In video applications, real-time image scaling techniques are often required. In this paper, an efficient implementation of a scaling engine based on 4×4 cubic convolution is proposed. The cubic convolution has a better performance than other traditional interpolation kernels and can also be realized on hardware. The engine is designed to perform arbitrary scaling ratios with an image resolution smaller than 2560×1920 pixels and can scale up or down, in horizontal or vertical direction. It is composed of four functional units and five line buffers, which makes it more competitive than conventional architectures. A strict fixed-point strategy is applied to minimize the quantization errors of hardware realization. Experimental results show that the engine provides a better image quality and a comparatively lower hardware cost than reference implementations.

Optical proximity correction (OPC) is a key step in modern integrated circuit (IC) manufacturing. The quality of model-based OPC (MB-OPC) is directly determined by segment offsets after OPC processing. However, in conventional MB-OPC, the intensity of a control site is adjusted only by the movement of its corresponding segment; this scheme is no longer accurate enough as the lithography process advances. On the other hand, matrix MB-OPC is too time-consuming to become practical. In this paper, we propose a new sparse matrix MB-OPC algorithm with model-based mapping between segments and control sites. We put forward the concept of ‘sensitive area’. When the Jacobian matrix used in the matrix MB-OPC is evaluated, only the elements that correspond to the segments in the sensitive area of every control site need to be calculated, while the others can be set to 0. The new algorithm can effectively improve the sparsity of the Jacobian matrix, and hence reduce the computations. Both theoretical analysis and experiments show that the sparse matrix MB-OPC with model-based mapping is more accurate than conventional MB-OPC, and much faster than matrix MB-OPC while maintaining high accuracy.

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.

Online monitoring of temporally-sequenced news streams for interesting patterns and trends has gained popularity in the last decade. In this paper, we study a particular news stream monitoring task: timely detection of bursty events which have happened recently and discovery of their evolutionary patterns along the timeline. Here, a news stream is represented as feature streams of tens of thousands of features (i.e., keyword. Each news story consists of a set of keywords.). A bursty event therefore is composed of a group of bursty features, which show bursty rises in frequency as the related event emerges. In this paper, we give a formal definition to the above problem and present a solution with the following steps: (1) applying an online multi-resolution burst detection method to identify bursty features with different bursty durations within a recent time period; (2) clustering bursty features to form bursty events and associating each event with a power value which reflects its bursty level; (3) applying an information retrieval method based on cosine similarity to discover the event’s evolution (i.e., highly related bursty events in history) along the timeline. We extensively evaluate the proposed methods on the Reuters Corpus Volume 1. Experimental results show that our methods can detect bursty events in a timely way and effectively discover their evolution. The power values used in our model not only measure event’s bursty level or relative importance well at a certain time point but also show relative strengths of events along the same evolution.

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.

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.

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.

Selecting the optimal parameters for support vector machine (SVM) has long been a hot research topic. Aiming for support vector classification/regression (SVC/SVR) with the radial basis function (RBF) kernel, we summarize the rough line rule of the penalty parameter and kernel width, and propose a novel linear search method to obtain these two optimal parameters. We use a direct-setting method with thresholds to set the epsilon parameter of SVR. The proposed method directly locates the right search field, which greatly saves computing time and achieves a stable, high accuracy. The method is more competitive for both SVC and SVR. It is easy to use and feasible for a new data set without any adjustments, since it requires no parameters to set.

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, 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.

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 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.

We present an algorithm for image compression based on an image inpainting method. First the image regions that can be accurately recovered are located. Then, to reduce the data, information of such regions is removed. The remaining data besides essential details for recovering the removed regions are encoded to produce output data. At the decoder, an inpainting method is applied to retrieve removed regions using information extracted at the encoder. The image inpainting technique utilizes partial differential equations (PDEs) for recovering information. It is designed to achieve high performance in terms of image compression criteria. This algorithm was examined for various images. A high compression ratio of 1:40 was achieved at an acceptable quality. Experimental results showed attainable visible quality improvement at a high compression ratio compared with JPEG.

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.

One major goal of mesh parameterization is to minimize the conformal distortion. Measured boundary parameterizations focus on lowering the distortion by setting the boundary free with the help of distance from a center vertex to all the boundary vertices. Hence these parameterizations strongly depend on the determination of the center vertex. In this paper, we introduce two methods to determine the center vertex automatically. Both of them can be used as necessary supplements to the existing measured boundary methods to minimize the common artifacts as a result of the obscure choice of the center vertex. In addition, we propose a simple and fast measured boundary parameterization method based on the Poisson’s equation. Our new approach generates less conformal distortion than the fixed boundary methods. It also generates more regular domain boundaries than other measured boundary methods. Moreover, it offers a good tradeoff between computation costs and conformal distortion compared with the fast and robust angle based flattening (ABF++).

