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Image copy detection is an important technology of copyright protection. This paper proposes an efficient hashing method for image copy detection using 2D-2D (two-directional two-dimensional) PCA (Principal Component Analysis). The key is the discovery of the translation invariance of 2D-2D PCA. With the property of translation invariance, a novel model of extracting rotation-invariant low-dimensional features is designed by combining PCT (Polar Coordinate Transformation) and 2D-2D PCA. The PCT can convert an input rotated image to a translation matrix. Since the 2D-2D PCA is invariant to translation, the low-dimensional features learned from the translation matrix are rotation-invariant. Moreover, vector distances of low-dimensional features are stable to common digital operations and thus hash construction with the vector distances is of robustness and compactness. Three open image datasets are exploited to conduct various experiments for validating efficiencies of the proposed method. The results demonstrate that the proposed method is much better than some representative hashing methods in the performances of classification and copy detection.

The extrusion process of plastic transformation involves a series of variables and components which must be associated with the concept of reliability to achieve an expected performance in operational and quality terms. This paper describes the reliability framework of an extruder of a packaging company located in Brazil through the following mathematical representations: probability density function f(t), reliability curve R(t) and function rate of failure h(t). Through a statistical software the set of representations was able to develop the reliability framework of the machine, which can be used to support a FMEA analysis. Besides that, it was evidenced that in the current operating conditions of the machine, approximately 50% of the failure observations happen on the hour 100 of operating time. The main outcome is that it is recommended a systematic preventive maintenance based on 100h cycles. It is expected that this work may act as a guide for future implementation of improvement activities.

Recent advances in high-resolution microscopy have allowed scientists to better understand the underlying brain connectivity. However, due to the limitation that biological specimens can only be imaged at a single timepoint, studying changes to neural projections is limited to general observations using population analysis. In this paper, we introduce NeuRegenerate, a novel end-to-end framework for the prediction and visualization of changes in neural fiber morphology within a subject, for specified age-timepoints.To predict projections, we present neuReGANerator, a deep-learning network based on cycle-consistent generative adversarial network (cycleGAN) that translates features of neuronal structures in a region, across age-timepoints, for large brain microscopy volumes. We improve the reconstruction quality of neuronal structures by implementing a density multiplier and a new loss function, called the hallucination loss.Moreover, to alleviate artifacts that occur due to tiling of large input volumes, we introduce a spatial-consistency module in the training pipeline of neuReGANerator. We show that neuReGANerator has a reconstruction accuracy of 94% in predicting neuronal structures. Finally, to visualize the predicted change in projections, NeuRegenerate offers two modes: (1) neuroCompare to simultaneously visualize the difference in the structures of the neuronal projections, across the age timepoints, and (2) neuroMorph, a vesselness-based morphing technique to interactively visualize the transformation of the structures from one age-timepoint to the other. Our framework is designed specifically for volumes acquired using wide-field microscopy. We demonstrate our framework by visualizing the structural changes in neuronal fibers within the cholinergic system of the mouse brain between a young and old specimen., Comment: Accepted for publication in IEEE Transactions on Visualization and Computer Graphics

This paper presents a study of nonlinear waves in shallow water. The Korteweg-de Vries (KdV) equation has a canonical version based on oceanography theory, the shallow water waves in the oceans, and the internal ion-acoustic waves in plasma. Indeed, the main goal of this investigation is to employ a semi-analytical method based on the homotopy perturbation transform method (HPTM) to obtain the numerical findings of nonlinear dispersive and fifth order KdV models for investigating the behaviour of magneto-acoustic waves in plasma via fuzziness. This approach is connected with the fuzzy generalized integral transform and HPTM. Besides that, two novel results for fuzzy generalized integral transformation concerning fuzzy partial g H -derivatives are presented. Several illustrative examples are illustrated to show the effectiveness and supremacy of the proposed method. Furthermore, 2D and 3D simulations depict the comparison analysis between two fractional derivative operators (Caputo and Atangana-Baleanu fractional derivative operators in the Caputo sense) under generalized g H -differentiability. The projected method (GHPTM) demonstrates a diverse spectrum of applications for dealing with nonlinear wave equations in scientific domains. The current work, as a novel use of GHPTM, demonstrates some key differences from existing similar methods.

A new family of multivariate copulas constructed from existing copulas by transforming the corresponding random variables using their conditional distributions are introduced. It is shown that for symmetrical copulas in a triangular scheme, this transformation tends to asymptotic independence.

Watermarking images is essential for several reasons like security, validation etc. The extracted watermark should have similar characteristics as that of the watermark image embedded during the watermarking process. Also, it is important that the watermark image should not differ the features of the host image after embedding. In order conceive this features, it is important to have a technique of watermarking. In this paper, such an attempt has been made to incorporate the novel techniques of watermarking using single value decomposition (SVD), discrete wavelet transformation (DWT) and social group optimization algorithm (SGOA). The simulation-based experiment has been carried out to evaluate the performance of the technique in terms of structural similarity index (SSI). The Matlab environment is used for the simulations.

