A concurrent model of computation and a language based on the model for bit-level operation are useful for developing asynchronous and concurrent programs compositionally, which frequently use bit-level operations. Some examples are programs for video games, hardware emulation (including virtual machines), and signal processing. However, few models and languages are optimized and oriented to bit-level concurrent computation. We previously developed a visual programming language called A-BITS for bit-level concurrent programming. The language is based on a dataflow-like model that computes using processes that provide serial bit-level operations and FIFO buffers connected to them. It can express bit-level computation naturally and develop compositionally. We then devised a concurrent computation model called APEC (Asynchronous Program Elements Connection) for bit-level concurrent computation. This model enables precise and formal expression of the process of computation, and a notion of primitive program elements for controlling and operating can be expressed synthetically. Specifically, the model is based on a notion of uniform primitive processes, called primitives, that have three terminals and four ordered rules at most, as well as on bidirectional communication using vehicles called carriers. A new notion is that a carrier moving between two terminals can briefly express some kinds of computation such as synchronization and bidirectional communication. The model's properties make it most applicable to bit-level computation compositionally, since the uniform computation elements are enough to develop components that have practical functionality. Through future application of the model, our research may enable further research on a base model of fine-grain parallel computer architecture, since the model is suitable for expressing massive concurrency by a network of primitives.

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Energy-efficiency is one of the main concerns in the wireless information dissemination system. This paper presents a wireless broadcast stream organization scheme which enables complex queries (e. g., aggregation queries) to be processed in an energy-efficient way. For efficient processing of complex queries, we propose an approach of broadcasting their pre-computed results with the data stream, wherein the way of replication of index and pre-computation results are investigated. Through analysis and experiments, we show that the new approach can achieve significant performance enhancement for complex queries with respect to the access time and tuning time.

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Software rejuvenation is a preventive and proactive solution that is particularly useful for counteracting the phenomenon of software aging. In this article, we consider periodic software rejuvenation models based on the expected cost per unit time in the steady state under discrete-time operation circumstance. By applying the discrete renewal reward processes, we describe the stochastic behavior of a telecommunication billing application with a degradation mode, and determine the optimal periodic software rejuvenation schedule minimizing the expected cost. Similar to the earlier work by the same authors, we develop a statistically non-parametric algorithm to estimate the optimal software rejuvenation schedule, by applying the discrete total time on test concept. Numerical examples are presented to estimate the optimal software rejuvenation schedules from the simulation data. We discuss the asymptotic behavior of estimators developed in this paper.

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A new structure learning approach for Bayesian networks (BNs) based on dual genetic algorithm (DGA) is proposed in this paper. An individual of the population is represented as a dual chromosome composed of two chromosomes. The first chromosome represents the ordering among the BN nodes and the second represents the conditional dependencies among the ordered BN nodes. It is rigorously shown that there is no BN structure that cannot be encoded by the proposed dual genetic encoding and the proposed encoding explores the entire solution space of the BN structures. In contrast with existing GA-based structure learning methods, the proposed method learns not only the topology of the BN nodes, but also the ordering among the BN nodes, thereby, exploring the wider solution space of a given problem than the existing method. The dual genetic operators are closed in the set of the admissible individuals. The proposed method is applied to real-world and benchmark applications, while its effectiveness is demonstrated through computer simulation.

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In this study, we propose a method of classifying a spontaneous electroencephalogram (EEG) approach to a brain-computer interface. Ten subjects, aged 21-32 years, volunteered to imagine left-and right- hand movements. An independent component analysis based on a fixed-point algorithm is used to eliminate the activities found in the EEG signals. We use a fractal dimension value to reveal the embedded potential responses in the human brain. The different fractal dimension values between the relaxing and imaging periods are computed. Featured data is classified by a three-layer feed-forward neural network based on a simple backpropagation algorithm. Two conventional methods, namely, the use of the autoregressive (AR) model and the band power estimation (BPE) as features, and the linear discriminant analysis (LDA) as a classifier, are selected for comparison in this study. Experimental results show that the proposed method is more effective than the conventional methods.