It is universally acknowledged by network security experts that proactive peer-to-peer (P2P) worms may soon engender serious threats to the Internet infrastructures. These latent threats stimulate activities of modeling and analysis of the proactive P2P worm propagation. Based on the classical two-factor model, in this paper, we propose a novel proactive worm propagation model in unstructured P2P networks (called the four-factor model) by considering four factors: (1) network topology, (2) countermeasures taken by Internet service providers (ISPs) and users, (3) configuration diversity of nodes in the P2P network, and (4) attack and defense strategies. Simulations and experiments show that proactive P2P worms can be slowed down by two ways: improvement of the configuration diversity of the P2P network and using powerful rules to reinforce the most connected nodes from being compromised. The four-factor model provides a better description and prediction of the proactive P2P worm propagation.

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.

This paper deals with a unified and novel approach for analyzing the frequency and time domain performance of grounding systems. The proposed procedure is based on solving the full set of Maxwell’s equations in the frequency domain, and enables the exact computation of very near fields at the surface of the grounding grid, as well as far fields, by simple and accurate closed-form expressions for solving Sommerfeld integrals. In addition, the soil ionization is easily considered in the proposed method. The frequency domain responses are converted to the time domain by fast inverse Laplace transform. The results are validated and have shown acceptable accuracy.

A model predictive controller was designed in this study for a single supply chain unit. A demand model was described using an autoregressive integrated moving average (ARIMA) model, one that is identified on-line to forecast the future demand. Feedback was used to modify the demand prediction, and profit was chosen as the control objective. To imitate reality, the purchase price was assumed to be a piecewise linear form, whereby the control objective became a nonlinear problem. In addition, a genetic algorithm was introduced to solve the problem. Constraints were put on the predictive inventory to control the inventory fluctuation, that is, the bullwhip effect was controllable. The model predictive control (MPC) method was compared with the order-up-to-level (OUL) method in simulations. The results revealed that using the MPC method can result in more profit and make the bullwhip effect controllable.

The problems existing in the binary logic system and the advantages of multiple-valued logic (MVL) are introduced. A literal circuit with three-track-output structure is created based on resonant tunneling diodes (RTDs) and it has the most basic memory function. A ternary RTD D flip-flop with pre-set and pre-reset functions is also designed, the key module of which is the RTD literal circuit. Two types of output structure of the ternary RTD D flip-flop are optional: one is three-track and the other is single-track; these two structures can be transformed conveniently by merely adding tri-valued RTD NAND, NOR, and inverter units after the three-track output. The design is verified by simulation. Ternary flip-flop consists of an RTD literal circuit and it not only is easy to understand and implement but also provides a solution for the algebraic interface between the multiple-valued logic and the binary logic. The method can also be used for design of other types of multiple-valued RTD flip-flop circuits.

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.

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.

A lack of myoelectric sources after limb amputation is a critical challenge in the control of multifunctional motorized prostheses. To reconstruct myoelectric sources physiologically related to lost limbs, a newly proposed neural-function construction method, targeted muscle reinnervation (TMR), appears promising. Recent advances in the TMR technique suggest that TMR could provide additional motor command information for the control of multifunctional myoelectric prostheses. However, little is known about the nature of the physiological functional recovery of the reinnervated muscles. More understanding of the underlying mechanism of TMR could help us fine tune the technique to maximize its capability to achieve a much higher performance in the control of multifunctional prostheses. In this study, rats were used as an animal model for TMR surgery involving transferring a median nerve into the pectoralis major, which served as the target muscle. Intramuscular myoelectric signals reconstructed following TMR were recorded by implanted wire electrodes and analyzed to explore the nature of the neural-function reconstruction achieved by reinnervation of targeted muscles. Our results showed that the active myoelectric signal reconstructed in the targeted muscle was acquired one week after TMR surgery, and its amplitude gradually became stronger over time. These preliminary results from rats may serve as a basis for exploring the mechanism of neural-function reconstruction by the TMR technique in human subjects.

With the continual increase in switching speed and rating of power semiconductors, the switching voltage spike becomes a serious problem. This paper describes a new technique of driving pulse edge modulation for insulated gate bipolar transistors (IGBTs). By modulating the density and width of the pulse trains, without regulating the hardware circuit, the slope of the gate driving voltage is controlled to change the switching speed. This technique is used in the driving circuit based on complex programmable logic devices (CPLDs), and the switching voltage spike of IGBTs can be restrained through software, which is easier and more flexible to adjust. Experimental results demonstrate the effectiveness and practicability of the proposed method.

