic unscented Kalman filter models calculate the distribution of random faults and process noise, match the degradation stage of the system and obtain the operation data. Then, optimize the degradation process of the system, and the covariance and the optimal estimate of the system are calculated by cyclic iteration. The real degradation process of control system is simulated by optimizing the results, so as to compensate for the lack of accurate measurement of the real degradation process. The proposed method can improve the accuracy of RUL prediction and enhance the robustness of the prediction model. The methodology is verified by subsea Christmas tree with electro-hydraulic compound control.

Anti-disturbance cooperative formation containment control for multiple autonomous underwater vehicles with actuator saturation

This paper is concerned with the issue of time-varying formation containment control for multiple autonomous underwater vehicles (MAUVs) system subject to actuator saturation in three-dimensional space. The adaptive saturation dynamic filter technology is incorporated to reduce the influence of actuator saturation on the MAUVs system. For the sake of actively compensating the total disturbance containing the unknown nonlinearities and external disturbances, the extended state observers (ESOs) are customized, thus improving the anti-disturbance capability of MAUVs system. The tracking differentiators (TDs) are employed into estimating the derivatives of the proposed virtual control laws and dealing with the matter of explosion of complexity. Furthermore, via Lyapunov stability analysis, it is proven that the designed control laws can effectively guarantee that the leader AUVs are capable of tracking the virtual leader AUV with time-varying offset vectors, and at the same time the follower AUVs are capable of being steered into the convex hull formed by the leader AUVs. At last, a numerical simulation is implemented to verify the effectiveness of the proposed method.

Observer Based Single-Phase Active Current Harmonic Filter for Distributed Industrial Loads

Harmonic currents are introduced into power system as use of nonlinear electrical loads increases. Extra losses are caused by harmonic current stream into effective supply lines. A dynamic power filter engenders harmonic compensating current using a switching inverter. Single-phase current into the harmonic filter connects in parallel to load end and inject current harmonic required by the load feeder to avoid harmonic current in the direction of power system. This report discusses harmonic problem, available harmonic mitigation techniques, basic design of filter, control strategy, design steps of major hardware blocks, modelling and simulation of current harmonic filter in MATLAB SIMULINK setting, component selection, hardware implementation and final results. This filter will eliminate harmonic up to 45th order. The control circuit consists of a peak filter, all-pass filter, low pass filter and adders which are used to produce error signal by comparing harmonic current demand of load and injected harmonic current through switching inductor.

Universal and dynamic ridge filter for pencil beam scanning particle therapy: a novel concept for ultra-fast treatment delivery

Objective. In pencil beam scanning particle therapy, a short treatment delivery time is paramount for the efficient treatment of moving targets with motion mitigation techniques (such as breath-hold, rescanning, and gating). Energy and spot position change time are limiting factors in reducing treatment time. In this study, we designed a universal and dynamic energy modulator (ridge filter, RF) to broaden the Bragg peak, to reduce the number of energies and spots required to cover the target volume, thus lowering the treatment time. Approach. Our RF unit comprises two identical RFs placed just before the isocenter. Both RFs move relative to each other, changing the Bragg peak’s characteristics dynamically. We simulated different Bragg peak shapes with the RF in Monte Carlo simulation code (TOPAS) and validated them experimentally. We then delivered single-field plans with 1 Gy/fraction to different geometrical targets in water, to measure the dose delivery time using the RF and compare it with the clinical settings. Main results. Aligning the RFs in different positions produces different broadening in the Bragg peak; we achieved a maximum broadening of 2.5 cm. With RF we reduced the number of energies in a field by more than 60%, and the dose delivery time by 50%, for all geometrical targets investigated, without compromising the dose distribution transverse and distal fall-off. Significance. Our novel universal and dynamic RF allows for the adaptation of the Bragg peak broadening for a spot and/or energy layer based on the requirement of dose shaping in the target volume. It significantly reduces the number of energy layers and spots to cover the target volume, and thus the treatment time. This RF design is ideal for ultra-fast treatment delivery within a single breath-hold (5–10 s), efficient delivery of motion mitigation techniques, and small animal irradiation with ultra-high dose rates (FLASH).

