LCD backlights,light sources,and flat fluorescent lamps

Abstract— Backlights are indispensable for the operation of LCDs. Light sources or lamps are the core components of backlights. There are many types of light sources for backlights such as cold cathode fluorescent lamps (CCFLs), external electrode fluorescent lamps (EEFLs), hot‐cathode fluorescent lamps (HCFLs), flat fluorescent lamps (FFLs), and light‐emitting diodes (LEDs). Recently, FFLs are becoming one the most interesting light sources for LCD‐TV backlights. Channel profiles and the structure of FFLs in more detail are discussed in this paper. The channel profile of FFLs with maximum brightness uniformity was designed, fabricated, and characterized. The FFL backlight demonstrated 10,500 nits and the total power consumption was 110 W at room temperature.     

一种线性动态模型参数估计方法

本文提出一种线性动态模型参数估计方法,该算法改进并推广了Durbin算法.它能在噪声过程结构未知时,给出实际过程和噪声过程参数的全局P相容估值.五个数字仿真例子表明了算法的有效性.     

噪声环境中时延区间CNN网络鲁棒稳定性

由于采用大规模集成电路方法实现细胞神经网络(cellular neural networks,CNN),其电路所产生的噪声不可避免,实际的网络都是在噪声环境中进行工作的,弄清楚这些随机干扰是如何影响网络的稳定性,在网络设计时非常关键.利用鞅收敛定理、李雅普诺夫直接法和矩阵分析的方法,研究了白噪声干扰下时延区间细胞神经网络承受扰动的能力,得到了仅依赖系统参数的充分性代数判据.所得结果在系统设计时检验较为方便.     

Unsupervised analysis of top-k core members in poly-relational networks

Poly-relational networks such as social networks are prevalent in the real world. The existing research on poly-relational networks focuses on community detection, aiming to find a global partition of nodes across relations. However, in some real cases, users may be not interested in such a global partition. For example, commercial analysts often care more about the top-k core members in business competitions, and relations among them that are more important to their competitions. Motivated by this, in this paper, we investigate an unsupervised analysis of the top-k core members in a poly-relational network and identify two complementary tasks, namely (1) detection of the top-k core members that are most tightly connected by relevant relations, and (2) identification of the relevant relations via analysis on the importance of each relation to the formation of the top-k core members. Towards this, we propose an optimization framework to jointly deal with the two tasks by maximizing the connectivity between the candidates of the top-k core members across all relations with a synchronously updated weight for each relation. The effectiveness of our framework is verified both theoretically and experimentally.     

Identification of quadratic systems using higher order cumulants and neural networks: Application to model the delay of video-packets transmission

This work concerns the development of two approaches for the identification of diagonal parameters of quadratic systems from only the output observation. The systems considered are excited by an unobservable independent identically distributed (i.i.d), stationary zero mean, non-Gaussian process and corrupted by an additive Gaussian noise. The proposed approaches exploit higher order cumulants (HOC) (fourth order cumulants) and are the extension of the algorithms developed in the linear version 1D, which uses a non-Gaussian signal input. For test and validity purpose, these approaches are compared to recursive least square (RLS), least mean square (LMS) and neural network identification algorithms using non-linear model in noisy environment. To demonstrate the applicability of the theoretical methods on real processes, we applied the developed approaches to search for models able to describe the delay of the video-packets transmission over IP networks from video server. The simulation results show the correctness and the efficiency of the developed approaches.     

基于事务日志的社会网络抽取

社会网络分析(social network analysis, SNA)是数据挖掘领域的一个重要研究方向,社会网络数据的质量和规模对研究十分重要.在当前的社会网络分析研究中,大多数是基于社交网站生成的社会网络,社交网站生成的在线社会网络只是对真实社会网络近似模拟,其现象、结论无法代表真实社会网络;少数基于真实社会网络的研究中,由于数据采集难度较大,往往只能使用规模有限的社会网络,从而降低了分析结果的可信程度.现代软件系统产生大量的事务日志让构建基于真实环境的社会网络成为可能.以高校学生卡管理系统产生的事务日志为例,探索如何从海量事务日志中抽取社会网络.根据事务日志的特征,建立以共现(co-occurrence)特征为基础的网络抽取模型,抽取出所有可能构成这个社会网络的边;定义了一个基于边的权重和Jaccard相关性系数的边存在系数,识别网络中的噪音边,筛选噪音边;最后,通过同班级比率分析和网络拓扑结构分析,对抽取的网络进行验证.实验结果表明,所抽取的网络具有很高的同班级比率,该抽取模型具有较好效果,同时该网络具有小世界网络(small-world)特征和满足无标度(scale-free)度分布,符合常见社会网络特征.     