This article investigates the synchronization problem of interconnected linear two-time-scale systems (TTSSs) with switching topology. By utilizing the Chang transformation, a distributed synchronization protocol is proposed with event-triggered communication. Static and dynamic event-triggered mechanisms are proposed successively, which both contain two separated event-triggering conditions corresponding to the slow and the fast subsystems. The existence of a strictly positive time period between any two successive transmissions is ensured regardless of the initial states. The main difficulty of this study lies in that the state jump and parametric uncertainty appear because of the system transformation. To overcome the difficulty, the system is first modeled as an uncertain hybrid system. Then, the control gain is properly designed by solving Riccati-like equations dependent on the rough bounds of the eigenvalues of communication graph Laplacians, and a piecewise quadratic Lyapunov function is proposed with which the jump caused by the switching topology is subtly evaluated. Sufficient conditions are thus established to achieve the event-triggered synchronization. Furthermore, the results are also extended to solve the synchronization problem of the interconnected impulsive linear TTSSs. Finally, three numerical examples are provided to demonstrate the effectiveness of the proposed theoretical results.

The fundamental understanding of the effects of rare earth (RE) on the phase transformation of Fe-based alloys is essential in the development of advanced RE-containing high strength steels. In this work, we investigated the effects of La and Ce on the martensitic phase transformation of Fe in terms of the driving force and minimum energy path (MEP) using the first-principles calculations. The calculations of formation energy show that the RE substitution increases the stabilization of fcc-Fe and decreases that of bcc-Fe, thus decrease the driving force of phase transformation. Meanwhile, the analysis based on the generalized solid-state nudged elastic band (G-SSNEB) method indicates that in the RE-containing systems, an additional energy barrier appears when the phase transformation proceeds along the Bain transition path to c/a ratio of 1.14 in the FM state, which is associated with a sudden decrease in magnetization due to the hybridization between the orbitals of the RE atoms and those of the nearest neighbor Fe atoms. The results of potential-energy surfaces (PES) confirm that magnetic ordering and lattice volume change simultaneously with the Bain transition, and in comparison with pure Fe, the variation between the onset and the end structural volumes of the phase transformation is slight for the RE-containing Fe systems.

A new transformation that converts a Bayesian game to a so-called ex-ante agent game (a normal form game) is proposed. Differently from the existing transformation proposed by R. Selten that changes a Bayesian game to an interim agent game (also a normal form game), we prove that the new transformation preserves potentiality. In addition, there is a nonpotential Bayesian game whose ex-ante agent game is potential. We also prove that there is one-to-one correspondence between pure Bayesian Nash equilibriums (BNEs) of Bayesian games (if one exists) and pure Nash equilibriums (NEs) of the resulting ex-ante agent games. Then, we provide a sufficient and necessary condition for a Bayesian game to have an ex-ante agent potential game. By using these results, one can transform pure BNE seeking in Bayesian games to pure NE seeking in their ex-ante agent games (by using the potential functions of the ex-ante agent games (if one exists)), where previously pure BNE seeking in Bayesian games by using potential functions can only be done in Bayesian potential games (BPGs). Particularly, we prove for two-player games, BPGs are exactly the Bayesian games having ex-ante agen

The issue of adaptive output-feedback stabilization is investigated for a category of stochastic nonstrict-feedback nonlinear systems subject to unmeasured state and unknown control directions. By combining the event-triggered mechanism and backstepping technology, an adaptive fuzzy output-feedback controller is devised. In order to make the controller design feasible, a linear state transformation is introduced into the initial system. At the same time, the Nussbaum function technology is used to overcome the difficulties caused by unknown control directions, and the state observer solves the problem of the unmeasured state. Based on the fuzzy-logic system and its structural characteristics, the issue of unknown nonlinear function with nonstrict-feedback structure in the system is tackled. The designed controller could not only guarantee all signals of closed-loop systems are bounded in probability but also save communication resources effectively. Finally, numerical simulation and ship dynamics example are given to confirm the effectiveness of the proposed method.

To realize the three-dimensional stabilization control of fixed-wing unmanned aerial vehicles (UAVs), we analyze the nonholonomic characteristics, constraint non-integrability, and controllability of UAVs. To simplify the trigonometric function term in the dynamics of the UAV and avoid the singularity problem of the Euler angle in describing attitude, we use the quaternion theory to transform the dynamics of the UAV to avoid the complex trigonometric function derivation, which makes the dynamic matrix more concise. Based on this, a continuous periodic time-varying controller (CPTVC) is designed, and the effectiveness of the controller is proved using the homogeneous method. Finally, the results of the hardware in a loop simulation indicated that the exponential stability provided by the feedback controller can realize the three-dimensional stabilization of any initial position.

This article investigates the fixed-time synchronization problem of a class of delayed complex-valued inertial neural networks (CVINNs). The analysis does not rely on the traditional reduced-order transformation, but constructing Lyapunov functions directly focused on the original system. Based on the direct method and the separation method, different control strategies are proposed, under which the addressed CVINNs can achieve synchronization perfectly in a fixed time. The corresponding synchronization criteria in terms of matrix inequalities are derived, which are more concise and easier to verify than algebraic inequalities conditions, and the estimation of the settling times. The direct method makes full use of some innovative inequalities in the complex field; the exponential parameters in the designed controllers are independent. Finally, in order to fully support the theoretical results, based on two typical activation functions, the proposed theoretical results are numerically validated and compared.

