Hardware-efficient common-feedback Markov-random-field probabilistic-based noise-tolerant VLSI circuits

As the size of CMOS devices is scaled down to lower the power consumption and space occupied on the chip to the nano-scale, unfortunately, noise is not reduced accordingly. As a result, interference due to noise can significantly affect circuit performance and operation. Since noises are random and dynamic in nature, probabilistic noise-tolerant approaches are more desirable to handle this problem. However, trade-offs between hardware complexity and noise-tolerance are severe design challenges in the probabilistic-based noise-tolerant approaches. In this paper, we proposed a cost-effective common-feedback probabilistic-based noise-tolerant VLSI circuit based on Markov random field (MRF) theory. We proposed a common latch feedback method to lower the hardware complexity. To further enhance the noise-tolerant ability, the common latch feedback technique is combined with Schmitt trigger. To demonstrate the proof-of-concept design, a 16-bit carry-lookahead adder was implemented in the TSMC 90 nm CMOS process technology. As compared with the state-of-art master-and-slave MRF design, the experimental results show that not only the transistor count can be saved by 20%, the noise-tolerant performance can also be enhanced from 18.1 dB to 24.2 dB in the proposed common feedback MRF design.     

基于改进相容函数的超分辨率图像重建

提出了一种基于机器学习的超分辨率(SR)改进算法。首先建立一个包括低分辨率(LR)图像及其相应的高分辨率(HR)图像的训练样本集,为LR图像提供了HR的图像解释。把训练集中的每一幅图像分成若干个图像块,每一个图像块作为马尔可夫随机场(MRF)模型的结点,MRF模型参数从这些训练样本中学习得到,通过对训练样本中的LR图像块进行k-均值聚类减少计算开销,并用k-均值的聚类结果提出了一种新的相容函数形式。实验结果表明,该算法是可行的,并与同类算法相比能取得较好的结果,使得SR后的图像更平滑自然。     

应用分层MRF/GRF模型的立体图像视差估计及分割

视差估计与分割是立体图像编码及立体视觉匹配的核心问题,本文提出一种基于分层MRF/GRF模型和交叠块匹配(HMOM)视差估计算法以及结合主动轮廓模型的视差分割提取算法.该混合视差估计方法,可得到光滑准确,且具有清晰边缘的视差场;并便于用主动轮廓模型提取感兴趣对象(OOI)的视差轮廓.与通常的变尺寸块匹配(VSBM)相比,本算法得到的视差补偿图像的峰值信噪比可提高2.5dB左右.本文得到的视差场及对应的轮廓可进一步用于立体图像编码以及视频对象分割.     

结合颜色和MGD特征及MRF模型的场景文本分割

针对场景文本受到光照、复杂背景等因素影响而难以进行有效分割的问题,提出了一种融合颜色和最大梯度差(MGD,maximum gradient difference)特征及马尔科夫随机场(MRF,Markov random field)的场景文本分割方法。首先提取能够有效表达文本纹理特性的MGD特征,通过概率框架将其和颜色特征结合起来对观测图像进行建模;然后结合空间关系和邻域像素属性差异对传统势函数进行改进;最后建立场景文本分割的MRF模型,利用图割(graph cut)算法快速地求解该模型。实验结果表明,采用颜色和MGD特征相结合以及改进的势函数对分割结果具有较大地改善,尤其在光照不均匀及背景复杂情况下相比其他算法取得了较好的性能。     

基于生成MRF和局部统计特性的红外弱小目标检测算法

红外复杂背景中的弱小目标检测问题可看作是马尔可夫随机场理论框架下红外图像中背景与目标的二元分类标记问题.基于马尔可夫随机场后验概率模型, 提出利用先验的目标信杂比信息和图像局部统计特性构建观测图像后验概率模型的方法, 并采用经典ICM (Iterated conditional mode)方法对图像最优标记结果进行估计.仿真试验结果表明, 算法在保证目标标记结果准确率的同时, 有效降低了背景的误标记概率; 且由于采用局部统计特性进行建模, 算法有效降低了模型参数与标记结果间的关联性, 提高了最优标记估计的收敛速度.     

A Kernel PCA Shape Prior and Edge Based MRF Image Segmentation

We introduce both shape prior and edge information to Markov random field (MRF) to segment target of interest in images.Kernel Principal component analysis (PCA) is performed on a set of training shapes to obtain statistical shape representation.Edges are extracted directly from images.Both of them are added to the MRF energy function and the integrated energy function is minimized by graph cuts.An alignment procedure is presented to deal with variations between the target object and shape templates.Edge information makes the influence of inaccurate shape alignment not too severe,and brings result smoother.The experiments indicate that shape and edge play important roles for complete and robust foreground segmentation.     