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Image authentication is applied to protect the integrity of the digital image. Conventional image authentication mechanisms, however, are unfit for the palette-based color images. Palette-based color images such as GIF images are commonly used for media communications. This article proposes a palette-based color image authentication mechanism. This novel scheme can guarantee the essentials of general authentication schemes to protect palette-based color images. Morphological operations are adopted to draw out the tampered area precisely. According to the experimental results, the images embedded with the authentication data still can preserve high image quality; specifically, the new scheme is highly sensitive to altered areas.

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The reduction of a structural pattern at specific gray levels or at the special condition of image data has mainly been discussed in digital halftone methods. This problem is more severe in some flat panel displays because their black levels typically are brighter than other displays blocks. The authors proposed an advanced confined error diffusion (ACED) algorithm which was a well-organized halftone algorithm for flat panel devices. In this paper, we extend the ACED algorithm to the multilevel systems, which are capable of displaying more than 2 levels. Our extension has two merits for the hardware implementation. First, it can be processed in real time using the look-up table based method. The second one is the flexibility of selecting the used gray level. This paper discusses the performance of the proposed algorithms with experimental results for natural test images.

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We propose a method to prevent the degradation of decoded MPEG pictures caused by video transmission over error-prone networks. In this paper, we focus on the error concealment that is processed at the decoder without using any backchannels. Though there have been various approaches to this problem, they generally focus on minimizing the degradation measured frame by frame. Although this frame-level approach is effective in evaluating individual frame quality, in the sense of human perception, the most noticeable feature is the spatio-temporal discontinuity of the image feature in the decoded video image. We propose a novel error concealment algorithm comprising the combination of i) A spatio-temporal error recovery function with low processing cost, ii) A MB-based image fidelity tracking scheme, and iii) An adaptive post-filter using the fidelity information. It is demonstrated by experimental results that the proposed algorithm can significantly reduce the subjective degradation of corrupted MPEG video quality with about 30% of additional decoding processing power.

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In this paper we propose an efficient line feature-based 2D object recognition algorithm using a novel entropy correspondence measure (ECM) that encodes the probabilistic similarity between two line feature sets. Since the proposed ECM-based method uses the whole structural information of objects simultaneously for matching, it overcomes the common drawbacks of the conventional techniques that are based on feature to feature correspondence. Moreover, since ECM is endowed with probabilistic attribute, it shows quite robust performance in the noisy environment. In order to enhance the recognition performance and speed, line features are pre-clustered into several groups according to their inclination by an eigen analysis, and then ECM is applied to each corresponding group individually. Experimental results on real images demonstrate that the proposed algorithm has superior performance to those of the conventional algorithms in both the accuracy and the computational efficiency, in the noisy environment.

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Visual tracking is required by many vision applications such as human-computer interfaces and human-robot interactions. However, in daily living spaces where such applications are assumed to be used, stable tracking is often difficult because there are many objects which can cause the visual occlusion. While conventional tracking techniques can handle, to some extent, partial and short-term occlusion, they fail when presented with complete occlusion over long periods. They also cannot handle the case that an occluder such as a box and a bag contains and carries the tracking target inside itself, that is, the case that the target invisibly moves while being contained by the occluder. In this paper, to handle this occlusion problem, we propose a method for visual tracking by a particle filter, which switches tracking targets autonomously. In our method, if occlusion occurs during tracking, a model of the occluder is dynamically created and the tracking target is switched to this model. Thus, our method enables the tracker to indirectly track the “invisible target” by switching its target to the occluder effectively. Experimental results show the effectiveness of our method.

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This paper presents an RK-means clustering algorithm which is developed for reliable data grouping by introducing a new reliability evaluation to the K-means clustering algorithm. The conventional K-means clustering algorithm has two shortfalls: 1) the clustering result will become unreliable if the assumed number of the clusters is incorrect; 2) during the update of a cluster center, all the data points belong to that cluster are used equally without considering how distant they are to the cluster center. In this paper, we introduce a new reliability evaluation to K-means clustering algorithm by considering the triangular relationship among each data point and its two nearest cluster centers. We applied the proposed algorithm to track objects in video sequence and confirmed its effectiveness and advantages.

