We propose an online spatiotemporal articulation model estimation framework that estimates both articulated structure as well as a temporal prediction model solely using passive observations. The resulting model can predict future motions of an articulated object with high confidence because of the spatial and temporal structure. We demonstrate the effectiveness of the predictive model by incor...

Motion compensated wavelet (MC-Wavelet) video coding provides high coding efficiency and robustness along with spatio-temporal-SNR-complexity scalability. Such schemes use a multi-resolution motion compensated temporal filtering (MCTF) decomposition along with the spatial wavelet transform to remove video redundancy. These multi-resolution transforms partition the spatiotemporal sequence-volume...

The near-field propagation behavior of terahertz (THz) pulses generated by a planar large-aperture photoconducting THz transmitter has been characterized. A simulation model based on Huygens-Fresnel diffraction theory has been developed that permits accurate prediction of the spatiotemporal profiles of the THz beam everywhere and gives excellent agreement with experimental measurements. Two key...

Informed driving is becoming a key feature to increase the sustainability of taxi companies. Some recent works are exploring the data broadcasted by each vehicle to provide live information for decision making. In this paper, we propose a method to employ a learning model based on historical GPS data in a real-time environment. Our goal is to predict the spatiotemporal distribution of the Taxi-...

Abstract. This article studies the growth of prediction error over lead time in a schematic model atmospheric transport. Inspired by Lorenz (2005) system, we mimic an variable one dimension, which can be decomposed into three spatiotemporal scales. We identify parameter values that provide scaling and chaotic behavior. Instead exponential forecast time, observe more complex test power law quadr...

The maneuvering characteristics and range of motion real aircraft are highly uncertain, which significantly increases the difficulty trajectory prediction. To solve problem that high-speed maneuvers excessive trajectories in airspace cause a decrease prediction accuracy to find out laws hidden large number trajectories, this paper proposes deep learning algorithm based on clustering spatiotempo...

We present several variational approaches for fluid flow estimation from image sequences in experimental fluid dynamics. These approaches enable the contextual data analysis of particle images based on physical constraints, including bounds on the variation of divergence and vorticity of flow patterns, vanishing divergence for incompressible flows, and iterative estimation-prediction schemes ba...

We examine numerically the full spatiotemporal correlation functions for all hydrodynamic quantities for the random collision model introduced recently. The autocorrelation function of the heat current, through the Kubo formula, gives a thermal conductivity exponent of 1/3 in agreement with the analytical prediction and previous numerical work. Remarkably, this result depends crucially on the c...

Nowadays data acquisition methods continuously record all sort of events occurring in the physical world. Improvements on both hardware and software technologies allow us to collect huge amounts of data, producing, every day, more complete, accurate and detailed pictures of human activity and interaction with the environment. General purpose tools are required for motion patterns, detection of ...

Kink dynamics of the damped Frenkel-Kontorova (discrete sine-Gordon) chain driven by a constant external force are investigated. Resonant steplike transitions of the average velocity occur due to the competitions between the moving kinks and their radiated phasonlike modes. A mean-field consideration is introduced to give a precise prediction of the resonant steps. Slip-stick motion and spatiot...