This paper presents a robust lossless data hiding scheme. The original cover image can be recovered without any distortion after data extraction if the stego-image remains intact, and conversely, the hidden data can still be extracted correctly if the stego-image goes through JPEG compression to some extent. A cover image is divided into a number of non-overlapping blocks, and the arithmetic difference of each block is calculated. By shifting the arithmetic difference value, we can embed bits into the blocks. The shift quantity and shifting rule are fixed for all blocks, and reversibility is achieved. Furthermore, because the bit-0- and bit-1-zones are separated and the particularity of the arithmetic differences, minor changes applied to the stego-image generated by non-malicious attacks such as JPEG compression will not cause the bit-0- and bit-1-zones to overlap, and robustness is achieved. The new embedding mechanism can enhance embedding capacity and the addition of a threshold can make the algorithm more robust. Experimental results showed that, compared with previous schemes, the performance of the proposed scheme is significantly improved.

We investigate a problem of object-oriented (OO) software quality estimation from a multi-instance (MI) perspective. In detail, each set of classes that have an inheritance relation, named ‘class hierarchy’, is regarded as a bag, while each class in the set is regarded as an instance. The learning task in this study is to estimate the label of unseen bags, i.e., the fault-proneness of untested class hierarchies. A fault-prone class hierarchy contains at least one fault-prone (negative) class, while a non-fault-prone (positive) one has no negative class. Based on the modification records (MRs) of the previous project releases and OO software metrics, the fault-proneness of an untested class hierarchy can be predicted. Several selected MI learning algorithms were evaluated on five datasets collected from an industrial software project. Among the MI learning algorithms investigated in the experiments, the kernel method using a dedicated MI-kernel was better than the others in accurately and correctly predicting the fault-proneness of the class hierarchies. In addition, when compared to a supervised support vector machine (SVM) algorithm, the MI-kernel method still had a competitive performance with much less cost.

Recently, Sun et al. (2005) highlighted the essential property of perfect forward secrecy (PFS) for e-mail protocols when a higher security level is desirable. Furthermore, Sun et al. (2005)’s protocols take only a single e-mail server into account. Actually, it is much more common that the sender and the recipient register at different e-mail servers. Compared to existing protocols, the protocol proposed in this paper takes into account the scenario that the sender and the recipient register at different servers. The proposed protocol is skillfully designed to achieve PFS and end-to-end security as well as to satisfy the requirements of confidentiality, origin, integrity and easy key management. The comparison in terms of functionality and computational efficiency demonstrates the superiority of the present scheme.

In this paper, a novel Bayesian-Gaussian neural network (BGNN) is proposed and applied to on-line modeling of a hydraulic turbine system (HTS). The new BGNN takes account of the complex nonlinear characteristics of HTS. Two redefined training procedures of the BGNN include the off-line training of the threshold matrix parameters, optimized by swarm optimization algorithms, and the on-line BGNN predictive application driven by the sliding window data method. The characteristics models of an HTS are identified using the new BGNN method and simulation results are presented which show the effectiveness of the BGNN in addressing modeling problems of HTS.

Thin metal sheets are often located in the coupling paths of magnetic coupling energy transfer (MCET) systems. Eddy currents in the metals reduce the energy transfer efficiency and can even present safety risks. This paper describes the use of etched fractal patterns in the metals to suppress the eddy currents and improve the efficiency. Simulation and experimental results show that this approach is very effective. The fractal patterns should satisfy three features, namely, breaking the metal edge, etching in the high-intensity magnetic field region, and etching through the metal in the thickness direction. Different fractal patterns lead to different results. By altering the eddy current distribution, the fractal pattern slots reduce the eddy current losses when the metals show resistance effects and suppress the induced magnetic field in the metals when the metals show inductance effects. Fractal pattern slots in multilayer high conductivity metals (e.g., Cu) reduce the induced magnetic field intensity significantly. Furthermore, transfer power, transfer efficiency, receiving efficiency, and eddy current losses all increase with the increase of the number of etched layers. These results can benefit MCET by efficient energy transfer and safe use in metal shielded equipment.

