Lapped transform‐based codec for frame‐memory reduction in super‐quality LCD overdrive

Abstract— Overdrive is commonly used to reduce the liquid‐crystal response time and motion blur in liquid‐crystal displays (LCDs). However, overdrive requires a large frame memory in order to store the previous frame for reference. In this paper, a lapped transform‐based codec (LTC) is proposed to reduce the frame memory needed for LCD overdrive. In the latest literature, a directional prediction‐based codec (DPC) employs eight directional predictions, which takes up a large percentage of the computational complexity of the codec and does not consider the de‐correlation of the inter‐blocks. Therefore, the LTC first uses the lapped transform to decompose the correlation of the inter‐blocks in the YUV color space. A hadamard transform is then used for energy compaction. The reordered coefficients are pre‐quantized and encoded using the proposed adaptive bit‐plane coding (ABPC) method for a simple hardware implementation. The simulation results show that the proposed LTC significantly improves the DPC in both subjective and objective performance and outperforms the block truncation coding (BTC) and adaptive multi‐level BTC (AM‐BTC), which have been described in the literature.     

Shape "break-and-repair" strategy and its application to automated medical image segmentation

In three-dimensional medical imaging, segmentation of specific anatomy structure is often a preprocessing step for computer-aided detection/diagnosis (CAD) purposes, and its performance has a significant impact on diagnosis of diseases as well as objective quantitative assessment of therapeutic efficacy. However, the existence of various diseases, image noise or artifacts, and individual anatomical variety generally impose a challenge for accurate segmentation of specific structures. To address these problems, a shape analysis strategy termed "break-and-repair" is presented in this study to facilitate automated medical image segmentation. Similar to surface approximation using a limited number of control points, the basic idea is to remove problematic regions and then estimate a smooth and complete surface shape by representing the remaining regions with high fidelity as an implicit function. The innovation of this shape analysis strategy is the capability of solving challenging medical image segmentation problems in a unified framework, regardless of the variability of anatomical structures in question. In our implementation, principal curvature analysis is used to identify and remove the problematic regions and radial basis function (RBF) based implicit surface fitting is used to achieve a closed (or complete) surface boundary. The feasibility and performance of this strategy are demonstrated by applying it to automated segmentation of two completely different anatomical structures depicted on CT examinations, namely human lungs and pulmonary nodules. Our quantitative experiments on a large number of clinical CT examinations collected from different sources demonstrate the accuracy, robustness, and generality of the shape "break-and-repair" strategy in medical image segmentation.     

Hilbert‐shaped complementary ring resonator and application to enhanced‐performance low pass filter with high selectivity

In this article, a novel single negative metamaterial (MTM) transmission line (TL) consisting of a Hilbert‐shaped complementary ring resonator (H‐CRR) on the ground plane is initially presented and studied in depth. Then based on the proposed MTM TL, a novel six‐section Hi‐Lo microstrip low‐pass filter (LPF) with a cut‐off frequency 2.5 GHz is developed, fabricated, and measured. Measurement results indicate that: by integrating H‐CRR, the selectivity has been significantly improved which is 77.3 dB/GHz due to the single negative permittivity; by etching a crown square on low‐impedance section, the bandwidth characterized by 20 dB return loss was obviously enhanced by 26.2% and the maximal sidelobe level of in‐band return loss was reduced from 22 to 24.6 dB. What' more, the developed LPF achieved a 36.3% size reduction with respect to its conventional counterpart. The design concept, which was confirmed by the measurement data, is of practical value and can be popularized in other microwave devices where high selectivity is requested. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2011.     

Community discovery using nonnegative matrix factorization

Complex networks exist in a wide range of real world systems, such as social networks, technological networks, and biological networks. During the last decades, many researchers have concentrated on exploring some common things contained in those large networks include the small-world property, power-law degree distributions, and network connectivity. In this paper, we will investigate another important issue, community discovery, in network analysis. We choose Nonnegative Matrix Factorization (NMF) as our tool to find the communities because of its powerful interpretability and close relationship between clustering methods. Targeting different types of networks (undirected, directed and compound), we propose three NMF techniques (Symmetric NMF, Asymmetric NMF and Joint NMF). The correctness and convergence properties of those algorithms are also studied. Finally the experiments on real world networks are presented to show the effectiveness of the proposed methods.     