The approximation learning of fuzzy concepts associated with fuzzy rough sets and three-way decisions is a useful technology for the representation, learning, and transformation of uncertain knowledge. However, how to integrate the advantages of fuzzy rough approximations and three-way approximations has rarely been carefully investigated so far. In this paper, we focus on exploring the connection and interplay of these two methods, and further propose the hierarchical fuzzy rough approximations in the dynamic fuzzy open-world environment. We utilize the temporal-spatial perspectives of three-way decisions to construct multi-granularity structures and implement multi-granularity learning. The temporality of data and the spatiality of model parameters are both considered in such frameworks. Subsequently, we discuss the interpretation and representation of fuzzy three-way regions in fuzzy rough sets with some definitions and properties. The max, double, and aggregated evaluation-based models of three-way approximations are proposed to gradually transform a fuzzy concept to a crisp concept by the time-evolving attributes. Finally, the comparative experimental results between static and dynamic approaches demonstrate the effectiveness of our proposed hierarchical approximation learning models.

This paper proposes a proportional-integral (PI) event-triggered control strategy for networked nonlinear systems subject to unmatched uncertainties. The influences of the unmatched uncertainties and external disturbances on the output channel are attenuated by an extended-state-observer-based output feedback controller through a state transformation. The outputs of the controller are transmitted in an event-triggered fashion by comparing the differences between the current sampling error and its integration with respect to time. We prove the stability of the closed-loop event-triggered control system by regulating the trajectories of the observation error and the tracking error into a compact set. Further, the relationships between control performance and designed parameters are investigated quantitatively. In addition, the nonexistence of Zeno behavior is guaranteed by proving the existence of a nonzero minimum inter-event time. Finally, the effectiveness of the proposed PI-type event-triggered control strategy in reducing signal transmission rate is validated through a numerical example and various experiments.

This paper considers a new bi-directional cascaded system of a fractional ordinary differential equation (FODE) and a fractional partial differential equation (FPDE) which interacts at an intermediate point. The space-dependent coefficients, interaction between the FODE and FPDE at an intermediate point and the presence of fractional calculus makes the FODE-FPDE cascaded system, representative. In this note, we first apply an invertible integral transformation to convert the system into a FODE-FPDE coupled system, as the target system, which is Mittag–Leffler stable. Using the backstepping method and under some assumptions of the space-dependent coefficients, we work out the kernel functions in the transformation and we design a boundary controller. Also, by the invertibility of the transformation, we show the Mittag–Leffler stability of the closed-loop system via the Lyapunov approach. Second, we propose an observer for which we prove that it can well estimate the original cascaded system. Then, we design an output feedback boundary control law and show that the closed-loop system is Mittag–Leffler stable under the designed output feedback control law. Finally, we present some numerical illustrations to show the correctness of the theoretical results.

This paper proposes an improved multiple synchronous reference frame (MSRF) current harmonic regulation strategy for dual three phase permanent magnet synchronous machine drive system. A new vector space decomposition transformation combined with auxiliary currents is designed to decouple the current fundamental with current harmonics, and the auxiliary currents can be established by the /9 phase shifting of the physical currents in ABC set and the /18 phase shifting of the physical currents in XYZ set. By employing the proposed current decomposition, it is easy to detect them using low pass filters in MSRFs. A generic complex vector proportional integral regulator is then employed to control the detected current harmonics to track the current harmonic references. Compared with the existing MSRF-based methods, the proposed method provides a new current harmonic detection with reduced delay effect, which contributes to enhancing the dynamic performance of current harmonic regulation, and also stabilizing the control system. Comprehensive experimental results are given to validate the effectiveness of the proposed control strategy.

The proportional-integral (PI) control method is long well-known because of its simplicity as well as effectiveness. Although the PI control method overwhelmingly dominates engineering applications, the issue of developing a general PI control for nonlinear systems remains open. This paper considers the PI control problem for a family of unknown nonlinear systems. A structurally simple and computationally inexpensive PI-type control approach with constant gains is provided to solve the problem, which maintains pre-assigned transient as well as steady-state performance and guarantees the global stability of closed-loop systems. The key to possessing such properties comes down to the transformation and modification of tracking errors. The effectiveness of the presented control approach is assessed via the experiment on a pneumatic artificial muscle, and the results corroborate the theoretical findings.

Adaptive output-feedback consensus with funnel performance is studied for nonlinear uncertain multi-agent systems (MASs). The nonlinear MASs contain unknown dynamics and only an output variable can be measured. The other states are not measured directly and are reconstructed via fuzzy state observers. The presence of output feedback makes the proposed design significantly different from the existing literature on control with funnel performance. In particular, appropriate adaptive laws must be defined to handle the presence of uncertain dynamics and local output information from a few neighboring nodes. Interestingly, with a suitable error transformation, it is shown that the proposed control law has a simpler structure than barrier function methods proposed in the literature to handle funnel-like performance. With respect to this point, simulation studies illustrate that the proposed method can dramatically reduce the control effort while satisfying transient and steady-state performance imposed by the funnel.

The accuracy and stability of navigation algorithms are crucial preconditions for underwater gravity matching aided navigation. To improve the matching accuracy and robustness of the matching algorithm, this paper presents a novel gravity matching navigation algorithm based on multiscale search and Hadamard transformed difference. The Hadamard transformation process was first time introduced in the new algorithm for the Walsh–Hadamard kernel function with the property of energy conservation. The gravity measurement sequences could be converted to the Hadamard domain; thus, the difference in numerical values, tendency, and spatial structure of the gravity measurement sequence were also a focus in the new algorithm, whereas only gravity statistical values were considered in classical matching algorithms. Therefore, with the proposed algorithm, the number of measurements necessary for matching can be effectively reduced, while improving the matching accuracy and success rate. In addition, a multiscale neighborhood search strategy based on contour constraints was designed to improve the matching efficiency, whereas a point-by-point global search was widely used in classical matching algorithms. Marine gravity maps of the South China Sea were used to construct the simulation tests. Simulation results show that under various conditions, the new algorithm has lower requirements for the number of measurements, measuring accuracy, and matching areas while exhibiting a higher navigation accuracy, matching success rate, and matching efficiency. Thus, the proposed algorithm could provide a new option for future practical applications of gravity matching aided navigation.

