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

Embedded Sensor System for Five-degree-of-freedom Error Detection on Machine Tools

Any linear stage of machine tool has inherent six-degree-of-freedom (6-DOF) geometric errors. Its motion control system, however, has only the position feedback. Moreover, the feedback point is not the commanded cutting point. This is the main reason why the positioning error along each axis and the volumetric error in the working space are inevitable. This paper presents a compact 5-DOF sensor system that can be embedded in each axis of motion as additional feedback sensors of the machine tool for the detection of three angular errors and two straightness errors. Using the derived volumetric error model, the feedback point can be transferred to the cutting point. The design principle of the developed 5-DOF sensor system is described. An in-depth study of systematic error compensation due to crosstalk of straightness error and angular error is analyzed. A prototype has been built into a three-axis NC milling machine. The results of a series of the comparison experiments demonstrate the feasibility of the developed sensor system

AC-CDCN:A Cross-Subject EEG Emotion Recognition Model with Anti-Collapse Domain Generalization

Emotion recognition is a critical area in brain-computer interfaces, with electroencephalography (EEG) shown to be effective for emotional analysis. In domain generalization, cross subject emotion recognition encounters significant generalization challenges, including excessive feature collapse and insufficient capture of EEG features. To tackle these issues, we propose an Anti-Collapse Cross-Domain Consistency Network (AC-CDCN), which leverages Maximum Mean Discrepancy (MMD) to reduce distribution discrepancies between source domains, facilitating the capture of domain-invariant features, and innovatively introduces an Anti-Feature Collapse Strategy (AFCS), which incorporates an Anti-Collapse Domain Discriminator (ACDD) and the code rate loss function, effectively preventing excessive feature collapse. Furthermore, we propose a Flexible Feature Rebalance Module (FlexiReMod), a plug-and-play component that enhances generalization and dynamic feature capture through feature fusion and attention mechanisms. Experimental results indicate AC-CDCN achieved 87.14% (±5.60) and 71.77% (±12.92) accuracy on SEED and SEED-IV datasets, underscoring its significant generalization advantage

Error estimates of a high order numerical method for solving linear fractional differential equations

In this paper, we first introduce an alternative proof of the error estimates of the numerical methods for solving linear fractional differential equations proposed in Diethelm [6] where a first-degree compound quadrature formula was used to approximate the Hadamard finite-part integral and the convergence order of the proposed numerical method is O(∆t 2−α ), 0 < α < 1, where α is the order of the fractional derivative and ∆t is the step size. We then use the similar idea to prove the error estimates of a high order numerical method for solving linear fractional differential equations proposed in Yan et al. [37], where a second-degree compound quadrature formula was used to approximate the Hadamard finite-part integral and we show that the convergence order of the numerical method is O(∆t 3−α ), 0 < α < 1. The numerical examples are given to show that the numerical results are consistent with the theoretical results