This paper presents a hardware efficient high definition television (HDTV) encoder for H.264/AVC. We use a two-level mode decision (MD) mechanism to reduce the complexity and maintain the performance, and design a sharable architecture for normal mode fractional motion estimation (NFME), special mode fractional motion estimation (SFME), and luma motion compensation (LMC), to decrease the hardware cost. Based on these technologies, we adopt a four-stage macro-block pipeline scheme using an efficient memory management strategy for the system, which greatly reduces on-chip memory and bandwidth requirements. The proposed encoder uses about 1126k gates with an average Bjontegaard-Delta peak signal-to-noise ratio (BD-PSNR) decrease of 0.5 dB, compared with JM15.0. It can fully satisfy the real-time video encoding for 1080p@30 frames/s of H.264/AVC high profile.

Vector graphic , as a kind of geometric representation of raster images, has many advantages, e.g., definition independence and editing facility. A popular way to convert raster images into vector graphics is {image meshing}, the aim of which is to find a mesh to represent an image as faithfully as possible. For traditional meshing algorithms, the crux of the problem resides mainly in the high non-linearity and non-smoothness of the objective, which makes it difficult to find a desirable optimal solution. To ameliorate this situation, we present a hierarchical optimization algorithm solving the problem from coarser levels to finer ones, providing initialization for each level with its coarser ascent. To further simplify the problem, the original non-convex problem is converted to a linear least squares one, and thus becomes convex, which makes the problem much easier to solve. A dictionary learning framework is used to combine geometry and topology elegantly. Then an alternating scheme is employed to solve both parts. Experiments show that our algorithm runs fast and achieves better results than existing ones for most images.

Carrier frequency offset (CFO) estimation is critical for orthogonal frequency-division multiplexing (OFDM) based transmissions. In this paper, we present a low-complexity, blind CFO estimator for OFDM systems with constant modulus (CM) signaling. Both single-input single-output (SISO) and multiple-input multiple-output (MIMO) systems are considered. Based on the assumption that the channel keeps constant during estimation, we prove that the CFO can be estimated uniquely and exactly through minimizing the power difference of received data on the same subcarriers between two consecutive OFDM symbols; thus, the identifiability problem is assured. Inspired by the sinusoid-like cost function, curve fitting is utilized to simplify our algorithm. Performance analysis reveals that the proposed estimator is asymptotically unbiased and the mean square error (MSE) exhibits no error floor. We show that this blind scheme can also be applied to a MIMO system. Numerical simulations show that the proposed estimator provides excellent performance compared with existing blind methods.

Congestion in wireless sensor networks (WSNs) not only causes severe information loss but also leads to excessive energy consumption. To address this problem, a novel scheme for congestion avoidance, detection and alleviation (CADA) in WSNs is proposed in this paper. By exploiting data characteristics, a small number of representative nodes are chosen from those in the event area as data sources, so that the source traffic can be suppressed proactively to avoid potential congestion. Once congestion occurs inevitably due to traffic mergence, it will be detected in a timely way by the hotspot node based on a combination of buffer occupancy and channel utilization. Congestion is then alleviated reactively by either dynamic traffic multiplexing or source rate regulation in accordance with the specific hotspot scenarios. Extensive simulation results under typical congestion scenarios are presented to illuminate the distinguished performance of the proposed scheme.

The widespread availability of portable computing power and inexpensive digital cameras are opening up new possibilities for retailers in some markets. One example is in optical shops, where a number of systems exist that facilitate eyeglasses selection. These systems are now more necessary as the market is saturated with an increasingly complex array of lenses, frames, coatings, tints, photochromic and polarizing treatments, etc. Research challenges encompass Computer Vision, Multimedia and Human-Computer Interaction. Cost factors are also of importance for widespread product acceptance. This paper describes a low-cost system that allows the user to visualize different glasses models in live video. The user can also move the glasses to adjust its position on the face. The system, which runs at 9.5 frames/s on general-purpose hardware, has a homeostatic module that keeps image parameters controlled. This is achieved by using a camera with motorized zoom, iris, white balance, etc. This feature can be specially useful in environments with changing illumination and shadows, like in an optical shop. The system also includes a face and eye detection module and a glasses management module.