This paper addresses the vision-based landing problem of a low-cost quadrotor on an unknown moving platform. A robust landing controller is developed, which consists of the design of the low-complexity outer-loop controller and the geometric inner-loop attitude controller. First, an error transformation based on prescribed performance is designed to guarantee the landing behaviors and deal with the intermediate control signal of backstepping approaches, resulting in a low-complexity position-based visual servoing (PBVS) design. In addition, the proposed PBVS controller exhibits strong robustness against an uncertain relative dynamic system due to no incorporation of any prior knowledge of the moving platform. Next, a modified geometric attitude controller is presented by characterizing the geometric properties of rotation matrices intrinsically. Finally, the stability analysis is presented using Lyapunov stability theory, and the effectiveness of the proposed control strategy is demonstrated through numerical simulations and experiments.

This paper presents a non-smooth multibody dynamic formulation and error analysis of an antenna pointing mechanism including frictional spatial revolute joints (FSRJs) with small clearance in the framework of the special Euclidian group SE(3). The formulation leads to an inertial frame-invariant, a compact and unified description for rigid bodies and spatial revolute joints (SRJs). The geometric constraint of the bearing is covered by four open semi-cylinders, which can be treated as bilateral constraints assuming that the impact effects are negligible. The frictional contact problem is formulated as a horizontal linear complementary problem (HLCP), which is embedded in the Lie-group integration scheme. Error of the antenna pointing mechanism is modeled by means of the adjoint transformation and POE-based formula. The evolution of errors is obtained through the solution of non-smooth dynamics. The obtained numerical results illustrate the influences of FSRJs in dynamics modeling and error analysis of the antenna pointing mechanism.

This paper is concerned with the problem of output feedback control for a class of continuous-time hidden semi-Markov jump systems with time delays. Due to the limitations of the actual environment, system modes are usually undetectable, which are called hidden modes. The controller modes are described as observable modes. Emission probability is used to establish the relationship between above-mentioned two concepts. The jump parameters are governed by the hidden semi-Markov process, and the reason is that the mode information of the controller and the system is asynchronous. Besides, time delays are considered to be time-varying and dependent on the hidden mode. By employing some mathematical transformation and constructing a novel Lyapunov-Krasovskii functional, some new parameter-dependent sufficient stablization conditions can be obtained by designing an observed-mode-dependent static output-feedback controller. Finally, a practical example is provided to illustrate the effectiveness and merits of the proposed methods.

Uncertainty is inevitable in the decision-making process of real applications. Quantum mechanics has become an interesting and popular topic in predicting and explaining human decision-making behaviors, especially regarding interference effects caused by uncertainty in the process of decision making, due to the limitations of Bayesian reasoning. In addition, complex evidence theory (CET), as a generalized Dempster-Shafer evidence theory, has been proposed to represent and handle uncertainty in the framework of the complex plane, and it is an effective tool in uncertainty reasoning. Particularly, the complex mass function, also known as a complex basic belief assignment in CET, is complex-value modeled, which is superior to the classical mass function in expressing uncertain information. CET is considered to have certain inherent connections with quantum mechanics since both are complex-value modeled and can be applied in handling uncertainty in decision-making problems. In this article, therefore, by bridging CET and quantum mechanics, we propose a new complex evidential quantum dynamical (CEQD) model to predict interference effects on human decision-making behaviors. In addition, uniform and weighted complex Pignistic belief transformation functions are proposed, which can be used effectively in the CEQD model to help explain interference effects. The experimental results and comparisons demonstrate the effectiveness of the proposed method. In summary, the proposed CEQD method provides a new perspective to study and explain the interference effects involved in human decision-making behaviors, which is significant for decision theory.

In this paper, for the line-of-sight (LOS) tracking problem of a tractor-trailer vehicle system, we propose a novel adaptive constrained tracking control algorithm for the trailer to track a desired trajectory, while satisfying multiple performance and feasibility constraint requirements during the operation. To deal with these constraint requirements, both a universal barrier function approach and a novel state transformation scheme are incorporated to deal with constraints of different nature. An adaptive control structure is introduced to deal with system parameter uncertainties and external disturbances. We show that for the LOS distance and angle tracking errors, exponential convergence into small neighbourhoods of equilibrium can be guaranteed, with the convergence rate depending on the control input and adaptive gains. In the end, a simulation example and an experimental study further demonstrate the efficacy of the proposed algorithm.

