Fast and scalable parallel layout decomposition in double patterning lithography

For 32/22 nm technology nodes and below, double patterning (DP) lithography has become the most promising interim solutions due to the delay in the deployment of next generation lithography (e.g., EUV). DP requires the partitioning of the layout patterns into two different masks, a procedure called layout decomposition. Layout decomposition is a key computational step that is necessary for double patterning technology. Existing works on layout decomposition are all single-threaded, which is not scalable in runtime and/or memory for large industrial layouts. This paper presents the first window-based parallel layout decomposition methods for improving both runtime and memory consumption. Experimental results are promising and show the presented parallel layout decomposition methods obtain upto 21× speedup in runtime and upto 7.5×reduction in peak memory consumption with acceptable solution quality.     

一种基于无平方根QR分解的流水RLS算法

传统ＱＲ分解（ＧＩＶＥＮＳ旋转）中存在着平方根运算,在用数字电路实现时,会在时间和面积上花费较大的代价,因此,已有无平方根的ＱＲ分解算法出现,这些算法避免了平方根运算,但遇射为Ｓｙｓｔｏｌｉｃ阵列以实现ＲＬＳ算法时,却不能实现单元内部的流水,本文提出了一种新的无平方要ＱＲ分解,能够实现单元内部的流水,进一步提高了速度。     

A customized low-rank prior model for structured cartoon–texture image decomposition

The mathematical characterization of the texture component plays an instrumental role in image decomposition. In this paper, we are concerned with a low-rank texture prior based cartoon–texture image decomposition model, which utilizes a total variation norm and a global nuclear norm to characterize the cartoon and texture components, respectively. It is promising that our decomposition model is not only extremely simple, but also works perfectly for globally well-patterned images in the sense that the model can recover cleaner texture (or details) than the other novel models. Moreover, such a model can be easily reformulated as a separable convex optimization problem, thereby enjoying a splitting nature so that we can employ a partially parallel splitting method (PPSM) to solve it efficiently. A series of numerical experiments on image restoration demonstrate that PPSM can recover slightly higher quality images than some existing algorithms in terms of taking less iterations or computing time in many cases.     

一种新的小波阈值去噪方法研究

本文研究用组合小波对含噪信号的去噪问题,对均方根误差、信噪比、平滑度等信号指标进行多指标融合作为去噪效果的评价参数,根据评价参数确定最佳小波分解尺度、小波去噪最佳小波基和最佳阈值。新的去噪方法与传统的去噪方法相比,克服了硬阈值不连续的缺点,还克服了软阈值中估计小波系数与分解小波系数之间的恒定偏差的缺点。MATLAB仿真结果表明,新的阈值函数的去噪效果在各指标上都优于传统的阈值去噪方法。     

基于正交基分解算法的航空磁探方法

航空磁探中,将潜艇目标以磁偶极子模型代替。针对目标信号信噪比较低的情况,利用正交基分解检测算法,大幅提高了水中磁性目标信号的信噪比,有效地检测到磁性目标。采用双L型航空磁探方法,确定磁性目标的运动位置、航向和航速。仿真试验表明,该方法具有一定的工程应用价值。     

一种分布式MIMO信号的发射与检测方法

在 V-BLAST结构的分布式多输入多输出(MIMO)系统中,针对发射天线地域分布所引起的发射符号异步到达接收天线的问题,给出了一种分布式 MIMO 系统的信号发射与检测方法。在慢衰落信道中,该方法利用接收端反馈的信道状态信息,采用几何平均分解(GMD)构建等对角的上三角矩阵,消除子信道间增益的不平衡。在莱斯衰落信道下的计算机仿真表明,与已有 QR分解的分层算法相比,该检测方法能够显著提升系统的误码性能。     

Efficient clustering algorithms for self-organizing wireless sensor networks

Self-organization of wireless sensor networks, which involves network decomposition into connected clusters, is a challenging task because of the limited bandwidth and energy resources available in these networks. In this paper, we make contributions towards improving the efficiency of self-organization in wireless sensor networks. We first present a novel approach for message-efficient clustering, in which nodes allocate local “growth budgets” to neighbors. We introduce two algorithms that make use of this approach. We analyze the message complexity of these algorithms and provide performance results from simulations. The algorithms produce clusters of bounded size and low diameter, using significantly fewer messages than the earlier, commonly used, Expanding Ring approach. Next, we present a new randomized methodology for designing the timers of cluster initiators. This methodology provides a probabilistic guarantee that initiators will not interfere with each other. We derive an upper bound on the expected time for network decomposition that is logarithmic in the number of nodes in the network. We also present a variant that optimistically allows more concurrency among initiators and significantly reduces the network decomposition time. However, it produces slightly more clusters than the first method. Extensive simulations over different topologies confirm the analytical results and demonstrate that our proposed methodology scales to large networks.     

Least squares decoding in binomial frequency division multiplexing

This paper proposes a method that can reduce the complexity of a system matrix by analyzing the characteristics of a pseudoinverse matrix to receive a binomial frequency division multiplexing (BFDM) signal and decode it using the least squares (LS) method. The system matrix of BFDM can be expressed as a band matrix, and as this matrix contains many zeros, its amount of calculation when generating a transmission signal is quite small. The LS solution can be obtained by multiplying the received signal by the pseudoinverse matrix of the system matrix. The singular value decomposition of the system matrix indicates that the pseudoinverse matrix is a band matrix. The signal-to-interference ratio is obtained from their eigenvalues. Meanwhile, entries that do not contribute to signal generation are erased to enhance calculation efficiency. We decode the received signal using the pseudoinverse matrix and the removed pseudoinverse matrix to obtain the bit error rate performance and to analyze the difference.     

Inter‐cell interference mitigation based on joint beamforming

In cellular networks, global channel state information (CSI) shall be shared among neighboring cells to mitigate various inter‐cell interference (ICI). In this paper, we propose that comparable ICI is mitigated while weak ICI is treated as noise. The amount of shared CSI is highly reduced, because of that only the CSI corresponding to comparable ICI is shared among neighboring cells. In the case of single user equipment in each cell, a joint singular value decomposition and interference alignment method is proposed along with the analysis of its approximate sum rate. Some nodes select singular value decomposition method, while others choose interference alignment method. The method at each node is determined by global selection principle or user classification principle. In the case of multiple UEs in each cell, all nodes utilize minimum mean square error method, but ICI is neglected at cell center users. Copyright © 2014 John Wiley & Sons, Ltd.     

精确提取InSAR时间去相关分量的方法

提出了一种干涉合成孔径雷达(InSAR,Synthetic Aperture Radar Interferometry)回波信号时间去相关分析的新方法,该方法主要包括三个步骤:采用自适应区域增长算法(IDAN,Intensity-Driven Adaptive Neighborhood)估计所有干涉子集的相干性;利用迭代最小二乘去除估计量偏差;采用相干性分解技术对无偏样本相干性进行分离,获得精确的时间去相关分量.以美国南加州洛杉矶地区的ENVISAT ASAR数据集为例,对新方法和现有方法进行了比较研究.结果表明,新的融合算法能够获得更加可靠、精度更高的时间去相关分量,并具有非阈值和近乎完全的自适应性.本文的研究将有利于改善与时间相干性有关的地球物理参数反演,也有利于地表变化周期性和随时间变化的气候环境实时监测等.