Question-guided feature pyramid network for medical visual question answering

Medical VQA (VQA-Med) is a critical multi-modal task that raises attention from the community. Existing models utilized just one high-level feature map (i.e., the last layer feature map) extracted by CNN and then fused it with semantic features through the co-attention mechanism. However, only using the high-level feature as a visual feature often ignores the details of the image, which are crucial for VQA-Med. In addition, questions often serve as a guide to targets of attention in the medical image. Therefore, in this paper, we propose a question-guided Feature Pyramid Network (QFPN) for VQA-Med. It extracts multi-level visual features with a feature pyramid network (FPN). In this way, the multi-scale information of medical images can be captured by using the high resolution of low-level features and rich semantic information of high-level features. Besides, a novel question-guided dynamic filter network (DFN) is designed to modulate the fusion progress of multi-level visual features and semantic features with respect to the raised question. Extensive results have demonstrated the effectiveness of the QFPN. Especially, we beat the winner of the 2019 ImageCLEF challenge and achieved 63.8% Accuracy and 65.7% BLEU in the ImageCLEF 2019 VQA-Med dataset.

Efficient Adaptive Upsampling Module for Real-Time Semantic Segmentation

Upsampling operation is necessary for semantic segmentation and other pixel-level prediction tasks. Among the commonly used upsampling operations, some are too simple to effectively recover the spatial details lost during downsampling process, and some are too complex and have high computation complexity. In real-world applications, it is critical to achieve high accuracy and maintain real-time inference speed. Therefore, an efficient upsampling operation is essential for these tasks. In this paper, we introduce efficient adaptive upsampling module (EAUM) for real-time semantic segmentation. Inspired by dynamic filter networks, EAUM adaptively predicts the kernel weight of each point in the upsampled feature map according to the corresponding points in the input feature map. To reduce computational cost, EAUM decomposes the spatial information and channel information required for upsampling. The proposed EAUM shows impressive performance on Cityscapes and CamVid benchmarks. Specifically, DenseENet with EAUM outperforms the baseline by 1.4% [Formula: see text] and 1.6% [Formula: see text] in accuracy with a slight drop in inference speed on Cityscapes test dataset.

Manipulator Actuated Integrated Position and Attitude Stabilization of Spacecraft Subject to External Disturbances

This article addresses the dynamics and control problem of integrated position and attitude stabilization via manipulator actuation for spacecraft in proximity operations subject to external disturbances. Following recursive modeling philosophy, kinematics, and dynamics are first formulated for multiple-manipulator actuated coupled position and attitude system of spacecraft in the presence of external disturbances, where two vector factorizations are proposed to ensure a compact and explicit dynamic formulation, holding the inherent skew-symmetric property of the coefficient matrices. In view of the unconspicuous cascaded-like system structure, a nonlinear control scheme is designed following backstepping philosophy to drive the joints of manipulators causing reactions to robustly stabilize the spacecraft position and attitude. Toward this end, a reference trajectory prescribing the spacecraft motion is predesigned by means of polynomial functions ensuring well-behaviored performance. Then, as a key component, a second-order dynamic filter is constructed making use of the dynamics structure to generate the command joint motion capable of manipulator actuation. The rigorous closed-loop stability and robust performance analyses are undertaken within the Lyapunov framework. Moreover, three types of self-collisions possibly happening in the motion of multiple manipulators are considered and handled by three collision detection methods, which facilitates a collision-free trajectory shaping during the aforementioned reference trajectory design phase. Finally, numerical simulations are given to demonstrate the effect and robustness of the proposed control scheme.

Image restoration of luminous objects in dense fog

Images captured in dense fog are always severely degraded. For luminous objects, the luminance and contrast of the image are severely reduced due to the strong absorption of suspended particles. In this case, owing to the influence of forward scattered light and detector noise, the signal-to-noise ratio of the image is greatly reduced. Traditional image dehazing algorithms will enhance the noise while improving the image contrast. Here a dynamic equation-based filter is proposed, which can strongly suppress speckle and noise while improving target contrast. Based on this filter, we design an image restoration method for luminous targets in dense fog. The effectiveness of the proposed method is verified by experiments. The experimental results show that the proposed method has a good recovery effect on the luminous targets in dense fog with an optical thickness of 4.51. The proposed method has better performance than the traditional dehazing algorithms and conventional filtering method.

Multifield-Controlled Terahertz Hybrid Metasurface for Switches and Logic Operations.