In this paper, a fine-grained query authorization scheme with integrity verification is proposed over encrypted spatial data for location-based services (LBS). The fine-grained query authorization is enabled based on a distribution of spatial data by employing a non-uniform partition in the spatial domain to generate a density-based space filling curve (DSC), which can be used to generate index values for querying and transformation keys. The transformation keys can be used to generate query tokens for a secure spatial query as well as construct a transformation key tree whose subtree can be distributed by the LBS provider to an authorized user as transformation key for query tokens generation. Furthermore, the proposed scheme constructs a Merkle quad tree (MQ-tree) to support integrity verification by aggregating a digest of spatial data based on DSC and employing MQ-tree as a verification structure. The LBS provider can share a subtree of MQ-tree to authorized user as his verification structure, which corresponds to the transformation key of the authorized user. In this way, the authorized user can only generate the valid query tokens and verify query results in his authorized region. Extensive experimental results demonstrate the high efficiency of verification structure generation and verification operations

As an effective method for clustering applications, the clustering ensemble algorithm integrates different clustering solutions into a final one, thus improving the clustering efficiency. The key to designing the clustering ensemble algorithm is to improve the diversities of base learners and optimize the ensemble strategies. To address these problems, we propose a clustering ensemble framework that consists of three parts. First, three view transformation methods, including random principal component analysis, random nearest neighbor, and modified fuzzy extension model, are used as base learners to learn different clustering views. A random transformation and hybrid multiview learning-based clustering ensemble method (RTHMC) is then designed to synthesize the multiview clustering results. Second, a new random subspace transformation is integrated into RTHMC to enhance its performance. Finally, a view-based self-evolutionary strategy is developed to further improve the proposed method by optimizing random subspace sets. Experiments and comparisons demonstrate the effectiveness and superiority of the proposed method for clustering different kinds of data.

This article aims to solve the problem of formation control of mobile robots based on image and provide a low cost as well as ease of implementation solution for mobile robots relying merely on a monocular camera under field-of-view (FOV) constraints. A low complexity image-based visual servo (IBVS) controller is proposed, which can achieve the desired relative position on the image plane and solve the FOV constraints without the feature depth and leader's velocities information. To facilitate the control design, a state transformation is first performed to decouple the visual motion kinematics. Then, an error transformation is introduced to handle the FOV constraints and performance specifications are incorporated in the error transformation to achieve the predefined control performance. Finally, a simple static controller is derived using only information from images, and the stability of the uncertain system with unknown control direction/coefficients under the given performance control condition is analyzed. The effectiveness and performance of the proposed visual servoing controller can be illustrated using both simulations and experiments.

For leader-following networked systems with the topology switching and the aperiodic silence, limited-energy output formation tracking problems are investigated. Firstly, a new output formation tracking control protocol is proposed, which contains two components associated with the communication interactions between the leader and tracking intelligent agents and the communication interactions among tracking intelligent agents, respectively, and the aperiodic silence, the topology switching and the energy constraint index is introduced properly. Then, a two-step transformation method is presented to separate the whole dynamics of a networked system into the relative dynamics between the leader and tracking intelligent agents and the dynamics of the leader, and sufficient conditions for limited-energy output formation tracking for networked systems with limited energy and aperiodic silence are presented, which are extended into networked systems without the aperiodic silence. Especially, a partition checking algorithm is presented to check limited-energy output formation tracking design criteria. Finally, a numerical example is illustrated to demonstrate the validness of theoretical results.

We consider a system of a reaction-advection-diffusion partial differential equation (PDE) with a distributed input subject to an unknown and arbitrarily large time-delay. Using Lyapunov technique, we derive a delay-adaptive predictor feedback controller to ensure the global stability of the closed-loop system in the L^2 sense. More precisely, we write the input delay as a 1-D transport PDE with a spatial argument leading to the transformation of the time-delay into a spatially distributed shift. For the resulting mixed transport and reaction-advection-diffusion PDE system, we employ a PDE backstepping design and certainty equivalence principle to derive the suitable adaptive control law that compensates for the effect of the unknown time-delay. Our controller ensures the global stabilization in the L^2 sense. Our result is the first delay-adaptive predictor feedback controller with a PDE plant subject to a delayed distributed input. The feasibility of the proposed approach is illustrated by considering a mobile robot that spread a neutralizer over a polluted surface to achieve efficient decontamination with an unknown actuator delay arising from the non-collocation of the contamin

The goal of cross-view image matching based on geo-localization is to determine the location of a given ground-view image (front view) by matching it with a group of satellite-view images (vertical view) with geographic tags. Due to the rapid development of unmanned aerial vehicle (UAV) technology in recent years, it has provided a real viewpoint close to 45 degrees (oblique view) to bridge the visual gap between views. However, existing methods ignore the direct geometric space correspondence of UAV-satellite views, and only use brute force for feature matching, leading to inferior performance. In this context, we propose an end-to-end cross-view matching method that integrates cross-view synthesis module and geo-localization module, which fully considers the spatial correspondence of UAV-satellite views and the surrounding area information. To be specific, the cross-view synthesis module includes two parts: the oblique view of UAV is first converted to the vertical view by perspective projection transformation (PPT), which makes the UAV image closer to the satellite image; then we use conditional generative adversarial nets (CGAN) to synthesize the UAV image with vertical view style, which is close to the real satellite image by learning the converted UAV as the input image and the real satellite image as the label. Geo-localization module refers to existing local pattern network (LPN), which explicitly considers the surrounding environment of the target building. These modules are integrated in a single architecture called PCL, which mutually reinforce each other. Our method is superior to the existing UAV-satellite cross-view methods, which improves by about 5%.