自增长网络模型及其算法

众所周知,现实世界的网络大部分都不是随机网络,少数的节点往往拥有大量的连接,而大多数的节点连接却很少,这正是无标度网络的重要特性。于是对于无标度网络性质的研究,因为其实用性而变得及其重要。首先定义了一种新的自增长网络模型,对它的基本参数进行计算,证明了它的无标度性。其次验证模型的最大叶子生成树的度分布服从幂率分布,并且得到了网络的平衡集,从而对无标度网络有了初步探索。最后给出了一个计算平均路长的算法。     

DynMAC: A resistant MAC protocol to coexistence in wireless sensor networks

The growth of mobile and ubiquitous computing has increased the demand for wireless communications, which in turn raises interference levels and spectrum pollution, causing problems of network coexistence. The coexistence assurance between these devices and wireless sensor networks is a big challenge. This paper proposes a new medium access protocol, DynMAC (Dynamic MAC), which uses mechanisms of dynamic channel reconfiguration, recovery from lost links and reconfiguration of transmission parameters based on the properties of the cognitive radios, to deal with this problem. Simulations and experiments using a real WSN testbed, were performed to validate our protocol. Results show that the proposed mechanisms solve the WSN configuration problems, in noisy and interference environments, and enable the coexistence with different networks and devices operating in the same frequency spectrum, while maintaining application requirements in critical deployment scenarios.     

噪声环境中时滞双向联想记忆神经网络指数稳定

任何系统实际上都是在噪声环境中进行工作的.对处在噪声强度已知的噪声环境下双向联想记忆(BAM)神经网络,其平衡点具有指数渐近稳定性是网络进行异联想记忆的基础.构造一个适当的Lyapunov函数,应用It^o公式、M矩阵等工具讨论了在噪声环境下具有时滞的BAM神经网络概率1指数渐近稳定,得到了指数稳定的代数判据和两个推论,此判据只需验证仅由网络参数构成的矩阵是M矩阵即可,给网络设计带来方便.本文所得结果包括相关文献中确定性结果作为特例.     

Recognizing environments from action sequences using self-organizing maps

In this paper, we describe development of a mobile robot which does unsupervised learning for recognizing an environment from action sequences. We call this novel recognition approach action-based environment modeling (AEM). Most studies on recognizing an environment have tried to build precise geometric maps with high sensitive and global sensors. However such precise and global information may be hardly obtained in a real environment, and may be unnecessary to recognize an environment. Furthermore unsupervised-learning is necessary for recognition in an unknown environment without help of a teacher. Thus we attempt to build a mobile robot which does unsupervised-learning to recognize environments with low sensitive and local sensors. The mobile robot is behavior-based and does wall-following in enclosures (called rooms). Then the sequences of actions executed in each room are transformed into environment vectors for self-organizing maps. Learning without a teacher is done, and the robot becomes able to identify rooms. Moreover, we develop a method to identify environments independent of a start point using a partial sequence. We have fully implemented the system with a real mobile robot, and made experiments for evaluating the ability. As a result, we found out that the environment recognition was done well and our method was adaptive to noisy environments.     

A globally convergent learning algorithm for PCA neural networks

Principal component analysis (PCA) by neural networks is one of the most frequently used feature extracting methods. To process huge data sets, many learning algorithms based on neural networks for PCA have been proposed. However, traditional algorithms are not globally convergent. In this paper, a new PCA learning algorithm based on cascade recursive least square (CRLS) neural network is proposed. This algorithm can guarantee the network weight vector converges to an eigenvector associated with the largest eigenvalue of the input covariance matrix globally. A rigorous mathematical proof is given. Simulation results show the effectiveness of the algorithm.     

