23190531
Algorithm for yield driven correction of layout
As the development of VLSI technique, the critical dimension of IC has become smaller than the exposure wavelength. Due to the diffraction and interaction of optical waves, deformations between the image on wafer and the feature on layout are undeniable. This results in bad performance or even invalid circuits of the chips. OPC is critical compensation technique to correct the deformations on wafer images. This work presents a layout correction and optimization algorithm called MOPC; it's a flexible and efficient core for the model-based OPC system. Since we divide the target features into different types before correction, the OPE between the target features and the environment features and the OPE between the neighboring segments of the inside feature are both considered during the correction.
17238158
A Novel Cookie-Based DDoS Protection Scheme and its Performance Analysis
Seamless handover is one of the most attractive research fields in B3G systems. Many mechanisms are proposed to provide certain QoS guarantees in handovers of mobile systems, which would also introduce new threats, such as DoS and DDOS attacks. In this paper, we extend a cookie-based scheme to protect systems from DoS and DDoS attacks. We also adopt a novel queuing network model to analyze our scheme by estimating two essential metrics, i.e. the mean total response time and the mean queue length of MAP. Numerical results indicate that our mechanism works better than the traditional cookie-based scheme and it could effectively help networks defend against abnormal attacks in the handover process.
14617194
Effects of Suffix Repetition Rates of a String on the Performance of String Matching Algorithms
The highly efficient Boyer-Moore’s string matching algorithm utilizes information on multi-occurrences of string suffixes in a pattern string to avoid backtracks in searching the pattern string. One hypothesis is that Boyer-Moore’s algorithm even benefits more from highly self-repetitive patterns. In this paper, the author studies how multi-occurrences of string suffixes affect the performance of the Boyer-Moore’s algorithm as well as some other well known string search algorithms. The paper introduces a new concept of suffix repetition rate (SRR) to measure how frequently the suffixes of a string occur inside of the string. Using this measurement, experiments with several thousands patterns over the entire range of SRRs have been carried out, and the results show that increasing of SRR on pattern strings does not improve the performance of a searching algorithm in terms of efficiency.
9978380
Learning a discriminative hidden part model for human action recognition
We present a discriminative part-based approach for human action recognition from video sequences using motion features. Our model is based on the recently proposed hidden conditional random field (hCRF) for object recognition. Similar to hCRF for object recognition, we model a human action by a flexible constellation of parts conditioned on image observations. Different from object recognition, our model combines both large-scale global features and local patch features to distinguish various actions. Our experimental results show that our model is comparable to other state-of-the-art approaches in action recognition. In particular, our experimental results demonstrate that combining large-scale global features and local patch features performs significantly better than directly applying hCRF on local patches alone.
9587091
Topology control for delay-constraint data collection in wireless sensor networks
Data collection is one of the most important operations in wireless sensor networks. Many practical applications require the real-time data transmission, such as monitoring, tracking, etc. In this paper, we import and define the topology control problem for delay-constraint data collection (TDDC), and then formalize this problem into an integer programming problem. As NP-Hardness of this problem, we present a load-aware power-increased topology control algorithm (namely LPTC) to heuristically solve the problem. The theoretical analysis shows that this algorithm can reach O(1)-approximation ratio for the linear networks. And we also analyze the impact of the delay-constraint on the worst-case for the planar networks. Moreover, this paper designs two localized algorithms, called as SDEL and DDEL, based on the area division for TDDC problem. The experimental results show that LPTC algorithm can save at least 17% power consumptions compared with HBH algorithm in many situations.
758563
Cost-sensitive boosting for classification of imbalanced data
Classification of data with imbalanced class distribution has posed a significant drawback of the performance attainable by most standard classifier learning algorithms, which assume a relatively balanced class distribution and equal misclassification costs. The significant difficulty and frequent occurrence of the class imbalance problem indicate the need for extra research efforts. The objective of this paper is to investigate meta-techniques applicable to most classifier learning algorithms, with the aim to advance the classification of imbalanced data. The AdaBoost algorithm is reported as a successful meta-technique for improving classification accuracy. The insight gained from a comprehensive analysis of the AdaBoost algorithm in terms of its advantages and shortcomings in tacking the class imbalance problem leads to the exploration of three cost-sensitive boosting algorithms, which are developed by introducing cost items into the learning framework of AdaBoost. Further analysis shows that one of the proposed algorithms tallies with the stagewise additive modelling in statistics to minimize the cost exponential loss. These boosting algorithms are also studied with respect to their weighting strategies towards different types of samples, and their effectiveness in identifying rare cases through experiments on several real world medical data sets, where the class imbalance problem prevails.
17478869
Teraflop Computing for Nanoscience
Over the last three decades there has been significant progress in the first principles methods for calculating the properties of materials at the quantum level. They have largely been based on the local density approximation (LDA) to density functional theory (DFT). However, nanoscience places new demands on these first principles methods because of the thousands to millions of atoms present in even the simplest of nano-structured materials. Recent advances in the locally self-consistent multiple scattering (LSMS) method are making the direct quantum mechanical simulation of nano-structured materials possible. The LSMS method is an order-N approach to first principles electronic structure calculation. It is highly scalable on massively parallel processing supercomputers, and is suited for performing large unit cell simulations to study the electronic and magnetic properties of materials with complex structure. In this presentation, we show that the LSMS accomplishes the first step towards understanding the electronic and magnetic structure of nano-structured materials with dimension size close to 10 nanometers (nm). As an example, we describe a 16,000 atom calculation of the electronic and magnetic structure calculated for an iron nanoparticle embedded in iron aluminide crystal matrix.