This article investigates the controller design problem for an electromagnetic torsional micromirror with guaranteed transient performance constraint. Specifically, the developed solution works under the output regulation framework and utilizes the internal model principle for model parameter uncertainties and general reference trajectories tracking, incorporated with barrier Lyapunov function (BLF) method to prevent the tracking constraint violation. We first formulate the micromirror model as an output feedback system with relative degree two and unknown control coefficient, and further turn it into a lower triangular system with an extension transformation. Then, using the extended internal model design, we transform the output regulation problem of the transformed system into the stabilization problem of an augmented system. Finally, based on the BLF technique, we develop a stabilization controller for the augmented system to realize the asymptotic tracking of reference trajectory with transient performance constraint, where the effects of the main design parameters on the control performances are also investigated. By such a technical treatment, the entire control architecture is independent of the angular velocity information of the micromirror, which reduces the measurement complexity in practical. This feature behind the control scheme is important since the velocity information is difficult to be available in many microelectromechanical systems (MEMS). Moreover, the enhanced transient performance is beneficial for improving the scanning quality of the packaged micromirror system. The developed control solution is verified on an experimental platform using a field programmable gate array (FPGA)-based hardware, where the scanning and imaging applications are both conducted.

Fisheye cameras can capture a large field-of-view (Fov) scene but it introduces severe radial distortion in images. Thus, distortion rectification is a crucial step for subsequent computer vision tasks using fisheye cameras. A prevalent type of method predicts the displacement field between the input and output to rectify the distorted images. However, it is challenging to estimate the accurate flow in Cartesian coordinates (both x and y directions), in which the sampling strategy of the convolution kernel ignores the radial symmetry of distortion. In general, the pixel’s distortion at the same radius from the center is the same, while the radius corresponds to one parameter in polar coordinates. Motivated by this fact, we exploit the radial symmetry of distortion to predict a more straightforward one-dimensional flow, transforming the distorted image into the polar coordinates domain instead of predicting two-dimensional flow in x and y directions. Specifically, we propose a Polar coordinates Distortion Rectification Network (PCDRN), whose sampling strategy corresponds to the radial distortion characteristic so that a more accurate flow can be predicted. To eliminate the blurs and ring artifacts induced by the coordinates transformation, a Polar-To-Cartesian Appearance Enhancement Network is designed to enhance the local appearance of rectified images. Experimental results on the synthesized dataset and real-world dataset demonstrate the superiority of our approach in both quantitative and qualitative evaluations.

High entropy alloys (HEAs) with superb mechanical properties have been traditionally produced by solidification and subsequent heat treatment. In this paper, we demonstrate a new route via one-step process using sub-rapid cooling. Under proper cooling rates, the CrFeCoNiAl0.6 HEA could form ultrafine FCC+BCC dual-phase structure. By varying cooling rate, we can control the fraction of the BCC phase and refinement of FCC microstructures that have tunable mechanical properties in yield strength and hardness ranging from ∼580 to ∼1460 MPa and ∼260 to ∼550 Hv. We show that the structure-property-processing relation originates from the sideplate microstructures formed during fast cooling that have specific crystallographic orientation relationship between the FCC and BCC phases and chemical segregation. This work provides a new setting for better understanding of the solid-state phase transformation in HEAs under sub-rapid solidification conditions as well as a novel method for development of high-performance HEAs.

Supervised dimensionality reduction for sequence data learns a transformation that maps the observations in sequences onto a low-dimensional subspace by maximizing the separability of sequences in different classes. It is typically more challenging than conventional dimensionality reduction for static data, because measuring the separability of sequences involves non-linear procedures to manipulate the temporal structures. In this paper, we propose a linear method, called order-preserving Wasserstein discriminant analysis (OWDA), and its deep extension, namely DeepOWDA, to learn linear and non-linear discriminative subspace for sequence data, respectively. We construct novel separability measures between sequence classes based on the order-preserving Wasserstein (OPW) distance to capture the essential differences among their temporal structures. Specifically, for each class, we extract the OPW barycenter and construct the intra-class scatter as the dispersion of the training sequences around the barycenter. The inter-class distance is measured as the OPW distance between the corresponding barycenters. We learn the linear and non-linear transformations by maximizing the inter-class distance and minimizing the intra-class scatter. In this way, the proposed OWDA and DeepOWDA are able to concentrate on the distinctive differences among classes by lifting the geometric relations with temporal constraints. Experiments on four 3D action recognition datasets show the effectiveness of OWDA and DeepOWDA.

In this article, we present a multiagent framework for real-time large-scale 3-D reconstruction applications. In SLAM, researchers usually build and update a 3-D map after applying nonlinear pose graph optimization techniques. Moreover, many multiagent systems are prevalently using odometry information from additional sensors. These methods generally involve extensive computer vision algorithms and are tightly coupled with various sensors. We develop a generic method for the key challenging scenarios in multiagent 3-D mapping based on different camera systems. The proposed framework performs actively in terms of localizing each agent after the first loop closure between them. It is shown that the proposed system only uses monocular cameras to yield real-time multiagent large-scale localization and 3-D global mapping. Based on the initial matching, our system can calculate the optimal scale difference between multiple 3-D maps and then estimate an accurate relative pose transformation for large-scale global mapping.

Multimodal signals are powerful for emotion recognition since they can represent emotions comprehensively. In this paper, we compare the recognition performance and robustness of two multimodal emotion recognition models: deep canonical correlation analysis (DCCA) and bimodal deep autoencoder (BDAE). The contributions of this paper are three folds: 1) We propose two methods for extending the original DCCA model for multimodal fusion: weighted sum fusion and attention-based fusion. 2) We systemically compare the performance of DCCA, BDAE, and traditional approaches on five multimodal datasets. 3) We investigate the robustness of DCCA, BDAE, and traditional approaches on SEED-V and DREAMER datasets under two conditions: adding noises to multimodal features and replacing EEG features with noises. Our experimental results demonstrate that DCCA achieves state-of-the-art recognition results on all five datasets: 94.6% on the SEED dataset, 87.5% on the SEED-IV dataset, 84.3% and 85.6% on the DEAP dataset, 85.3% on the SEED-V dataset, and 89.0%, 90.6%, and 90.7% on the DREAMER dataset. Meanwhile, DCCA has greater robustness when adding various amounts of noises to the SEED-V and DREAMER datasets. By visualizing features before and after DCCA transformation on the SEED-V dataset, we find that the transformed features are more homogeneous and discriminative across emotions.