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This paper describes a method of producing segmentation point candidates for on-line handwritten Japanese text by a support vector machine (SVM) to improve text recognition. This method extracts multi-dimensional features from on-line strokes of handwritten text and applies the SVM to the extracted features to produces segmentation point candidates. We incorporate the method into the segmentation by recognition scheme based on a stochastic model which evaluates the likelihood composed of character pattern structure, character segmentation, character recognition and context to finally determine segmentation points and recognize handwritten Japanese text. This paper also shows the details of generating segmentation point candidates in order to achieve high discrimination rate by finding the optimal combination of the segmentation threshold and the concatenation threshold. We compare the method for segmentation by the SVM with that by a neural network (NN) using the database HANDS-Kondate_t_bf-2001-11 and show the result that the method by the SVM bring about a better segmentation rate and character recognition rate.

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Auto-calibration for structure and motion recovery can be used for match move where the goal is to insert synthetic 3D objects into real scenes and create views as if they were part of the real scene. However, most auto-calibration methods for multi-views utilize bundle adjustment with non-linear optimization, which requires a very good starting approximation. We propose a novel key-frame selection measurement and LMedS (Least Median of Square)-based approach to estimate scene structure and motion from image sequences captured with a hand-held camera. First, we select key-frames considering the ratio of number of correspondences and feature points, the homography error and the distribution of corresponding points in the image. Then, by using LMedS, we reject erroneous frames among the key-frames in absolute quadric estimation. Simulation results demonstrated that the proposed method can select suitable key-frames efficiently and achieve more precise camera pose estimation without nonlinear optimization.

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Because of its simplicity and intuitive approach, point-based rendering has been a very popular research area. Recent approaches have focused on hardware-accelerated techniques. By applying a deferred shading scheme, both high-quality images and high-performance rendering have been achieved. However, previous methods showed problems related to depth-based visibility computation. We propose an extended point-based rendering method using a visibility map. In our method we employ a distance-based visibility technique (replacing depth-based visibility), an averaged position map and an adaptive fragment processing scheme, resulting in more accurate and improved image quality, as well as improved rendering performance.

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Image Registration can be seen as an optimization problem to find a cost function and then use an optimization method to get its minimum. Normalized mutual information is a widely-used robust method to design a cost function in medical image registration. Its calculation is based on the joint histogram of the fixed and transformed moving images. Usually, only a discrete joint histogram is considered in the calculation of normalized mutual information. The discrete joint histogram does not allow the cost function to be explicitly differentiated, so it can only use non-gradient based optimization methods, such as Powell's method, to seek the minimum. In this paper, a parzen-window based method is proposed to estimate the continuous joint histogram in order to make it possible to derive the close form solution for the derivative of the cost function. With this help, we successfully apply the gradient-based optimization method in registration. We also design a new kernel for the parzen-window based method. Our designed kernel is a second order polynomial kernel with the width of two. Because of good theoretical characteristics, this kernel works better than other kernels, such as a cubic B-spline kernel and a first order B-spline kernel, which are widely used in the parzen-window based estimation. Both rigid and non-rigid registration experiments are done to show improved behavior of our designed kernel. Additionally, the proposed method is successfully applied to a clinical CT-MR non-rigid registration which is able to assist a magnetic resonance (MR) guided microwave thermocoagulation of liver tumors.

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We propose a query language based on extended regular expressions. This language extends texts with text-generating macros. These macros make it possible to define languages in a compressed, elegant way. This paper also extends queries with linear implications and additive (classical) conjunctions. To be precise, it allows goals of the form D _??_ G and G₁ & G₂ where D is a text or a macro and G is a query. The first goal is solved by adding D to the current text and then solving G. This goal is flexible in controlling the current text dynamically. The second goal is solved by solving both G₁ and G₂ from the current text. This goal is particularly useful for internet search.

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Self embedding watermarking is a technique used for tamper detection, localization and recovery. This letter proposes a novel self embedding scheme, in which the halftone version of the host image is exploited as a watermark, instead of a JPEG-compressed version used in most existing methods. Our scheme employs a pixel-wise permuted and embedded mechanism and thus overcomes some common drawbacks of the previous methods. Experimental results demonstrate our technique is effective and practical.

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Shot transition detection is a core technology in video browsing, indexing systems and information retrieval. In this paper we propose a dissolve detection algorithm using the characteristics of edge in MPEG compressed video. Using the intensity change information of edge pixels obtained by Sobel edge detector, we detect the location of a dissolve and its precise duration. We also present a new reliable method to eliminate the false dissolves. The proposed algorithm is tested in various types of videos, and the experimental results show that the proposed algorithm is effective and robust.

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