Application of enhanced cluster validity index function to automatic stock portfolio selection system

This paper presents an automatic stock portfolio selection system. In the proposed approach, 53 financial indices are collected for each stock item and are consolidated into six financial ratios [Grey relational grades (GRGs)] using a Grey relational analysis model. The GRGs are processed using a modified form of the PBMF index method (designated as the Huang index function) to determine the optimal number of clusters per GRG. The resulting cluster indices are then processed using rough set theory to identify the stocks within the lower approximate sets. Finally, the GRGs of each stock item in the lower approximate sets are consolidated into a single GRG, indicating the ability of the stock item to maximize the rate of return. It is demonstrated that the proposed stock selection mechanism yields a higher rate of return than several existing portfolio selection systems.     

A job grouping approach for planning container transfers at automated seaport container terminals

This paper proposes a practical job grouping approach, which aims to enhance the time related performance metrics of container transfers in the Patrick AutoStrad container terminal, located in Brisbane, Australia. It first formulates a mathematical model of the automated container transfers in a relatively complex environment. Apart from the consideration on collision avoidance of a fleet of large vehicles in a confined area, it also deals with many other difficult practical challenges such as the presence of multiple levels of container stacking and sequencing, variable container orientations, and vehicular dynamics that require finite acceleration and deceleration times. The proposed job grouping approach aims to improve the makespan of the schedule for yard jobs, while reducing straddle carrier waiting time by grouping jobs using a guiding function. The performance of the current sequential job allocation method and the proposed job grouping approach are evaluated and compared statistically using a pooled t-test for 30 randomly generated yard configurations. The experimental results show that the job grouping approach can effectively improve the schedule makespan and reduce the total straddle carrier waiting time.     

Minimax mean-variance models for fuzzy portfolio selection

This paper discusses fuzzy portfolio selection problem in the situation where each security return belongs to a certain class of fuzzy variables but the exact fuzzy variable cannot be given. Two credibility-based minimax mean-variance models are proposed. The crisp equivalents of the models to linear programming ones are given in three special cases. In addition, a general solution algorithm is also provided. To help understand the modeling idea and to illustrate the effectiveness of the proposed algorithm, one example is presented.     

基于SIMD结构的多标准LDPC译码器的VLSI实现

近年来,支持多标准的LDPC译码器已逐渐成为研究的热点.与传统译码器相比,所设计的LDPC译码器具有以下优点:1.实现了一个码率、码长可配置结构,进而可以支持多种标准;2.采用了一种改进型TPMP算法,使译码器的存储器容量大大减少,避免了因分块LDPC码的非规则性所造成的数据冲突问题;3.采用基于SIMD处理器的硬件结构,实现了硬件的高度规整性,易于芯片布局布线;4.设计了一个6级可配置流水线,可分时构造校验节点处理单元和变量节点处理单元,提高了硬件利用率和系统数据吞吐率.用这种架构实现了一个同时支持CMMB和DTMB两个标准的多标准LDPC译码器;芯片规模为75万门,时钟频率为220MHz,数据吞吐率为300Mbps.     

物联网环境下智能插座的设计

实时反馈机制被普遍认为是一种有效的节能方式,同时物联网技术使这种方式变得更为舒适便捷.提出在物联网环境下使用智能插座来实现电能的实时反馈.详细介绍了构成智能插座的电源模块、电能传感模块、电器通断模块、存储模块、处理器和无线通信模块的设计.此外还对电源模块、无线通信模块、电能传感模块、以及实际的节能效果方面进行了测试验证.     

LILT:一个基于"乐纹"技术的音乐分享系统

随着Web2. 0的推广,音乐分享平台纷纷出现.目前的音乐分享平台在用户上传时没有进行检查,导致音乐库里常有大量的冗余.提出了一个基于乐纹的音乐分享平台系统LILT,该系统包含一个含有音乐乐纹的数据库,并通过提取音乐片段的乐纹进行查找判重.建立了6万余首歌的乐纹库,并且在这个库上做了大量测试.测试结果显示,该系统对用户上传的音乐能够有效地识别并判重.在系统识别率、识别速度、指纹鲁棒性测试中均得到了较满意的结果.