953854
Efficient Schemes for Monte Carlo Markov Chain Algorithms in Global Illumination
Current MCMC algorithms are limited from achieving high rendering efficiency due to possibly high failure rates in caustics perturbations and stratified exploration of the image plane. In this paper we improve the MCMC approach significantly by introducing new lens perturbation and new path-generation methods. The new lens perturbation method simplifies the computation and control of caustics perturbation and can increase the perturbation success rate. The new path-generation methods aim to concentrate more computation on "high perceptual variance" regions and "hard-to-find-but-important" paths. We implement these schemes in the Population Monte Carlo Energy Redistribution framework to demonstrate the effectiveness of these improvements. In addition., we discuss how to add these new schemes into the Energy Redistribution Path Tracing and Metropolis Light Transport algorithms. Our results show that rendering efficiency is improved with these new schemes.
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.
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.
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.
16557870
Collaboration upon Heterogeneous Platforms - from Desktop PC to Handheld Device
Nowadays, handheld wireless devices are being widely used by people to collaborate with one another using same handheld device in field or using desktop PC in office. Therefore, to study the effects of platform heterogeneity on task performance in collaborative virtual environments (CVEs) is helpful to improve future collaboration efficiency. In this paper, an experiment with highly interactive task was designed to study these effects. The task performance was evaluated under different combinations of three platforms: PC with a sensor, PDA with a stylus and PDA with direction buttons. The experiment results indicate that for simple 3D collaboration task, if right type of visualization and input device was chosen, we would be satisfied with performance both on homogeneous platforms and heterogeneous platforms
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.
40832727
Interactive Semisupervised Learning for Microarray Analysis
7396266
Dynamics of the noisy neural network
Abstract. The dynamic features of interspike interval sequences and structures of spatiotemporal patterns of firing in a coupled noisy neural network are investigated. The system displays complex dynamics under periodic external stimuli. The dynamics is modulated by a periodic impulse-like synaptic current which relates to a global coupling interaction between neurons. The firings of the stimulated neurons are phase-locked to this current. In addition, the interspike interval histograms are studied for the case of frozen noise which does not change its value within a time interval once it has been distributed onto the network. It is found that the peaks in these histograms are located at integer multiples of the period of the external stimulus, and the heights of these peaks decay exponentially, which corresponds to the experimental results.
35120658
Max-Relevance and Min-Redundancy Greedy Bayesian Network Learning on High Dimensional Data
Existing algorithms for learning Bayesian network require a lot of computation on high dimensional itemsets which affects accuracy especially on limited datasets and takes up a large amount of time. To address the above problem, we propose a novel Bayesian network learning algorithm MRMRG, Max Relevance-Min Redundancy Greedy. MRMRG algorithm is a variant of K2 which is a well- known BN learning algorithm. We also analyze the time complexity of MRMRG. The experimental results show that MRMRG algorithm has much better efficiency and accuracy than most of existing algorithms on limited datasets.
34743400
An optimal weather condition dependent approach for emission planning in urban areas
Abstract The urban air quality strongly depends on the weather conditions. When unfavorable weather conditions are forecasted, it is desirable to reduce a certain amount of pollutant emissions to maintain a given level of air quality. In this paper, we propose and demonstrate a new approach to find the optimal reduction, and we contribute to the methodology of decision support of short-term emission control. This approach is based on weather forecasts and state-of-the-art 3-dimensional numerical air-quality prediction models by solving an optimal control problem with the emission cuts as the control variables. The objective of the optimal problem is to minimize the total cost due to the emission cuts, subject to feasibility constraints, system-governing model constraints, and target constraints. When a high-resolution numerical model is used as the state constraint, the problem can become a very high dimensional one. A practical approach to solving this problem with high dimension is proposed, based on the adjoint technique. The proposed approach is demonstrated with two computational test cases in Jinan, China for the control of sulfur dioxide. The results show the capability and computational efficiency of the method and suggest a promising potential for emission planning applications based on weather forecasts.
22925595
A Novel Chase Based Multiuser Detector for MIMO-CDMA Systems
This paper proposes a new chase based multiuser detection scheme for MIMO-CDMA systems. The proposed approach provides performance gain by concentrating on improving the detection accuracy of the weakest symbol. Compared to the layered space-time multiuser detector (LAST-MUD), the proposed scheme can achieve substantial performance improvement, especially, when the number of transmit antennas is equal to the number of receive antennas. The comparison between our scheme and another chase based scheme, the B-Chase detector, which was originally proposed for improving the performance of the vertical Bell Laboratories layered space-time (V-BLAST) system, is also presented. The problem existing in the B-Chase detector is the criterion for selecting the weakest symbol. In our scheme, a more reasonable selection criterion is proposed. We show that the proposed scheme has less complexity than the B-Chase detector but with the tendency of achieving better performance at high SNR.