结合暗通道先验和MRF理论的单幅图像去雾算法

针对在雾霾环境下获取的图像降质严重、现有算法去雾图结构细节信息丢失较多的问题,提出了一种结合暗通道先验(DCP)和马尔可夫随机场(MRF)的单幅图像去雾算法。该算法先采用子块部分重叠局部直方图均衡(POSHE)对原始雾图进行增强,以提高其对比度,并通过DCP算法获取优化后的透射率;利用MRF模型对图像结构细节信息的约束特性,对透射率进行建模,以进一步细化透射率;由天空域的显著特征,通过分块搜索法求取大气光值。与传统去雾算法相比,该算法能得到更精确的透射率图,有效保持图像结构信息,去雾后的图呈现出丰富的细节和较真实的色彩视觉效果。     

CORDIC based fast algorithm for power-of-two point DCT and its efficient VLSI implementation

—In this paper, we present a coordinate rotation digital computer (CORDIC) based fast algorithm for power-of-two point DCT, and develop its corresponding efficient VLSI implementation. The proposed algorithm has some distinguish advantages, such as regular Cooley-Tukey FFT-like data flow, identical post-scaling factor, and arithmetic-sequence rotation angles. By using the trigonometric formula, the number of the CORDIC types is reduced dramatically. This leads to an efficient method for overcoming the problem that lack synchronization among the various rotation angles CORDICs. By fully reusing the uniform processing cell (PE), for 8-point DCT, only four carry save adders (CSAs)-based PEs with two different types are required. Compared with other known architectures, the proposed 8-point DCT architecture has higher modularity, lower hardware complexity, higher throughput and better synchronization.     

Accurate Segmentation for Infrared Flying Bird Tracking

Bird strikes present a huge risk for air ve-hicles, especially since traditional airport bird surveillance is mainly dependent on ine?cient human observation. For improving the effectiveness and e?ciency of bird monitor-ing, computer vision techniques have been proposed to detect birds, determine bird flying trajectories, and pre-dict aircraft takeoff delays. Flying bird with a huge de-formation causes a great challenge to current tracking al-gorithms. We propose a segmentation based approach to enable tracking can adapt to the varying shape of bird. The approach works by segmenting object at a region of inter-est, where is determined by the object localization method and heuristic edge information. The segmentation is per-formed by Markov random field, which is trained by fore-ground and background mixture Gaussian models. Exper-iments demonstrate that the proposed approach provides the ability to handle large deformations and outperforms the m ost state-of-the-art tracker in the infrared flying bird tracking problem.     

基于MRF的自适应正则化红外背景杂波抑制算法

针对复杂背景下红外弱小目标检测难题,将背景杂波抑制归结为从原始红外弱小目标图像中重建目标数据的过程,据此提出了一种基于马尔可夫随机场模型(MRF)的自适应正则化滤波算法.该算法采用MRF,建立了红外弱小目标图像的先验概率模型,并根据图像的粗糙度设计了新的势函数.在此基础上,采用MRF对背景杂波抑制过程进行正则化处理,从而实现了对红外背景杂波的自适应各向异性抑制.理论分析与实验结果表明,该算法能够随图像局部纹理特征的变化自适应地调整滤波算子结构,从而可在复杂背景下自适应地抑制杂波、增强信号,有效地提高了图像的信噪比,且该算法结构简单,更易于硬件实时实现.     

马尔可夫化的多尺度FCM在影像分割中的应用

为了同时处理影像分割问题中的随机性与模糊性,提出了一种多尺度(MR,multi-resolu-tion,马尔可夫随机场(MRF,markov random field)模型下的模糊C均值(FCM,fuzzy C-means)聚类分割算法(MR-MRF-FCM)。利用FCM算法能够处理影像模糊性的优点、MRF模型描述空间关系的长处以及小波的多尺度分析的优点,先对影像进行多尺度小波分解,并对小波系数建立MRF,进而用MR-MRF中的条件概率矩阵代替FCM算法的隶属度矩阵。实验结果从视觉效果和定量指标两方面表明,本文方法优于经典的MRF、多尺度MRF、FCM和核FCM等方法。     

Locating hot nodes and data routing for efficient decision fusion in sensor networks