基于随机配置网络的非线性系统智能建模方法

针对一类非线性离散动态系统,研究非线性系统的智能建模方法.首先,采用带遗忘因子的递推最小二乘法对低阶模型的未知参数进行辨识;然后,对高阶非线性部分采用随机配置网络进行估计;最后,利用两种辨识方法在外部误差准则下对系统进行交替辨识,进而提出一种改进的非线性系统交替辨识的智能建模方法.将随机配置网络与递推最小二乘算法相结合,可有效提高非线性系统的辨识精度,并且通过数值仿真实验进行对比分析以验证所提出算法的有效性.     

Discovering top-k patterns with differential privacy-an accurate approach

Frequent pattern mining discovers sets of items that frequently appear together in a transactional database; these can serve valuable economic and research purposes. However, if the database contains sensitive data (e.g., user behavior records, electronic health records), directly releasing the discovered frequent patterns with support counts will carry significant risk to the privacy of individuals. In this paper, we study the problem of how to accurately find the top-k frequent patterns with noisy support counts on transactional databases while satisfying differential privacy. We propose an algorithm, called differentially private frequent pattern (DFP-Growth), that integrates a Laplace mechanism and an exponential mechanism to avoid privacy leakage. We theoretically prove that the proposed method is (λ, δ)-useful and differentially private. To boost the accuracy of the returned noisy support counts, we take consistency constraints into account to conduct constrained inference in the post-processing step. Extensive experiments, using several real datasets, confirm that our algorithm generates highly accurate noisy support counts and top-k frequent patterns.     

Parallel Globally Consistent Normal Orientation of Raw Unorganized Point Clouds

A mandatory component for many point set algorithms is the availability of consistently oriented vertex‐normals (e.g. for surface reconstruction, feature detection, visualization). Previous orientation methods on meshes or raw point clouds do not consider a global context, are often based on unrealistic assumptions, or have extremely long computation times, making them unusable on real‐world data. We present a novel massively parallelized method to compute globally consistent oriented point normals for raw and unsorted point clouds. Built on the idea of graph‐based energy optimization, we create a complete kNN‐graph over the entire point cloud. A new weighted similarity criterion encodes the graph‐energy. To orient normals in a globally consistent way we perform a highly parallel greedy edge collapse, which merges similar parts of the graph and orients them consistently. We compare our method to current state‐of‐the‐art approaches and achieve speedups of up to two orders of magnitude. The achieved quality of normal orientation is on par or better than existing solutions, especially for real‐world noisy 3D scanned data.     

多通道CartoonGAN下的图像风格动漫化

为解决真实图像转换为动漫风格图像出现的参数量大、图像纹理和颜色损失的问题,提出了一种多通道卡通生成对抗网络(MC_CartoonGAN).首先,使用HSCNN+(advanced CNNs for the hyperspectral reconstruction task)和遗传算法重新构建多通道图像数据集,丰富图像信息.其次,利用DenseNet网络进行特征复用减少参数的内存占用率及缓解梯度消失的问题.最后,引入多通道颜色重建损失函数,在保证了生成图像内容完整的情况下,降低了生成图像的颜色损失.实验结果表明,提出的多通道卡通生成对抗网络将真实图像转换成动漫风格图像的质量更优.     

Training a neural network to select dispatching rules in real time

Dispatching rules are often suggested to schedule manufacturing systems in real-time. Numerous dispatching rules exist. Unfortunately no dispatching rule (DR) is known to be globally better than any other. Their efficiency depends on the characteristics of the system, operating condition parameters and the production objectives. Several authors have demonstrated the benefits of changing dynamically these rules, so as to take into account the changes that can occur in the system state. A new approach based on neural networks (NN) is proposed here to select in real time, each time a resource becomes available, the most suited DR. The selection is made in accordance with the current system state and the workshop operating condition parameters. Contrarily to the few learning approaches presented in the literature to select scheduling heuristics, no training set is needed. The NN parameters are determined through simulation optimization. The benefits of the proposed approach are illustrated through the example of a simplified flow-shop already published. It is shown that the NN can automatically select efficient DRs dynamically: the knowledge is only generated from simulation experiments, which are driven by the optimization method. Once trained offline, the resulting NN can be used online, in connection with the monitoring system of a flexible manufacturing system.     

Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization

The MEME algorithm extends the expectation maximization (EM) algorithm for identifying motifs in unaligned biopolymer sequences. The aim of MEME is to discover new motifs in a set of biopolymer sequences where little or nothing is known in advance about any motifs that may be present. MEME innovations expand the range of problems which can be solved using EM and increase the chance of finding good solutions. First, subsequences which actually occur in the biopolymer sequences are used as starting points for the EM algorithm to increase the probability of finding globally optimal motifs. Second, the assumption that each sequence contains exactly one occurrence of the shared motif is removed. This allows multiple appearances of a motif to occur in any sequence and permits the algorithm to ignore sequences with no appearance of the shared motif, increasing its resistance to noisy data. Third, a method for probabilistically erasing shared motifs after they are found is incorporated so that several distinct motifs can be found in the same set of sequences, both when different motifs appear in different sequences and when a single sequence may contain multiple motifs. Experiments show that MEME can discover both the CRP and LexA binding sites from a set of sequences which contain one or both sites, and that MEME can discover both the –10 and –35 promoter regions in a set of E. coli sequences.     

Optimal multilinear estimation of a random vector under constraints of causality and limited memory

A new technique is provided for random vector estimation from noisy data under the constraints that the estimator is causal and dependent on at most a finite number p of observations. Nonlinear estimators defined by multilinear operators of degree r are employed, the choice of r allowing a trade-off between the accuracy of the optimal filter and the complexity of the calculations. The techniques utilise an exact correspondence of the nonlinear problem to a corresponding linear one. This is then solved by a new procedure, the least squares singular pivot algorithm, whereby the linear problem can be repeated reduced to smaller structurally similar problems. Invertibility of the relevant covariance matrices is not assumed. Numerical experiments with real data are used to illustrate the efficacy of the new algorithm.     

Learning to Recognize and Grasp Objects

We apply techniques of computer vision and neural network learning to get a versatile robot manipulator. All work conducted follows the principle of autonomous learning from visual demonstration. The user must demonstrate the relevant objects, situations, and/or actions, and the robot vision system must learn from those. For approaching and grasping technical objects three principal tasks have to be done—calibrating the camera-robot coordination, detecting the desired object in the images, and choosing a stable grasping pose. These procedures are based on (nonlinear) functions, which are not known a priori and therefore have to be learned. We uniformly approximate the necessary functions by networks of gaussian basis functions (GBF networks). By modifying the number of basis functions and/or the size of the gaussian support the quality of the function approximation changes. The appropriate configuration is learned in the training phase and applied during the operation phase. All experiments are carried out in real world applications using an industrial articulation robot manipulator and the computer vision system KHOROS.     

Validating viral marketing strategies in Twitter via agent-based social simulation

A number of marketing phenomena are too complex for conventional analytical or empirical approaches. This makes marketing a costly process of trial and error: proposing, imagining, trying in the real world, and seeing results. Alternatively, Agent-based Social Simulation (ABSS) is becoming the most popular approach to model and study these phenomena. This research paradigm allows modeling a virtual market to: design, understand, and evaluate marketing hypotheses before taking them to the real world. However, there are shortcomings in the specialized literature such as the lack of methods, data, and implemented tools to deploy a realistic virtual market with ABSS. To advance the state of the art in this complex and interesting problem, this paper is a seven-fold contribution based on a (1) method to design and validate viral marketing strategies in Twitter by ABSS. The method is illustrated with the widely studied problem of rumor diffusion in social networks. After (2) an extensive review of the related works for this problem, (3) an innovative spread model is proposed which rests on the exploratory data analysis of two different rumor datasets in Twitter. Besides, (4) new strategies are proposed to control malicious gossips. (5) The experimental results validate the realism of this new propagation model with the datasets and (6) the strategies performance is evaluated over this model. (7) Finally, the article is complemented by a free and open-source simulator.