Anytime a robot manipulator is controlled via visual feedback, the transformation between the robot and camera frame must be known. However, in the case where cameras can only capture a portion of the robot manipulator in order to better perceive the environment being interacted with, there is greater sensitivity to errors in calibration of the base-to-camera transform. A secondary source of uncertainty during robotic control are inaccuracies in joint angle measurements which can be caused by biases in positioning and complex transmission effects such as backlash and cable stretch. In this work, we bring together these two sets of unknown parameters into a unified problem formulation when the kinematic chain is partially visible in the camera view. We prove that these parameters are non-identifiable implying that explicit estimation of them is infeasible. To overcome this, we derive a smaller set of parameters we call Lumped Error since it lumps together the errors of calibration and joint angle measurements. A particle filter method is presented and tested in simulation and on two real world robots to estimate the Lumped Error and show the efficiency of this parameter reduction., 18 pages, 13 figures, and 3 tables

The suppression of artifact noise in computed tomography (CT) with a low-dose scan protocol is challenging. Conventional statistical iterative algorithms can improve reconstruction but cannot substantially eliminate large streaks and strong noise elements. In this paper, we present a 3D cascaded ResUnet neural network (Ca-ResUnet) strategy with modified noise power spectrum loss for reducing artifact noise in low-dose CT imaging. The imaging workflow consists of four components. The first component is filtered backprojection (FBP) reconstruction via a domain transformation module for suppressing artifact noise. The second is a ResUnet neural network that operates on the CT image. The third is an image compensation module that compensates for the loss of tiny structures, and the last is a second ResUnet neural network with modified spectrum loss for fine-tuning the reconstructed image. Verification results based on American Association of Physicists in Medicine (AAPM) and United Image Healthcare (UIH) datasets confirm that the proposed strategy significantly reduces serious artifact noise while retaining desired structures.

Pedestrian motion trajectory prediction is an important task in intelligent driving, and it can provide a valuable reference for the subsequent path decision of intelligent driving. However, so far, there are only a few models in the field of specific pedestrian motion track prediction in intelligent driving from far shot first-person perspective video. To accomplish this task, we proposed a deep learning model for pedestrian motion trajectory prediction from far shot first-person perspective video with four key innovations: a) A macroscopic pedestrian trajectory prediction module is established under the close correlation between neighboring frames to estimate the pedestrian motion track on the whole; b) A relative motion transformation module of vehicle-mounted camera is designed to consider the effect of vehicle-mounted camera's ego-motion on the pedestrian motion track; c) We set up a circular training module to maintain the number of parameters in our model to simplify and reduce the size of model; d) A new far shot first-person pedestrian motion dataset under intelligent driving is specifically established to train and test the proposed model. The above four modules are integrated into the proposed deep learning model, which achieves state-of-the-art results for predicting pedestrian motion trajectory from both far and close shot first-person perspective video.

Modifying facial attributes without the paired dataset proves to be a challenging task. Previously, approaches either required supervision from a ground-truth transformed image or required training a separate model for mapping every pair of attributes. These limit the scalability of the models to accommodate a larger set of attributes since the number of models that we need to train grows exponentially large. Another major drawback of the previous approaches is the unintentional gain of the identity of the person as they transform the facial attributes. We propose a method that allows for controllable and identity-aware transformations across multiple facial attributes using only a single model. Our approach is to train a generative adversarial network (GAN) with a multitask conditional discriminator that recognizes the identity of the face, distinguishes real images from fake, as well as identifies facial attributes present in an image. This guides the generator into producing an output that is realistic while preserving the person’s identity and facial attributes. Through this framework, our model also learns meaningful image representations in a lower dimensional latent space and semantically associate separate parts of the encoded vector with both the person’s identity and facial attributes. This opens up the possibility of generating new faces and other transformations such as making the face thinner or chubbier. Furthermore, our model only encodes the image once and allows for multiple transformations using the encoded vector. This allows for faster transformations since it does not need to reprocess the entire image for every transformation. We show the effectiveness of our proposed method through both qualitative and quantitative evaluations, such as ablative studies, visual inspection, and face verification. Competitive results are achieved compared to the main competition (CycleGAN), however, at great space and extensibility gain by using a single model.

In this work, the Optimal Homotopy Asymptotic Method (OHAM) is prolifically implemented to find the optimal solutions of fractional order of fuzzy differential equations. We inspect the competence of the method by examining fractional order time-dependent first order fuzzy initial value problems in the Caputo derivative sense. The concepts of fuzzy sets theory and fractional calculus were used to present a new general fuzzification formulation of the OHAM method followed by fuzzy analysis for the proposed fractional problems are presented in detail. OHAM is recognized as a feasible and convergent method for solving linear and nonlinear models, and it provides a convenient technique for controlling the convergence of approximate solutions optimally. The method's capability is demonstrated by obtaining approximate solutions to linear and nonlinear fuzzy initial value problems in various orders of fractional derivative. The numerical results acknowledge that OHAM is a feasible and transformation mechanism for solving linear and nonlinear fuzzy fractional initial value problems. Also, the results ensure that the method used was succinct, efficient, and simple to use, to deal with a more comprehensive fuzzy fractional differential equation.