This paper presents a fundamentally new approach to integrating local decisions from various nodes and efficiently routing data in sensor networks. By classifying the nodes in the sensor field as “hot” or “cold” in accordance with whether or not they sense the target, we are able to concentrate on a smaller set of nodes and gear the routing of data to and from the sink to a fraction of the nodes that exist in the network. The introduction of this intermediary step is fundamentally new and allows for efficient and meaningful fusion and routing. This is made possible through the use of a novel Markov Random Field (MRF) approach, which, to the best of our knowledge, has never been applied to sensor networks, in combination with Maximum A Posteriori Probability (MAP) stochastic relaxation tools to flag out the “hot” nodes in the network, and to optimally combine their data and decisions towards an integrated and collaborative global decision fusion. This global decision supersedes all local decisions, and provides the basis for efficient use of the sensed data. Because of the MRF local nature, nodes need not see or interact with other nodes in the sensor network beyond their immediate neighborhood, which can either be defined in terms of distance between nodes or communication connectivity, hence adding to the flexibility of dealing with irregular and varying sensor topologies, and also minimizing node power usage and providing for easy scalability. The routing of the “hot” nodes’ data is confined to a cone of nodes and power constraints are taken into account. We also use the found location of the centroid of the hot nodes over time to track the movement of the target(s). This is achieved by using the segmentation at time t as an initial state in the stochastic MAP relaxation at time t + Δt.     

一种高能效低时延的LLN路由修复算法

由于低功耗有损网络(LLN)中无线链路的不稳定性和有损性,外部环境的干扰极易导致网络出现故障,从而严重影响网络性能,而LLN网络中现有路由修复算法存在控制开销冗余和修复时延较大等问题。为此,提出了一种高能效低时延的LLN路由修复算法(EELDR-RPL)。该算法通过采用“零额外控制开销通告链路故障及邻居节点信息”机制,使得链路故障节点的子节点能够及时获知链路故障以及链路故障节点的邻居情况;通过采用“自适应调整节点网络深度值”机制,使得链路故障节点能够快速地重新接入网络;通过采用“链路故障节点子节点自适应切换”机制,能够达到优化网络拓扑的目的。仿真结果表明,与现有路由修复算法相比,EELDR-RPL算法能够有效地降低路由修复时延和减少控制开销。     

基于局部竞争策略的极化SAR图像精细分类

合成孔径雷达（Synthetic Aperture Radar, SAR）成像技术已经成为一种高分辨对地观测的重要手段之一,而极化SAR图像地物分类一直是其中的研究热点。基于复Wishart分布的最大似然（Maximum Likelihood,ML）分类器是最经典的极化SAR图像分类算法之一,但由于地物类型的复杂性、区域的不均匀性等原因使得基于像素的ML-Wishart分类器的分类精度不高。针对这个问题,本文提出了一种基于复Wishart分布的局部最大后验概率（Maximum a Posteriori,MAP）竞争方法,该算法通过计算伪先验概率,并在每个像素的局部窗口中实施MAP分类器,可以提高复杂区域图像的分类精度。该文主要研究了4种基于Wishart分布的分类算法,包括经典复Wishart分类算法、混合复Wishart模型、基于马尔科夫随机场（Markov Random Field, MRF）的混合复Wishart模型和基于局部竞争策略的MAP分类算法。在混合模型建模中,不同于以往的对整幅图像进行建模的模型策略,本文采用对单个类别进行混合建模的策略。实验对比分析了上述4个分类器和SVM分类器在C波段RADARSAT-2多时相的全极化SAR农田数据上的分类效果。实验结果表明,所提出的基于局部竞争策略的分类器对数据的分类结果稳定,具有最高的分类精度,基于混合Wishart的MRF模型分类结果次之。     

MPEG专用IDCT处理器的优化设计

二维离散余弦逆变换 ( Inverse Discrete Cosine Transform,IDCT)是运动图象专家组( Moving Picture Expert Group,MPEG)视频解码器的重要模块之一 .提出了一种优化的二维IDCT超大规模集成电路 ( Very Large Scale Integrated Circuit,VLSI)实现结构 .利用 IDCT的矩阵乘法中固定系数的内在特点 ,采用公用表达式的方法 ,降低 IDCT实现规模 ,提高了电路的速度 .采用了 0 .6μm CMOS电路工艺 ,芯片面积约为 3.5mm× 3.5mm,速度可达 1 0 0 MHz     

