H

This paper discusses the H- index problem for stochastic linear discrete-time systems. A necessary and sufficient condition of H- index is given for such systems in finite horizon. It is proved that when the H- index is greater than a given value, the feasibility of H- index is equivalent to the solvability of a constrained difference equation. The above result can be applied to the fault detection observer design. Finally, some examples are presented to illustrate the proposed theoretical results

Triple-source saddle-curve cone-beam photon counting CT image reconstruction:A simulation study

Purpose: The most common detector material in the PC CT system, cannot achieve the best performance at a relatively higher photon flux rate. In the reconstruction view, the most commonly used filtered back projection, is not able to provide sufficient reconstructed image quality in spectral computed tomography (CT). Developing a triple-source saddle-curve cone-beam photon counting CT image reconstruction method can improve the temporal resolution. Methods: Triple-source saddle-curve cone-beam trajectory was rearranged into four trajectory sets for simulation and reconstruction. Projection images in different energy bins were simulated by forward projection and photon counting CT respond model simulation. After simulation, the object was reconstructed using Katsevich's theory after photon counts correction using the pseudo inverse of photon counting CT response matrix. The material decomposition can be performed based on images in different energy bins. Results: Root mean square error (RMSE) and structural similarity index (SSIM) are calculated to quantify the image quality of reconstruction images. Compared with FDK images, the RMSE for the triple-source image was improved by 27%, 21%, 14%, 8%, and 6% for the reconstrued image of 20–33, 33–47, 47–58, 58–69, 69–80 keV energy bin. The SSIM was improved by 1.031%, 0.665%, 0.396%, 0.235%, 0.174% for corresponding energy bin. The decomposition image based on corrected images shows improved RMSE and SSIM, each by 33.861% and 0.345%. SSIM of corrected decomposition image of iodine reaches 99.415% of the original image. Conclusions: A new Triple-source saddle-curve cone-beam PC CT image reconstruction method was developed in this work. The exact reconstruction of the triple-source saddle-curve improved both the image quality and temporal resolution

Implicit Motion-Compensated Network for Unsupervised Video Object Segmentation

Unsupervised video object segmentation (UVOS) aims at automatically separating the primary foreground object(s) from the background in a video sequence. Existing UVOS methods either lack robustness when there are visually similar surroundings (appearance-based) or suffer from deterioration in the quality of their predictions because of dynamic background and inaccurate flow (flow-based). To overcome the limitations, we propose an implicit motion-compensated network (IMCNet) combining complementary cues ($\textit{i.e.}$, appearance and motion) with aligned motion information from the adjacent frames to the current frame at the feature level without estimating optical flows. The proposed IMCNet consists of an affinity computing module (ACM), an attention propagation module (APM), and a motion compensation module (MCM). The light-weight ACM extracts commonality between neighboring input frames based on appearance features. The APM then transmits global correlation in a top-down manner. Through coarse-to-fine iterative inspiring, the APM will refine object regions from multiple resolutions so as to efficiently avoid losing details. Finally, the MCM aligns motion information from temporally adjacent frames to the current frame which achieves implicit motion compensation at the feature level. We perform extensive experiments on $\textit{DAVIS}_{\textit{16}}$ and $\textit{YouTube-Objects}$. Our network achieves favorable performance while running at a faster speed compared to the state-of-the-art methods.Comment: Accepted by IEEE Transactions on Circuits and Systems for Video Technology (TCSVT

IEBins: Iterative Elastic Bins for Monocular Depth Estimation

Monocular depth estimation (MDE) is a fundamental topic of geometric computer vision and a core technique for many downstream applications. Recently, several methods reframe the MDE as a classification-regression problem where a linear combination of probabilistic distribution and bin centers is used to predict depth. In this paper, we propose a novel concept of iterative elastic bins (IEBins) for the classification-regression-based MDE. The proposed IEBins aims to search for high-quality depth by progressively optimizing the search range, which involves multiple stages and each stage performs a finer-grained depth search in the target bin on top of its previous stage. To alleviate the possible error accumulation during the iterative process, we utilize a novel elastic target bin to replace the original target bin, the width of which is adjusted elastically based on the depth uncertainty. Furthermore, we develop a dedicated framework composed of a feature extractor and an iterative optimizer that has powerful temporal context modeling capabilities benefiting from the GRU-based architecture. Extensive experiments on the KITTI, NYU-Depth-v2 and SUN RGB-D datasets demonstrate that the proposed method surpasses prior state-of-the-art competitors. The source code is publicly available at https://github.com/ShuweiShao/IEBins.Comment: Accepted by NeurIPS 202

Stability of Nonlinear Stochastic Discrete-Time Systems

This paper studies the stability for nonlinear stochastic discrete-time systems. First of all, several definitions on stability are introduced, such as stability, asymptotical stability, and pth moment exponential stability. Moreover, using the method of the Lyapunov functionals, some efficient criteria for stochastic stability are obtained. Some examples are presented to illustrate the effectiveness of the proposed theoretical results

Exciton-Phonon Interaction Model for Singlet Fission in Prototypical Molecular Crystals

In singlet fission (SF), a spin-conserving splitting of one singlet exciton into two triplet excitation states, the transition between localized electronic states can be controlled and modulated by delocalized lattice phonons. In this work, we built an exciton–phonon (ex–ph) interaction model accounting local electronic states coupled with both local molecular vibrations and low frequency intermolecular phonon modes for SF in crystalline tetracene and rubrene. On the basis of the calculated electronic couplings at the equilibrium structure of the molecular dimer, a superexchange path for SF was found for tetracene while couplings between the triplet pair (TT) state and other diabatic states are zero for rubrene due to the high symmetry. Our further ex–ph spectral density analysis and quantum dynamics simulation based on our ex–ph interaction model suggested a thermal-activated mechanism for SF in rubrene crystal via symmetry breaking by nuclear vibration, which is in agreement with recent experiments. It is also shown that thermal fluctuations of electronic couplings in both tetracene and rubrene are mostly in the same order of magnitude at room temperature, and this could be one of the reasons for both tetracene and rubrene to exhibit SF time scales within a close range (hundreds to thousands of femtoseconds) in experiments