This paper focuses on fixed-time formation control (FTFC) of a fully-actuated surface vessel (FASV) considering complex unknowns, including fully unknown dynamics and disturbances, input saturation and time-varying delays. First, using prediction idea to address time delay, a novel state predictor (SP) strategy combining with state transformation (ST) technique is devised for each FASV to predict the evolution of system states such that fixed-time stability can be ensured while solving the delay problem. Besides, the uncertainties in the transformed system are attentively considered. In addition, aiming to distinctly identify complex unknowns, predictor-based neural network is injected into the foregoing delay processing method. Finally, using time base generator (TBG), a new adaptive terminal sliding mode (ATSM) is incorporated into FTFC strategy which in turn contributes to decreasing control inputs and acquiring smooth convergence process. Simulation results and comparisons are thoroughly provided to testify the effectiveness and superiority of the designed FTFC scheme.

Orthogonal time frequency space (OTFS) system constitutes an effective structure conceived for efficiently utilizing the channel information, which is capable of achieving a promising transmission performance in high-mobility environment. To extract enough channel diversity, a two-dimensional Fourier transformation combined with a pulse shape is designed at the OTFS transmitter. Consequently, the amplitude of OTFS signals may fluctuate drastically, owing to the combined dependency of the OTFS transformation and the pulse shape. To quantify the amplitude fluctuation, we investigate the peak-to-average power ratio (PAPR) of OTFS signals, for a large amount of data in the delay-Doppler domain. We first reveal that when the number of data points approaches to infinity, based on central limit theorems for dependent variables, the complexvalued OTFS signals weakly converge to a Gaussian distribution. Then, according to the extremal theory of the Chi-squared process for stationary OTFS signals, an accurate expression of the PAPR distribution is derived, depending on the transmit pulse and the number of data points. It is also demonstrated that upon modifying the exponential factor, the analytical PAPR expression is applicable for the non-stationary Gaussian distribution caused by the bandlimited pulse with a large roll-off factor. Simulation results confirm the accuracy of the analytical PAPR probability for practical conditions.

As the number of people involved in a decision-making problem increases, the complexity of the group decision-making (GDM) process increases accordingly. The size of participants and the heterogeneous information have important effects on the consensus reaching process in GDM. To deal with these two issues, traditional methods divide large groups into smaller ones to reduce the scale of GDM and translate heterogeneous information into a uniform format to handle the heterogeneity problem. These methods face two challenges: 1) how to determine the appropriate group size? and 2) how to avoid or reduce loss of information during the transformation process? To address these two challenges, this article uses fuzzy cluster analysis to integrate heterogeneous information for large-scale GDM problems. First, a large group is divided into smaller ones using fuzzy cluster analysis and the F-statistic is applied to determine the satisfactory number of clusters. The original information is retained based on the similarity degree. Then, a consensus reaching process is conducted within these small groups to form a unified opinion. A feedback mechanism is developed to adjust the small GDM matrix when any group cannot reach a consensus, and the heterogeneous technique for order preference by similarity to an ideal solution (TOPSIS) is used to select the best alternative. To validate the proposed approach, an experiment study is conducted using a practical example of selecting the best rescue plan in an emergency situation. The result shows that the proposed approach helps to choose the best rescue plan faster.

An integrated methodology for the design of an Unmanned Aerial Vehicle (UAV) derivative has been developed. The proposed methodology utilized the Multidisciplinary Design Optimization (MDO) for the derivative optimization and utilized flight simulation for virtual flight tests. Derivative design reduced the development time and the use of flight simulator allowed quick verification of the results. In this research, Found Aircraft Expedition E350 aircraft was selected as the baseline for the UAV derivative. Empirical equations were used for the optimization process where the results were organized for easy transformation into a flight simulation model. An in house program was developed to convert raw simulation data for flight-data analysis. The optimization result yielded an improvement on the endurance of the aircraft. The flight simulation result for the original aircraft demonstrated agreement with the chosen aircraft. The application of the process demonstrated that using MDO and flight simulation was a viable method for developing a UAV derivative.

In this paper, a quantized control capable of appointed-time performances for quadrotor attitude tracking is investigated. A continuous and piecewise behavior boundary, rather than an exponential decaying function employed in traditional pre-scribed performance control (PPC), is devised using error transformation to enforce attitude convergence to pre-assigned transient and steady zones in an appointed time. To circumvent the inertia moment uncertainties and external perturbations, an extended state observer (ESO) with only one parameter to be tuned is introduced to precisely estimate the total disturbances acting on angular rate subsystem. Addi-tionally, a hysteretic quantizer (HQ) capable of converting continuous control behaviors to finite discrete scalars is es-tablished in controller to actuator channel to reduce the transmission burden and amount of sampling data. With a novel quantization decomposition, an attitude quantized con-troller is integrated in terms of a guaranteed tracking. The prominent feature is that a prescribed appointed-time attitude response can be preserved with a decreased demand of communication bandwidth, despite of uncertainties. Theoreti-cal analysis shows that all error signals are ultimately uni-formly bounded. Eventually, the superiority of explored con-trol strategy is demonstrated through experimental results.

Controlling interpenetration and flexibility behaviors is intriguing and fundamental for porous coordination polymers. We report exceptional interpenetration behaviors involving controllable partia...