Terahertz (THz) meta-devices are considered to be a promising framework for constructing integrated photonic circuitry, which is significant for processing the upsurge of data brought about by next-generation telecommunications. However, present active metasurfaces are typically restricted by a single external driving field, a single modulated frequency, fixed switching speed, and deficiency in logical operation functions which prevents devices from further practical applications. Here, to overcome these limitations, we propose a hybrid THz metasurface consisting of vanadium dioxide (VO2) and germanium (Ge) that enables electrical and optical tuning methods individually or simultaneously and theoretically investigate its performance. Each of the two materials is arranged in the meta-atom to dominate the resonance strength of toroidal or magnetic dipoles. Controlled by either or both of the external excitations, the device can switch on or off at four different frequencies, possessing two temporal degrees of freedom in terms of manipulation when considering the nonvolatility of VO2 and ultrafast photogenerated carriers of Ge. Furthermore, the "AND" and "OR" logic operations are respectively achieved at two adjacent frequency bands by weighing normalized transmission amplitude. This work may provide an auspicious paradigm of THz components, such as dynamic filters, multiband switches, and logical modulators, potentially promoting the design and implementation of multifunctional electro-optical devices in future THz computing and communication.

Linear Parameter-Varying Model Predictive Control for Hydraulic Wind Turbine

Wind speed uncertainty and measurement noise affect the control effect in hydraulic wind turbine systems. This paper proposes a model predictive control (MPC) method with a dynamic Kalman filter (KF) based on a linear parameter-varying (LPV) model to address this problem. First of all, the LPV model for a nonlinear system of a hydraulic wind turbine is established using function substitution. Then, a LPV-based KF is introduced into the MPC to provide more precise estimated results and improve the anti-interference ability of the system. According to the current condition of the hydraulic wind turbine, the method updates the Kalman state estimator at each sampling instant and computes the optimal control input by solving a quadratic programming (QP) optimization problem. The performance and the efficiency of the proposed method is validated in simulation and compared with other methods.

An Adaptive Dynamic Multi-Template Correlation Filter for Robust Object Tracking

In the field of computer vision and robotics, scholars use object tracking technology to track objects of interest in various video streams and extend practical applications, such as unmanned vehicles, self-driving cars, robotics, drones, and security surveillance. Object tracking is a mature technology in the field of computer vision and robotics; however, there is still no one object tracking algorithm that can comprehensively and simultaneously solve the four problems encountered by tracking objects, namely deformation, illumination variation, motion blur, and occlusion. We propose an algorithm called an adaptive dynamic multi-template correlation filter (ADMTCF) which can simultaneously solve the above four difficulties encountered in tracking moving objects. The ADMTCF encodes local binary pattern (LBP) features in the HSV color space, so the encoded features can resist the pollution of the tracking image caused by illumination variation. The ADMTCF has four templates that can be adaptively and dynamically resized to maintain tracking accuracy to combat tracking problems such as deformation, motion blur, and occlusion. In this paper, we experimented with our ADMTCF algorithm and various state-of-the-art tracking algorithms in scenarios such as deformation, illumination variation, motion blur, and occlusion. Experimental results show that our proposed ADMTCF exhibits excellent performance, stability, and robustness in various scenarios.

On-board BDS dynamic filtering ballistic determination and precision evaluation

Abstract BeiDou navigation satellite system (BDS) receivers on-board cannot give real-time positioning precision in tracking missile and carrier rocket. In this article, the dynamic filtered optimal estimation theory is applied to the highly dynamic on-board BDS ballistic parameter solution, an optimally constrained geometric dilution precision (GDOP) constellation selection strategy is proposed, a mathematical model based on dynamic filtered estimation for the ballistic determination and precision estimation of the on-board BDS is established, and an analysis of the on-board BDS positioning precision is carried out using the observed data. The calculation results show that the dynamic filter positioning algorithm is simple, practical, and reliable, which can effectively suppress and reduce the random errors of ballistic parameters and significantly improve the positioning precision, fully satisfying the needs of high dynamic and high precision navigation and positioning users, and has good application prospects.

Decoupled dynamic group equivariant filter for saliency prediction on omnidirectional image

Current saliency prediction models based on convolutional neural networks (CNNs) achieve solid improvement in predicting human attention on omnidirectional image (ODI). However, these models that employ standard convolution have two main shortcomings: content-agnostic and computation-intensive. To address these two shortcomings, we propose a decoupled dynamic group equivariant filter (DDGF). Specifically, inspired by the attention mechanism that adopts light-weight branches for estimating spatial and channel attention, we decouple group equivariant convolution (i.e. p4 convolution) into spatial and channel dynamic group equivariant filters. Such a design not only makes p4 convolution filter adaptive to ODI content, but also considerably reduces computational cost. To our best knowledge, the DDGF is the first decoupled dynamic convolution filter that applied to the task of saliency prediction. Meanwhile, we observe that it is effective and efficient when replacing standard group equivariant convolution with DDGF in ODI saliency prediction. Experimental results show that the proposed DDGF can achieve superior performance in comparison with other state-of-the-art methods. Additionally, we conduct ablation experiments to verify the effectiveness of each component of the proposed DDGF.