An addition is a fundamental arithmetic operation which is used extensively in many very large-scale integration (VLSI) systems such as application-specific digital signal processing (DSP) and microprocessors. An adder determines the overall performance of the circuits in most of those systems. In this paper we propose a novel 1-bit full adder cell which uses only eight transistors. In this design, three multiplexers and one inverter are applied to minimize the transistor count and reduce power consumption. The power dissipation, propagation delay, and power-delay produced using the new design are analyzed and compared with those of other designs using HSPICE simulations. The results show that the proposed adder has both lower power consumption and a lower power-delay product (PDP) value. The low power and low transistor count make the novel 8T full adder cell a candidate for power-efficient applications.

目的：基于视觉的人体行为识别是一个非常活跃的研究领域。它在智能监控、感知接口和基于内容的视频检索等领域具有广泛的应用前景。然而，一些现实应用场景仍然阻碍行为识别技术的发展，比如现实场景中的动作往往是从任意角度拍摄的。因此与视点无关的行为识别显得十分重要。大量研究者开始致力于行为识别的视点无关性。本文提出一种基于多视角学习的视点无关人体行为识别方法。
创新点：针对现有多视角学习算法在构建近邻图时缺乏数据自适应性的问题，本文提出一种自适应多视角学习算法。此外，还提出一种迭代优化求解方法对所构建的目标函数进行优化求解。
方法：对于单个视角下的所有样本特征数据，构建一个该视角下的L1图。在获得数据的稀疏图结构后，对于单视角下的数据，希望学习一种最优的降维方法，在对原始数据进行降维的同时，最大程度地保持数据内在的局部结构信息；对于不同的视角，取一个非负的权重向量来衡量不同视角的重要程度。对于全部的视角可以统一起来得到目标函数。最后利用迭代优化求解，用支持向量机（SVM）分类。
结论：将本文所提算法应用到视点无关的行为识别中，实验结果表明：该算法能够自适应地选择近邻数与不同特征的权重；与其他几种对比算法相比，本文所提算法的分类准确率更高。

目的：以脑神经科学为基础，结合头朝向细胞(head direction cells)和网格细胞（grid cells）的放电特性，为运行体智能自主导航提供一种生物启发式的路径整合模型。
创新点：分别建立了头朝向细胞和网格细胞与方向和距离间的对应关系，以此解决路径整合问题。并针对模型的实现提出了网格间距和放电野半径的设置条件。
方法：首先，采用头朝向细胞感知其表征方向上的速度，并对运行方向进行度量。其次，相对头朝向细胞引入对应的距离细胞（distance cells）,用于处理各头朝向细胞感知的速度，并对相对方位进行度量。最后，距离细胞处理得到的位移输入到网格细胞中用于计算各网格细胞的放电率，并结合网格细胞的放电样式，对运行距离进行度量。以此，运行体通过各类细胞的放电情况感知运行方向、相对方位以及运行距离，最终实现路径整合。
结论：本文有效地将头朝向细胞和网格细胞的放电特性与路径整合所需的方向和距离进行了关联。运行体能够通过感知各类细胞的放电情况实现路径整合，且实现的路径整合性能具有一定稳定性。

Fine-grained access control (FGAC) must be supported by relational databases to satisfy the requirements of privacy preserving and Internet-based applications. Though much work on FGAC models has been conducted, there are still a number of ongoing problems. We propose a new FGAC model which supports the specification of open access control policies as well as closed access control policies in relational databases. The negative authorization is supported, which allows the security administrator to specify what data should not be accessed by certain users. Moreover, multiple policies defined to regulate user access together are also supported. The definition and combination algorithm of multiple policies are thus provided. Finally, we implement the proposed FGAC model as a component of the database management system (DBMS) and evaluate its performance. The performance results show that the proposed model is feasible.

This paper presents a velocity controller for the cutting system of a trench cutter (TC). The cutting velocity of a cutting system is affected by the unknown load characteristics of rock and soil. In addition, geological conditions vary with time. Due to the complex load characteristics of rock and soil, the cutting load torque of a cutter is related to the geological conditions and the feeding velocity of the cutter. Moreover, a cutter’s dynamic model is subjected to uncertainties with unknown effects on its function. In this study, to deal with the particular characteristics of a cutting system, a novel adaptive fuzzy integral sliding mode control (AFISMC) is designed for controlling cutting velocity. The model combines the robust characteristics of an integral sliding mode controller with the adaptive adjusting characteristics of an adaptive fuzzy controller. The AFISMC cutting velocity controller is synthesized using the backstepping technique. The stability of the whole system including the fuzzy inference system, integral sliding mode controller, and the cutting system is proven using the Lyapunov theory. Experiments have been conducted on a TC test bench with the AFISMC under different operating conditions. The experimental results demonstrate that the proposed AFISMC cutting velocity controller gives a superior and robust velocity tracking performance.