Deformation is an important research topic in graphics. There are two key issues in mesh deformation: (1) self-intersection and (2) volume preserving. In this paper, we present a new method to construct a vector field for volume-preserving mesh deformation of free-form objects. Volume-preserving is an inherent feature of a curl vector field. Since the field lines of the curl vector field will never intersect with each other, a mesh deformed under a curl vector field can avoid self-intersection between field lines. Designing the vector field based on curl is useful in preserving graphic features and preventing self-intersection. Our proposed algorithm introduces distance field into vector field construction; as a result, the shape of the curl vector field is closely related to the object shape. We define the construction of the curl vector field for translation and rotation and provide some special effects such as twisting and bending. Taking into account the information of the object, this approach can provide easy and intuitive construction for free-form objects. Experimental results show that the approach works effectively in real-time animation.

In the training of feedforward neural networks, it is usually suggested that the initial weights should be small in magnitude in order to prevent premature saturation. The aim of this paper is to point out the other side of the story: In some cases, the gradient of the error functions is zero not only for infinitely large weights but also for zero weights. Slow convergence in the beginning of the training procedure is often the result of sufficiently small initial weights. Therefore, we suggest that, in these cases, the initial values of the weights should be neither too large, nor too small. For instance, a typical range of choices of the initial weights might be something like (?0.4, ?0.1)∪(0.1,0.4), rather than (?0.1, 0.1) as suggested by the usual strategy. Our theory that medium size weights should be used has also been extended to a few commonly used transfer functions and error functions. Numerical experiments are carried out to support our theoretical findings.

We propose a robust digital watermarking algorithm for copyright protection. A stable feature is obtained by utilizing QR factorization and discrete cosine transform (DCT) techniques, and a meaningful watermark image is embedded into an image by modifying the stable feature with a quantization index modulation (QIM) method. The combination of QR factorization, DCT, and QIM techniques guarantees the robustness of the algorithm. Furthermore, an embedding location selection method is exploited to select blocks with small modifications as the embedding locations. This can minimize the embedding distortion and greatly improve the imperceptibility of our scheme. Several standard images were tested and the experimental results were compared with those of other published schemes. The results demonstrate that our proposed scheme can achieve not only better imperceptibility, but also stronger robustness against common signal processing operations and lossy compressions, such as filtering, noise addition, scaling, sharpening, rotation, cropping, and JPEG/JPEG2000 compression.

The gradients of a quaternion-valued function are often required for quaternionic signal processing algorithms. The HR gradient operator provides a viable framework and has found a number of applications. However, the applications so far have been limited to mainly real-valued quaternion functions and linear quaternion-valued functions. To generalize the operator to nonlinear quaternion functions, we define a restricted version of the HR operator, which comes in two versions, the left and the right ones. We then present a detailed analysis of the properties of the operators, including several different product rules and chain rules. Using the new rules, we derive explicit expressions for the derivatives of a class of regular nonlinear quaternion-valued functions, and prove that the restricted HR gradients are consistent with the gradients in the real domain. As an application, the derivation of the least mean square algorithm and a nonlinear adaptive algorithm is provided. Simulation results based on vector sensor arrays are presented as an example to demonstrate the effectiveness of the quaternion-valued signal model and the derived signal processing algorithm.

With the advance of automation technology, the scale of industrial communication networks at field level is growing. Guaranteeing real-time performance of these networks is therefore becoming an increasingly difficult task. This paper addresses the optimization of device allocation in industrial Ethernet networks with real-time constraints (DAIEN-RC). Considering the inherent diversity of real-time requirements of typical industrial applications, a novel optimization criterion based on relative delay is proposed. A hybrid genetic algorithm incorporating a reduced variable neighborhood search (GA-rVNS) is developed for DAIEN-RC. Experimental results show that the proposed novel scheme achieves a superior performance compared to existing schemes, especially for large scale industrial networks.

Emotion recognition via facial expressions (ERFE) has attracted a great deal of interest with recent advances in artificial intelligence and pattern recognition. Most studies are based on 2D images, and their performance is usually computationally expensive. In this paper, we propose a real-time emotion recognition approach based on both 2D and 3D facial expression features captured by Kinect sensors. To capture the deformation of the 3D mesh during facial expression, we combine the features of animation units (AUs) and feature point positions (FPPs) tracked by Kinect. A fusion algorithm based on improved emotional profiles (IEPs) and maximum confidence is proposed to recognize emotions with these real-time facial expression features. Experiments on both an emotion dataset and a real-time video show the superior performance of our method.