基于U分布的PolSAR图像无监督MAP分类方法

结合U分布对不同匀质性极化合成孔径雷达(PolSAR)数据的广泛建模能力及Potts马尔科夫随机场(MRF)模型对像素点之间类相关性的建模能力,提出了一种基于最大后验概率(MAP)准则的PolSAR图像无监督分类方法。利用迭代条件模式算法和Metropolis采样算法对像素点的类别进行更新,迭代过程中分布参数的估计采用基于梅林(Mellin)变换的矩阵对数累积量方法,以迭代过程中出现次数最多的类别最为像素点的最终分类结果。利用NASA/JPL实验室AIRSAR系统获取旧金山湾的PolSAR数据,对本文分类算法的有效性以及分布的杂波建模能力进行了仿真验证。实验结果表明,本文分类算法的精度优于Lee分类算法,分布对PolSAR数据的杂波建模准确性总体上优于复Wishart分布、K分布和G0分布。     

An efficient reliable broadcasting protocol for wireless mobile ad hoc networks

The mobile ad hoc network (MANET) has recently been recognized as an attractive network architecture for wireless communication. Reliable broadcast is an important operation in MANET (e.g., giving orders, searching routes, and notifying important signals). However, using a naive flooding to achieve reliable broadcasting may be very costly, causing a lot of contention, collision, and congestion, to which we refer as the broadcast storm problem. This paper proposes an efficient reliable broadcasting protocol by taking care of the potential broadcast storm problem that could occur in the medium-access level. Existing protocols are either unreliable, or reliable but based on a too costly approach. Our protocol differs from existing protocols by adopting a low-cost broadcast, which does not guarantee reliability, as a basic operation. The reliability is ensured by additional acknowledgement and handshaking. Simulation results do justify the efficiency of the proposed protocol.     

基于小波变换和马尔可夫随机场的极化SAR图像自动分类

本文提出一种对极化合成孔径雷达(SAR)图像进行自动多分辨率分类的方法。首先利用多视极化白化滤波(MPWF)抑制极化SAR图像的相干斑,得到反映地物辐射特征的纹理SAR图像,然后利用小波变换(WT)提取不同分辨率的纹理信息,在最低分辨率级利用Akaik信息准则(AIC)自动估计图像中的纹理类数,进而在各个分辨率级利用马尔可夫随机场(MRF)模型表征各像素间的空间关联信息,并分别利用最大似然(ML)方法和循环条件模式(ICM)进行自动的模型参数估计和最大后验概率(MAP)分类,最后应用NASA/JPL机载L波段极化SAR数据验证了本文所提分类方法的有效性和优越性。     

An integrated fault tolerance technique for combinational circuits based on implications and transistor sizing

With fabrication technology reaching nano levels, systems are exposed to higher susceptibility to soft errors. Thus, development of effective techniques for designing soft error tolerant systems is of high importance. In this work, an integrated soft error tolerance technique based on logical implications and transistor sizing is proposed. In order to reduce implication learning time, a set of source and target nodes with predefined thresholds are selected and implications between these nodes are extracted. Then, the impact of adding a functionally redundant wire (FRW) due to each implication is evaluated. This is done based on identifying an implication path and the gates along the implication path whose detection probabilities will be reduced due to adding the implication FRW. Then, the gain of an implication is estimated in terms of reduction in fault detection probabilities of gates along an implication path. The implication with the highest gain is selected. The process is repeated until the gain is less than a predetermined threshold. The proposed implication-based fault tolerance technique enhances the circuit reliability with minimal area overhead based on enhancing logical masking. However, its effectiveness depends on the existence of such relations in a circuit and can enhance circuit reliability upto a certain level. To enhance circuit reliability to any required level, selective-transistor redundancy (STR) based technique is then applied. This technique is based on providing fault tolerance for individual transistors with high detection probability based on transistor duplication and sizing. Experimental results show that the proposed integrated fault tolerance technique achieves similar reliability in comparison to applying STR alone with lower area overhead.     

基于MRF二次Membrane-Plate混合自适应先验的PET图像的收敛Bayesian重建算法

对于如何抑制正电子发射成像(positron emission tomography,PET)中的噪声效果的问题,Bayesian重建或者最大化后验估计(maximum a posteriori,MAP)的方法在重建图像质量和收敛性方面具有相对于其他方法的优越性.基于Bayesian理论,本文提出了一种新的能够保持其先验能量函数凸性的马尔可夫随机场(Markov Random Fields,MRF)混合多阶二次先验(quadratic hybrid multi-order,QHM),该QHM先验综合了二次-阶(quadratic membrane,QM)先验和二次二阶(quadratic plate,QP)先验,且能够根据不同阶数的二次先验和待重建表面的性质自适应的发挥QM先验和QP先验的作用.文中还给出了使用该新的混合先验的收敛重建算法.模拟实验结果的视觉和量化比较证明了对于PET重建,该先验在抑制背景噪声和保持边缘方面具有很好的表现.