Inverted index traversal techniques have been studied in addressing the query processing performance challenges of web search engines, but still leave much room for improvement. In this paper, we focus on the inverted index traversal on document-sorted indexes and the optimization technique called dynamic pruning, which can efficiently reduce the hardware computational resources required. We propose another novel exhaustive index traversal scheme called largest scores first (LSF) retrieval, in which the candidates are first selected in the posting list of important query terms with the largest upper bound scores and then fully scored with the contribution of the remaining query terms. The scheme can effectively reduce the memory consumption of existing term-at-a-time (TAAT) and the candidate selection cost of existing document-at-a-time (DAAT) retrieval at the expense of revisiting the posting lists of the remaining query terms. Preliminary analysis and implementation show comparable performance between LSF and the two well-known baselines. To further reduce the number of postings that need to be revisited, we present efficient rank safe dynamic pruning techniques based on LSF, including two important optimizations called list omitting (LSF_LO) and partial scoring (LSF_PS) that make full use of query term importance. Finally, experimental results with the TREC GOV2 collection show that our new index traversal approaches reduce the query latency by almost 27% over the WAND baseline and produce slightly better results compared with the MaxScore baseline, while returning the same results as exhaustive evaluation.

Missing values occur in bio-signal processing for various reasons, including technical problems or biological characteristics. These missing values are then either simply excluded or substituted with estimated values for further processing. When the missing signal values are estimated for electroencephalography (EEG) signals, an example where electrical signals arrive quickly and successively, rapid processing of high-speed data is required for immediate decision making. In this study, we propose an incremental expectation maximization principal component analysis (iEMPCA) method that automatically estimates missing values from multivariable EEG time series data without requiring a whole and complete data set. The proposed method solves the problem of a biased model, which inevitably results from simply removing incomplete data rather than estimating them, and thus reduces the loss of information by incorporating missing values in real time. By using an incremental approach, the proposed method also minimizes memory usage and processing time of continuously arriving data. Experimental results show that the proposed method assigns more accurate missing values than previous methods.

Many important developments in video compression technologies have occurred during the past two decades. The block-based discrete cosine transform with motion compensation hybrid coding scheme has been widely employed by most available video coding standards, notably the ITU-T H.26xand ISO/IEC MPEG-xfamilies and video part of China audio video coding standard (AVS). The objective of this paper is to provide a review of the developments of the four basic building blocks of hybrid coding scheme, namely predictive coding, transform coding, quantization and entropy coding, and give theoretical analyses and summaries of the technological advancements. We further analyze the development trends and perspectives of video compression, highlighting problems and research directions.

Baseline wander is a common noise in electrocardiogram (ECG) results. To effectively correct the baseline and to preserve more underlying components of an ECG signal, we propose a simple and novel filtering method based on a statistical weighted moving average filter. Supposed a and b are the minimum and maximum of all sample values within a moving window, respectively. First, the whole region [a, b] is divided into M equal sub-regions without overlap. Second, three sub-regions with the largest sample distribution probabilities are chosen (except M<3) and incorporated into one region, denoted as [a₀, b₀] for simplicity. Third, for every sample point in the moving window, its weight is set to 1 if its value falls in [a₀, b₀]; otherwise, its weight is 0. Last, all sample points with weight 1 are averaged to estimate the baseline. The algorithm was tested by simulated ECG signal and real ECG signal from www.physionet.org. The results showed that the proposed filter could more effectively extract baseline wander from ECG signal and affect the morphological feature of ECG signal considerably less than both the traditional moving average filter and wavelet package translation did.

