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The purpose of this paper is to present a transition detector (TD)-based radiation hardened flip-flop (TDRH-FF) for single event upset (SEU).

With SEU recovery and single event transient (SET) detector mechanism, the TDRH-FF can tolerate SEU during hold mode and generate a warning signal for architecture-level recovery during transport mode when input signal contains SET. Evaluation results show that the TDRH-FF outperforms comparable comprehensive performance.

Simulation results show that 1) the mean pulse width of the correction glitches (at full width half maximum) of TDRH-FF is less than 10 ps; 2) the area overhead of TDRH-FF is similar to the EVFERST-FF, BISER-FF and DNURHL-FF; 3) TDRH-FF has the same average power consumption as SETTOF, and moderate PDP and Ps values among these compared FFs.

In this paper, a TD-based TDRH-FF is proposed to solve the problems in the previous design. And the main contributions of the proposed TDRH-FF are summarized: Minimum size transistors are used in the proposed TD which leads to a considerable decrease in area overheads and propagation delay (resulting in an ignorable correction glitch); and compared with other radiation hardened flip-flop, TDRH-FF outperforms comparable comprehensive performance.

The purpose of this paper is to present a general formulation of the quadrature element method (QEM). The method is then used to investigate the free vibration of functionally graded (FG) beams with general boundary conditions and different variations of material properties.

The quadrature elements with arbitrary number of nodes and nodal distributions are established on the basis of two types of FG Timoshenko beam theories. One called TBT-1 takes the cross-sectional rotation as the unknown function and the other called TBT-2 uses the transverse shear strain as the unknown function. Explicit formulas are provided via the help of the differential quadrature (DQ) rule and thus the elements can be implemented adaptively with ease.

The suitability and computational efficiency of the proposed quadrature elements for the vibration analysis of FG beams are demonstrated. The convergence rate of the proposed method is high. The elements are shear-locking free and can yield accurate solutions with a small number of nodes for both thin and moderately thick beams. The performance of the element based on TBT-1 is better than the one based on TBT-2.

The present QEM is different from the existing one which exclusively uses Gauss–Lobatto–Legendre (GLL) nodes and GLL quadrature and thus is more general. The element nodes can be either the same or different from the integration points, making the selection of element nodes more flexible. Presented data are accurate and may be a reference for other researchers to develop new numerical methods. The QEM may be also useful in multi-scale modeling and in the analysis of civil infrastructures.

Recently, the double joint photographic experts group (JPEG) compression detection tasks have been paid much more attention in the field of Web image forensics. Although there are several useful methods proposed for double JPEG compression detection when the quantization matrices are different in the primary and secondary compression processes, it is still a difficult problem when the quantization matrices are the same. Moreover, those methods for the different or the same quantization matrices are implemented in independent ways. The paper aims to build a new unified framework for detecting the doubly JPEG compression.

First, the Y channel of JPEG images is cut into 8 × 8 nonoverlapping blocks, and two groups of features that characterize the artifacts caused by doubly JPEG compression with the same and the different quantization matrices are extracted on those blocks. Then, the Riemannian manifold learning is applied for dimensionality reduction while preserving the local intrinsic structure of the features. Finally, a deep stack autoencoder network with seven layers is designed to detect the doubly JPEG compression.

Experimental results with different quality factors have shown that the proposed approach performs much better than the state-of-the-art approaches.

To verify the integrity and authenticity of Web images, the research of double JPEG compression detection is increasingly paid more attentions.

This paper aims to propose a unified framework to detect the double JPEG compression in the scenario whether the quantization matrix is different or not, which means this approach can be applied in more practical Web forensics tasks.

In the online marketing context, virtual reality (VR) has been used to display products and shopping environment, which effectively enhances the consumer experience. VR technology can not only recreate scenes similar to the real world (virtual authenticity, VA) but also create fictitious and desirable scenes that do not exist in the real world (virtual ideality, VI). The differences between VA and VI in influencing consumers' responses have not been fully understood. In addition, social signals have appeared in VR apps. However, the role of social signals in a VR context has rarely been studied. To fill the gaps in the literature, this study investigates the roles of VA and VI in shaping consumers' responses, as well as whether and how their effectiveness in shaping consumers' responses is influenced by social signals.

A VR real estate service platform was selected as the experimental platform, and two experiments were conducted to test the hypotheses. The ANOVAs and regressions were used for data analysis.

Results show that VA leads to a higher level of perceived diagnosticity than VI, whereas VI leads to a higher level of inspiration than VA; perceived diagnosticity and inspiration positively affect visit intention. Furthermore, the relationship between consumers' perceived diagnosticity, inspiration and visit intention is moderated by the presence of social signals.

The study revealed the differences between VA and VI in shaping consumers' responses, as well as the effect of social signals in VR environment, which provide a new perspective for future VR research in the context of interactive marketing.

The purpose of this paper is to construct a multiwing chaotic system that has hidden attractors with multiple stable equilibrium points. Because the multiwing hidden attractors chaotic systems are safer and have more dynamic behaviors, it is necessary to construct such a system to meet the needs of developing engineering.

By introducing a multilevel pulse function into a three-dimensional chaotic system with two stable node–foci equilibrium points, a hidden multiwing attractor with multiple stable equilibrium points can be generated. The switching behavior of a hidden four-wing attractor is studied by phase portraits and time series. The dynamical properties of the multiwing attractor are analyzed via the Poincaré map, Lyapunov exponent spectrum and bifurcation diagram. Furthermore, the hardware experiment of the proposed four-wing hidden attractors was carried out.

Not only unstable equilibrium points can produce multiwing attractors but stable node–foci equilibrium points can also produce multiwing attractors. And this system can obtain 2N + 2-wing attractors as the stage pulse of the multilevel pulse function is N. Moreover, the hardware experiment matches the simulation results well.

This paper constructs a new multiwing chaotic system by enlarging the number of stable node–foci equilibrium points. In addition, it is a nonautonomous system that is more suitable for practical projects. And the hardware experiment is also given in this article which has not been seen before. So, this paper promotes the development of hidden multiwing chaotic attractors in nonautonomous systems and makes sense for applications.

The purpose of this paper is to present a novel registration method for augmented reality (AR) systems based on robust estimation of trifocal tensor using point and line correspondence simultaneously.

The proposed method distinguishes itself in following three ways: first, to establish the world coordinate system, the restriction that the four specified points must form an approximate square is relaxed, the only requirement is that these four points should not be collinear. Second, besides feature points, line segments are also used to calculate the needed trifocal tensors. The registration process can still be achieved even without the use of feature points. Third, to estimate trifocal tensors precisely, progressive sample consensus (PROSAC) is used instead of random sample consensus to remove outliers.

As shown in the experiments, the proposed method really enhances the usability of this system. To calculate trifocal tensor, a PROSAC based algebraic minimization algorithm is put forward which improves the accuracy and reduces the computation complexity.

In current system, it is stipulated that there is no large rotation of the user's head relative to the registration scenes, because the NCC will degrade when there is a large rotation between images.

A more robust feature matching strategy is needed. Treating feature matching as a classification problem may be a good choice.

This paper presents a novel registration approach for AR system.