1617013
Motion Path Synthesis for Intelligent Avatar
In this paper, we present a new motion generation technique, called motion path synthesis. The objective is to generate a motion for an intelligent avatar to move along a planned route using a pre-existing motion library. First, motion primitives, each defined as a dynamic motion segment, are extracted from the motion library. Then a motion graph is constructed based on the motion primitives and their connectivities. Within this motion graph, the desired realistic motion for avatars can be synthesized through a two-stage process: search an optimal motion path within the motion graph, joint the motion path and adapt it to the route. The experiment shows the effectiveness of the presented technique.
111120849
Generic Carrier-Based Core Model for Four-Terminal Double-Gate MOSFET Valid for Symmetric, Asymmetric, SOI, and Independent Gate Operation Modes
A generic carrier-based core model for undoped four-terminal double-gate (DG) MOSFET valid for symmetric, asymmetric, SOI, and independent gate operation modes is presented in this paper. Based on the exact solution of the 1-D Poisson's equation of a general DG-MOSFET configure, a generic drain current model is derived from Pao-Sah's double integral in terms of the carrier concentration. The model is verified by extensive comparisons with 2-D numerical simulations under different bias conditions to all four terminals. The concise mathematic formulation allows the unification various double-gate models into a carrier-based core model for compact DG-MOSFET model development.
7524507
Video retargeting: automating pan and scan
When a video is displayed on a smaller display than originally intended, some of the information in the video is necessarily lost. In this paper, we introduce Video Retargeting that adapts video to better suit the target display, minimizing the important information lost. We define a framework that measures the preservation of the source material, and methods for estimating the important information in the video. Video retargeting crops each frame and scales it to fit the target display. An optimization process minimizes information loss by balancing the loss of detail due to scaling with the loss of content and composition due to cropping. The cropping window can be moved during a shot to introduce virtual pans and cuts, subject to constraints that ensure cinematic plausibility. We demonstrate results of adapting a variety of source videos to small display sizes.
39158542
A New Algorithm of Gray Watermark Embedding
The problems of the digital information transmission safety, its’ copyright protection etc. are seriously increase. Digital watermark is one of the good solutions for many problems mentioned above. Generally the watermark information is a one-dimension random signal. Two-dimension gray watermark is difficult to guarantee the robustness of the information embedding. Put a new kind of discrete wavelet transform (DWT) and the singular value decompose (SVD) on two-dimension gray watermark, the watermark becomes very strong robust to the compression, noise, low pass filter, high pass filter and median filter. It is useful for the safety of digital information transmission and its copyright protection.
29221183
Intelligent Crowd Simulation
Crowd simulation is a continuous challenge in computer animation. In this paper, we present an intelligent crowd simulation technique based on multiple autonomous agents. Each animated character (AC) is modeled as an autonomous agent, consisting of the perception, behavior planning and motion generation system. AC perceives the environment information, generates intentions and plans behaviors autonomously. To execute behaviors, appropriate motions are selected from the fundamental motion library (FML) and edited to create required motions. And the crowd behavior can be regarded as a combination of the individual behavior of each AC. The experiment shows the feasibility of this technique.
8081608
An analytic model for Ge/Si core/shell nanowire MOSFETs considering drift-diffusion and ballistic transport
In this paper an analytic model for Ge/Si core/shell nanowire MOSFETs (NWFETs) is developed. First, the electrostatic potential and charge model are derived out from classical device physics. Then the drift-diffusion drain current model is obtained and verified by comparisons with the numerical simulation. The ballistic current model is obtained with the approximately described quantummechanical effect through modifying the intrinsic carrier concentration under 2-dimensional confinement. With the proposed model, the performances of Ge/Si core/shell NWFETs are analyzed and significant characteristics are demonstrated in details. The analytic model in this paper provides a framework for further developing compact models of the NWFETs with Ge/Si core/shell heterostructure for circuit design and simulation.
2283116
A Practical OpenMP Compiler for System on Chips
With the advent of modern System-on-Chip (SOC) design, the integration of multiple-processors into one die has become the trend. By far there are no standard programming paradigms for SOCs or heterogeneous chip multiprocessors. Users are required to write complex assembly language and/or C programs for SOCs. Developing a standard programming model for this new parallel architecture is necessary. In this paper, we propose a practical OpenMP compiler for SOCs, especially targeting 3SoC. We also present our solutions to extend OpenMP directives to incorporate advanced architectural features of SOCs. Preliminary performance evaluation shows scalable speedup using different types of processors and effectiveness of performance improvement through optimization.
10188810
Estimation Distribution of Algorithm for Fuzzy Clustering Gene Expression Data
With the rapid development of genome projects, clustering of gene expression data is a crucial step in analyzing gene function and relationship of conditions. In this paper, we put forward an estimation of distribution algorithm for fuzzy clustering gene expression data, which combines estimation of distribution algorithms and fuzzy logic. Comparing with sGA, our method can avoid many parameters and can converge quickly. Tests on real data show that EDA converges ten times as fast as sGA does in clustering gene expression data. For clustering accuracy, EDA can get a more reasonable result than sGA does in the worst situations although both methods can get the best results in the best situations.
41632859
A Novel Frequency Offset Estimator over Frequency Selective Fading Channels by Using Correlative Coding
A new data-aided carrier frequency offset (CFO) estimation technique is presented for correlative coded OFDM systems in the presence of strong multipath. Different from traditional data-aided estimation techniques, the technique estimates CFO by detecting amplitude of pilots rather than their phase shift and removes effects on CFO estimation due to intercarrier interference by an iterative compensation method. A theoretical analysis of its performance has been derived and simulation results comparing the new technique with a traditional data-aided estimation technique are presented.
6688376
Frequency sensitivity in Hodgkin–Huxley systems
Abstract. The frequency sensitivity of weak periodic signal detection has been studied via numerical simulations for both a single neuron and a neuronal network. The dependence of the critical amplitude of the signal upon its frequency and a resonance between the intrinsic oscillations of a neuron and the signal could account for the frequency sensitivity. In the presence of both a subthreshold periodic signal and noise, the signal-to-noise ratio (SNR) of the output of either a single neuron or a neuronal network present the typical characteristics of stochastic resonance. In particular, there exists a frequency-sensitive range of 30–100 Hz, and for signals with frequencies within this range the SNRs have large values. This implies that the system under consideration (a single neuron or a neuronal network) is more sensitive to the detection of periodic signals, and the frequency sensitivity may be of a functional significance to signal processing.
21836071
Use clustering to improve neural network in financial time series prediction
In this paper, a time series prediction method using clustering to improve neural network is studied. The big data group is divided into some small parts by clustering. By this way, every small part has a higher conformity, and data in these small parts is used to train corresponding neural network for prediction. The prediction model is constructed from neural network with the addition of clustering and is applied to the financial time series prediction. The experiment results demonstrate the effectiveness of the improvement. Comparison with the primitive neural network prediction model shows that clustering increases neural network's trend accuracy in continuous prediction, while debasing the cost of time and reducing the complexity of the prediction model.