Software reverse engineering techniques are applied most often to reconstruct the architecture of a program with respect to quality constraints, or non-functional requirements such as maintainability or reusability. In this paper, AOPR, a novel actor-oriented program reverse engineering approach, is proposed to reconstruct an object-oriented program architecture based on a high performance model such as an actor model. Reconstructing the program architecture based on this model results in the concurrent execution of the program invocations and consequently increases the overall performance of the program provided enough processors are available. The proposed reverse engineering approach applies a hill climbing clustering algorithm to find actors.

We present an antenna-in-package system integrated with a meander line antenna based on low temperature co-fired ceramic (LTCC) technology. The proposed system employs a meander line patch antenna, a packaging layer, and a laminated multi-chip module (MCM) for integration of integrated circuit (IC) bare chips. A microstrip feed line is used to reduce the interaction between patch and package. To decrease electromagnetic coupling, a via hole structure is designed and analyzed. The meander line antenna achieved a bandwidth of 220 MHz with the center frequency at 2.4 GHz, a maximum gain of 2.2 dB, and a radiation efficiency about 90% over its operational frequency. The whole system, with a small size of 20.2 mm × 6.1 mm × 2.6 mm, can be easily realized by a standard LTCC process. This antenna-in-package system integrated with a meander line antenna was fabricated and the experimental results agreed with simulations well.

Automatic pectoral muscle removal on medio-lateral oblique (MLO) view of mammogram is an essential step for many mammographic processing algorithms. However, it is still a very difficult task since the sizes, the shapes and the intensity contrasts of pectoral muscles change greatly from one MLO view to another. In this paper, we propose a novel method based on a discrete time Markov chain (DTMC) and an active contour model to automatically detect the pectoral muscle boundary. DTMC is used to model two important characteristics of the pectoral muscle edge, i.e., continuity and uncertainty. After obtaining a rough boundary, an active contour model is applied to refine the detection results. The experimental results on images from the Digital Database for Screening Mammography (DDSM) showed that our method can overcome many limitations of existing algorithms. The false positive (FP) and false negative (FN) pixel percentages are less than 5% in 77.5% mammograms. The detection precision of 91% meets the clinical requirement.

We investigate the bandwidth allocation and power control schemes in orthogonal frequency division multiplexing (OFDM) based multi-hop cognitive radio networks, and the color-sensitive graph coloring (CSGC) model is viewed as an efficient solution to the spectrum assignment problem. We extend the model by taking into account the power control strategy to avoid interference among secondary users and adapt dynamic topology. We formulate the optimization problem encompassing the channel allocation, power control with the interference constrained below a tolerable limit. The optimization objective with two different optimization strategies focuses on the routes rather than the links as in traditional approaches. A heuristic solution to this nondeterministic polynomial (NP)-hard problem is presented, which performs iterative channel allocation according to the lowest transmission power that guarantees the link connection and makes channel reuse as much as possible, and then the transmission power of each link is maximized to improve the channel capacity by gradually adding power level from the lowest transmission power until all co-channel links cannot satisfy the interference constraints. Numerical results show that our proposed strategies outperform the existing spectrum assignment algorithms in the performance of both the total network bandwidth and minimum route bandwidth of all routes, meanwhile, saving the transmission power.

In this paper, we propose an image driven shape deformation approach for stylizing a 3D mesh using styles learned from existing 2D illustrations. Our approach models a 2D illustration as a planar mesh and represents the shape styles with four components: the object contour, the context curves, user-specified features and local shape details. After the correspondence between the input model and the 2D illustration is established, shape stylization is formulated as a style-constrained differential mesh editing problem. A distinguishing feature of our approach is that it allows users to directly transfer styles from hand-drawn 2D illustrations with individual perception and cognition, which are difficult to identify and create with 3D modeling and editing approaches. We present a sequence of challenging examples including unrealistic and exaggerated paintings to illustrate the effectiveness of our approach.

Optical flow estimation is still an important task in computer vision with many interesting applications. However, the results obtained by most of the optical flow techniques are affected by motion discontinuities or illumination changes. In this paper, we introduce a brightness correction field combined with a gradient constancy constraint to reduce the sensibility to brightness changes between images to be estimated. The advantage of this brightness correction field is its simplicity in terms of computational complexity and implementation. By analyzing the deficiencies of the traditional total variation regularization term in weakly textured areas, we also adopt a structure-adaptive regularization based on the robust Huber norm to preserve motion discontinuities. Finally, the proposed energy functional is minimized by solving its corresponding Euler-Lagrange equation in a more effective multi-resolution scheme, which integrates the twice downsampling strategy with a support-weight median filter. Numerous experiments show that our method is more effective and produces more accurate results for optical flow estimation.