Damped Micromechanical Hyrovertical

The article considers the methods for vertical gyro construction on rough microelectromechanical elements, such as: angular velocity sensors and accelerometers. It is proposed to improve the accuracy of the microelectromechanical vertical gyro by combining it with a satellite navigation system. The solutions proposed in the article make it possible to improve the accuracy of the integrated vertical gyro based on micromechanical technologies, by the means of complex data processing that uses a compensation scheme with the latest dynamic filter, which practically does not distort the errors of the strapdown inertial microelectromechanical vertical gyro, and thus obtain an estimate of the ground speed as close to the true speed as possible. Based on the obtained estimate, it is proposed to construct a scheme of the vertical gyro speed correction (damping scheme), which would significantly improve the accuracy of estimation of the angular orientation’s parameters.

Potential of Fluid Dynamic Bowtie Filter for Dose Reduction and Image Quality Improvement of Cone-Beam CT

Reducing radiation dose to patients without compromising imaging quality has been an important issue in the medical use of X-ray computed tomography (CT). In this study, based on the conceptual designs of different types of attenuation filters, the radiation doses to patients who undergo a typical head, thorax and abdomen scan using a cone-beam CT with different scanning protocols were simulated using the Monte Carlo method, and the isotropy of the noise power spectrum (NPS) of the reconstructed images was also calculated. Compared to the scanning protocol without attenuation and tube current modulation (TCM), the results showed that the fluid dynamic bowtie filter (FDB) combined with the TCM technique reduced the average organ dose by 70%, 34% and 60% for a typical head, thorax and abdomen scan, respectively, and the NPS isotropy of the reconstructed images was also significantly improved. Compared to most currently used static bowtie filters, the FDB has a higher potential to reduce the dose for patients undergoing CT scans. Further efforts are warranted to make the FDB technique clinically useful.

Probabilistic Description of the State of Charge of Batteries Used for Primary Frequency Regulation

Battery participation in the service of power system frequency regulation is universally recognized as a viable means for counteracting the dramatic impact of the increasing utilization of renewable energy sources. One of the most complex aspects, in both the planning and operation stage, is the adequate characterization of the dynamic variation of the state of charge of the battery in view of lifetime preservation as well as the adequate participation in the regulation task. Since the power system frequency, which is the input of the battery regulation service, is inherently of a stochastic nature, it is easy to argue that the most proper methodology for addressing this complex issue is that of the theory of stochastic processes. In the first part of the paper, a preliminary characterization of the power system frequency is presented by showing that with an optimal degree of approximation it can be regarded as an Ornstein–Uhlenbeck process. Some considerations for guaranteeing desirable performances of the control strategy are performed by assuming that the battery-regulating power depending on the frequency can be described by means of a Wiener process. In the second part of the paper, more realistically, the regulating power due to power system changes is described as an Ornstein–Uhlenbeck or an exponential shot noise process driven by a homogeneous Poisson process depending on the frequency response features requested of the battery. Because of that, the battery state of charge is modeled as the output of a dynamic filter having this exponential shot noise process as input and its characterization constitutes the central role for the correct characterization of the battery life. Numerical simulations are carried out for demonstrating the goodness and the applicability of the proposed probabilistic approach.

A Dynamic Self-Tuning Maximum Correntropy Kalman Filter for Wireless Sensors Networks Positioning Systems

To improve the accuracy of the maximum correntropy Kalman filter (MCKF) in wireless sensors networks (WSNs) positioning, a dynamic self-tuning maximum correntropy Kalman filter (DSTMCKF) is proposed, where innovation and the sensors information of the WSNs are used to adjust the noise covariance matrices, and the maximum correntropy criterion is the criterion for the filter’s optimality. By dynamically adjusting the noise covariance matrices, the DSTMCKF ensures that the correntropy distribution is accurate in the presence of non-Gaussian noise (NGN), thus improving its ability to handle the NGN. In simulation and real environment positioning experiments, the DSTMCKF is used to compare with the MCKF, variable kernel width–maximum correntropy Kalman filter (VKW-MCKF) and robust minimum error entropy Kalman filter (R-MEEKF). Among the four filters, the DSTMCKF has the highest accuracy, and the error of the DSTMCKF is reduced by 34.5, 42.9 and 40.0%, respectively, compared with the MCKF, VKW-MCKF and R-MEEKF in the real-world environment positioning experiment. The application of the DSTMCKF in WSNs positioning systems improves the stability of the control systems because of the rising positioning accuracy, which makes WSNs positioning systems more widely used in scenarios requiring high stability, such as automatic parking.