12719796
Noisy video super-resolution
Low-quality videos often not only have limited resolution, but also suffer from noise. Directly up-sampling a video without considering noise could deteriorate its visual quality due to magnifying noise. This paper addresses this problem with a unified framework that achieves simultaneous de-noising and super-resolution. This framework formulates noisy video super-resolution as an optimization problem, aiming to maximize the visual quality of the result. We consider a good quality result to be fidelity-preserving, detail-preserving and smooth. Accordingly, we propose measures for these qualities in the scenario of de-noising and super-resolution. The experiments on a variety of noisy videos demonstrate the effectiveness of the presented algorithm.
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.
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.
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.
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.
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.
950378
Extending OpenMP for heterogeneous chip multiprocessors
The emergence of system-on-chip (SOC) design shows the growing popularity of the integration of multiple-processors into one chip. We propose that high-level abstraction of parallel programming like OpenMP is suitable for chip multiprocessors. For SOCs, the heterogeneity exists within one chip such that it may have different types of multiprocessors, e.g. RISC-like processors or DSP-like processors. Incorporating different processors into OpenMP is challenging. We present our solutions to extend OpenMP directives to tackle this heterogeneity. Several optimization techniques are proposed to utilize advanced architecture features of our target SOC, the software scalable system on chip (3SoC). Preliminary performance evaluation shows scalable speedup using different types of processors and performance improvement through individual optimization.
11096456
Learning Bayesian Networks with Combination of MRMR Criterion and EMI Method
Currently, learning Bayesian Networks (BNs) from data has become a much attention-getting issue in fields of machine learning and data mining. While there exists few efficient algorithms for learning BNs in presence of incomplete data. In this paper, we present a scoring function based on mutual information for evaluating BN structures. To decrease computational complexity, we introduce MRMR criterion into the scoring function, which enables the computation of the scoring function to involve in only two-dimensional mutual information. When the dataset is incomplete, we use EMI method to estimate the Mutual Information (MI) from the incomplete dataset. As for whether a node ordering is manually given or not, we develop two versions of algorithms, named as MRMR- E1 and MRMR-E2 respectively and evaluate them through experiments. The experimental results on Alarm network show good accuracy and efficiency of our algorithms.
29468800
SceBoost Learning Algorithm for Feature Selection
This paper proposes an improved boost learning algorithm, the SceBoost algorithm, and its application in developing fast and robust features for citrus canker detection by machine vision. The algorithm use symmetric cross entropy to eliminate redundancy among selected features using AdaBoost algorithm. Selected features are subjected to recognize citrus canker symptoms on given pictures of citrus foliage. Compared with related feature selection algorithm our method can get improvements in classification accuracy and significantly reduce computation time when reach the same requirements.
28489429
Transmission Scheduling in Data-Driven Peer-to-Peer Streaming towards Optimal Throughput
Peer-to-peer media streaming has been an important service on the internet in recent years. The Data-driven (or mesh-based) structure is adopted by most working systems,in which data scheduling is one of the important problems.However, those frequently used scheduling algorithms are often faced with such a case: A neighbor peer takes up its bandwidth to deliver the packets that other neighbors can also supply, but some packets only held by it are not delivered.These packets can not be delivered in the current scheduling cycle, even though that the other neighbors have surplus bandwidth. This is a kind of waste of bandwidth and decreases the throughput of transmission. In this paper we propose anew scheduling algorithm aiming at the optimal throughput:Bipartite-matching based Block Scheduling algorithm(BBS).We convert the original data scheduling problem to a problem of finding a maximum match on the correspond bipartite graph, then assign data packets to neighbors according to the maximum match. We evaluate the performance of BBS with extensive experiments and the results show that BBS throughput and provides better streaming quality than those frequently used scheduling algorithms.
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.