In this Exa byte scale era, data increases at an exponential rate. This is in turn generating a massive amount of metadata in the file system. Hadoop is the most widely used framework to deal with big data. Due to this growth of huge amount of metadata, however, the efficiency of Hadoop is questioned numerous times by many researchers. Therefore, it is essential to create an efficient and scalable metadata management for Hadoop. Hash-based mapping and subtree partitioning are suitable in distributed metadata management schemes. Subtree partitioning does not uniformly distribute workload among the metadata servers, and metadata needs to be migrated to keep the load roughly balanced. Hash-based mapping suffers from a constraint on the locality of metadata, though it uniformly distributes the load among NameNodes, which are the metadata servers of Hadoop. In this paper, we present a circular metadata management mechanism named dynamic circular metadata splitting (DCMS). DCMS preserves metadata locality using consistent hashing and locality-preserving hashing, keeps replicated metadata for excellent reliability, and dynamically distributes metadata among the NameNodes to keep load balancing. NameNode is a centralized heart of the Hadoop. Keeping the directory tree of all files, failure of which causes the single point of failure (SPOF). DCMS removes Hadoop’s SPOF and provides an efficient and scalable metadata management. The new framework is named ‘Dr. Hadoop’ after the name of the authors.

A general construction of inter-group complementary (IGC) codes is proposed based on perfect complementary (PC) codes, interleaving operation, and the orthogonal matrix. The correlation properties of the newly constructed IGC codes can be described as follows: (1) the autocorrelation sidelobes of the codes are zeros in the zero correlation zone (ZCZ); (2) the cross-correlation functions (CCFs) between any two different codes of the same group are zeros in the ZCZ; (3) the CCFs between any two codes of different groups are zeros everywhere. The key point of this construction is that the ZCZ length of the generated IGC codes can be chosen flexibly. It is well known that there is a limitation between the ZCZ length and the number of mates; that is, the smaller is the length of ZCZ, the more are the IGC codes that can be generated. Therefore, if we can choose the ZCZ length of the IGC codes flexibly according to the requirement of the system, more users can be accommodated in the system.

We introduce a kind of shape-adjustable spline curves defined over a non-uniform knot sequence. These curves not only have the many valued properties of the usual non-uniform B-spline curves, but also are shape adjustable under fixed control polygons. Our method is based on the degree elevation of B-spline curves, where maximum degrees of freedom are added to a curve parameterized in terms of a non-uniform B-spline. We also discuss the geometric effect of the adjustment of shape parameters and propose practical shape modification algorithms, which are indispensable from the user’s perspective.

目的：研究大规模数据的主题模型在线推理算法，针对随机变分推理算法中随机梯度误差较大的问题，提出一种移动平均随机变分推理算法。
创新点：使用多次迭代的随机梯度移动平均值近似代替真实随机梯度，以此减小随机梯度和真实梯度间的误差。
方法：以主题模型的基础模型潜在狄利克雷分配为载体展开研究。考虑不同次迭代的文本子集具有不同的词汇（表1），使用不同次迭代的随机项移动平均值近似代替真实随机梯度的随机项。为尽可能保证算法的精度，使用最近R次迭代的随机项（图2）并验证所提算法的收敛性。
结论：在随机变分推理算法基础上，提出一种移动平均随机变分推理算法，实现更好的文本主题建模效果和更快的收敛速度。

Contrast evaluation can be used as a criterion to evaluate performance of contrast enhancement algorithms and to compare contrast capability of display systems. This paper deals with contrast evaluation models for natural color images. Two separate models are defined for within- and cross-content evaluations. The former is to differentiate the perceived contrast of the images with the same content. The latter is to discriminate the differences in contrast among the images with different contents. Perception mechanisms are quite different for within- and cross-content evaluations. Local contrast plays an important role in within-content evaluation. In contrast, global contrast dominates the contrast perception for cross-content evaluation. Results of human visual experiments show that the proposed evaluation models outperform previous methods for both within- and cross-content evaluations.