Reliable camera motion estimation from compressed MPEG videos using machine learning approach

As an important feature in characterizing video content, camera motion has been widely applied in various multimedia and computer vision applications. A novel method for fast and reliable estimation of camera motion from MPEG videos is proposed, using support vector machine for estimation in a regression model trained on a synthesized sequence. Experiments conducted on real sequences show that the proposed method yields much improved results in estimating camera motions while the difficulty in selecting valid macroblocks and motion vectors is skipped

An Image Compression Scheme Based on Fuzzy Neural Network

Image compression technology is to compress the redundancy between the pixels to reduce the transmission broadband and storage space by using the correlation of the image pixels. Fuzzy neural network effectively integrates neural network technology and fuzzy technology; combines learning, self-adaptivity, imagination and identity and uses rule-based reasoning and fuzzy information processing in the nodes; thus greatly improving the transparency of fuzzy neural network. This paper mainly investigates the applications of fuzzy neural network in image compression and realizes the image compression and reconstruction of fuzzy neural network. It is demonstrated in the simulation experiment that the image compression algorithm based on fuzzy neural network has significant advantages in training speed, compression quality and robustness

Evolution and control of the phase competition morphology in a manganite film

The competition among different phases in perovskite manganites is pronounced since their energies are very close under the interplay of charge, spin, orbital and lattice degrees of freedom. To reveal the roles of underlying interactions, many efforts have been devoted towards directly imaging phase transitions at microscopic scales. Here we show images of the charge-ordered insulator (COI) phase transition from a pure ferromagnetic metal with reducing field or increasing temperature in a strained phase-separated manganite film, using a home-built magnetic force microscope. Compared with the COI melting transition, this reverse transition is sharp, cooperative and martensitic-like with astonishingly unique yet diverse morphologies. The COI domains show variable-dimensional growth at different temperatures and their distribution can illustrate the delicate balance of the underlying interactions in manganites. Our findings also display how phase domain engineering is possible and how the phase competition can be tuned in a controllable manner.Comment: Published versio

Research of Proxy Cache Algorithm in Multi-media Education System

Multi-media education system is more and more widely used in all levels of education. In order to decrease cost of multi-media system and keep efficiency with increasing multi-media materials, proxy cache algorithm has been widely studied. Based on analysis of existing research of proxy cache results, an improved proxy coaching strategy of prefix cache and postfix merging is proposed. The strategy can dynamically adjust prefix cache size with the object access change. A more effective method of steaming merging has been proposed with multicast used in postfix portion. The results show that the improved strategy can effectively utilize proxy cache resource, shorten time delay and save band width

Numerical Methods for Solving Space Fractional Partial Differential Equations Using Hadamard Finite-Part Integral Approach

From Springer Nature via Jisc Publications RouterHistory: received 2018-09-29, rev-recd 2018-11-09, accepted 2018-11-10, registration 2019-06-11, epub 2019-07-26, online 2019-07-26, ppub 2019-12Publication status: PublishedAbstract: We introduce a novel numerical method for solving two-sided space fractional partial differential equations in two-dimensional case. The approximation of the space fractional Riemann–Liouville derivative is based on the approximation of the Hadamard finite-part integral which has the convergence order O(h3-α), where h is the space step size and α∈(1, 2) is the order of Riemann–Liouville fractional derivative. Based on this scheme, we introduce a shifted finite difference method for solving space fractional partial differential equations. We obtained the error estimates with the convergence orders O(τ+h3-α+hβ), where τ is the time step size and β>0 is a parameter which measures the smoothness of the fractional derivatives of the solution of the equation. Unlike the numerical methods for solving space fractional partial differential equations constructed using the standard shifted Grünwald–Letnikov formula or higher order Lubich’s methods which require the solution of the equation to satisfy the homogeneous Dirichlet boundary condition to get the first-order convergence, the numerical method for solving the space fractional partial differential equation constructed using the Hadamard finite-part integral approach does not require the solution of the equation to satisfy the Dirichlet homogeneous boundary condition. Numerical results show that the experimentally determined convergence order obtained using the Hadamard finite-part integral approach for solving the space fractional partial differential equation with non-homogeneous Dirichlet boundary conditions is indeed higher than the convergence order obtained using the numerical methods constructed with the standard shifted Grünwald–Letnikov formula or Lubich’s higher order approximation schemes

Understanding the diffusion models by conditional expectations

This paper provide several mathematical analyses of the diffusion model in machine learning. The drift term of the backwards sampling process is represented as a conditional expectation involving the data distribution and the forward diffusion. The training process aims to find such a drift function by minimizing the mean-squared residue related to the conditional expectation. Using small-time approximations of the Green's function of the forward diffusion, we show that the analytical mean drift function in DDPM and the score function in SGM asymptotically blow up in the final stages of the sampling process for singular data distributions such as those concentrated on lower-dimensional manifolds, and is therefore difficult to approximate by a network. To overcome this difficulty, we derive a new target function and associated loss, which remains bounded even for singular data distributions. We illustrate the theoretical findings with several numerical examples

The anti-sepsis activity of the components of Huanglian Jiedu Decoction with high lipid A-binding affinity

Huanglian Jiedu Decoction (HJD), one of the classic recipes for relieving toxicity and fever, is a common method for treating sepsis in China. However, the effective components of HJD have not yet been identified. This experiment was carried out to elucidate the effective components of HJD against sepsis. Thus, seven fractions from HJD were tested using a biosensor to test their affinity for lipid A. The components obtained that had high lipid A-binding fractions were further separated, and their affinities to lipid A were assessed with the aid of a biosensor. The levels of LPS in the blood were measured, and pathology experiments were conducted. The LPS levels and mRNA expression analysis of TNF-α and IL-6 of the cell supernatant and animal tissue were evaluated to investigate the molecular mechanisms. Palmatine showed the highest affinity to lipid A and was evaluated by in vitro and in vivo experiments. The results of the in vitro and in vivo experiments indicated that the levels of LPS, TNF-α and IL-6 of the palmatine group were significantly lower than those of the sepsis model group (p \u3c 0.01). The group treated with palmatine showed strong neutralizing LPS activity in vivo. The palmatine group exhibited stronger protective activity on vital organs compared to the LPS-induced animal model. This verifies that HJD is a viable treatment option for sepsis given that there are multiple components in HJD that neutralize LPS, decrease the release of IL-6 and TNF-α induced by LPS, and protect vital organs

Higher Order Time Stepping Methods for Subdiffusion Problems Based on Weighted and Shifted Grünwald–Letnikov Formulae with Nonsmooth Data

Two higher order time stepping methods for solving subdiffusion problems are studied in this paper. The Caputo time fractional derivatives are approximated by using the weighted and shifted Gr\"unwald-Letnikov formulae introduced in Tian et al. [Math. Comp. 84 (2015), pp. 2703-2727]. After correcting a few starting steps, the proposed time stepping methods have the optimal convergence orders $O(k^2)$ and $O(k^3)$, respectively for any fixed time $t$ for both smooth and nonsmooth data. The error estimates are proved by directly bounding the approximation errors of the kernel functions. Moreover, we also present briefly the applicabilities of our time stepping schemes to various other fractional evolution equations. Finally, some numerical examples are given to show that the numerical results are consistent with the proven theoretical results