The online buffer management problem formulates the problem of queueing policies of network switches supporting QoS (Quality of Service) guarantee. For this problem, several models are considered.In this paper, we focus on shared memory switches with preemption. We prove that the competitive ratio of the Longest Queue Drop (LQD) policy is (4M-4)/(3M-2) in the case of N=2, where N is the number of output ports in a switch and M is the size of the buffer.This matches the lower bound given by Hahne, Kesselman and Mansour.Also, in the case of arbitrary N, we improve the competitive ratio of LQD from 2 to 2 - (1/M) min_{K = 1, 2, ..., N}{M/K + K - 1}.

An advanced communication system, the FlexRay system, has been developed for future automotive applications. It consists of time-triggered clusters, such as drive-by-wire in cars, in order to meet different requirements and constraints between various sensors, processors, and actuators. In this paper, an approach to static scheduling for FlexRay systems is proposed. Our experimental results show that the proposed scheduling method significantly reduces up to 36.3% of the network traffic compared with a past approach.

Recently, for more efficient filtering of XML data, YFilter system has been suggested to exploit the prefix commonalities that exist among path expressions. Sharing the prefix commonality gives the benefit of improving filtering performance through the tremendous reduction in filtering machine size. However, exploiting the postfix commonality can also be useful for an XML filtering situation. For example, when a stream of XML messages does not have any defined schema, or users cannot remember the defined schema exactly, users often use the partial matching path queries which begins with the descendant axis ("//"), e.g., '//science/article/title', '//entertainment/article/title', and '//title'. If so, the registered XPath queries are most likely to have the postfix commonality, e.g., the sample queries share the partial path expressions 'article/title' and 'title'. Therefore, in this paper, we introduce a bottom-up filtering approach exploiting the postfix commonality against the top-down approach of YFilter exploiting the prefix commonality. Some experimental results show that our method has better filtering performance when registered XPath queries mainly consist of the partial matching path queries with the postfix commonality.

Testing is a critical stage in integrated circuits production in order to guarantee reliability. The complexity and high integration level of mixed-signal ICs has put forward new challenges to circuit testing. This paper describes an oscillation-based combined self-test strategy for the analog portion and analog-to-digital converters (ADCs) in integrated mixed-signal circuits. In test mode, the analog portion under test is reconfigured into an oscillator, generating periodic signals as the test stimulus of ADC. By analyzing the A/D conversion results, a histogram test of ADC can be performed, and the oscillation frequency as well as amplitude can be checked, and in this way the oscillation test of the analog portion is realized simultaneously. For an analog benchmark circuit combined with an ADC, triangle oscillation and sinusoid oscillation schemes are both given to test their faults. Experimental results show that fault coverage of the analog portion is 92.2% and 94.3% in the two schemes respectively, and faults in the ADC can also be tested.

Introducing multi-agent (MA) technology into a SCADA (Supervisory Control and Data Acquisition) system can improve the serviceability and enhance maintenance-free operation with the inter-terminal parameter (ITP) method. In addition, the system's distributed intelligent field terminals (IFTs) use a common algorithm that is unaffected by any changes to the system specifications. As a result of these innovations, the proposed system has much better serviceability because it is much easier to make modifications compared to that of conventional systems. This system has been implemented for practical purposes at over 60 sites.

Face recognition is one of the most active research areas in pattern recognition, not only because the face is a human biometric characteristics of human being but also because there are many potential applications of the face recognition which range from human-computer interactions to authentication, security, and surveillance. This paper presents an approach to pose invariant human face image recognition. The proposed scheme is based on the analysis of discrete cosine transforms (DCT) and discrete wavelet transforms (DWT) of face images. From both the DCT and DWT domain coefficients, which describe the facial information, we build compact and meaningful features vector, using simple statistical measures and quantization. This feature vector is called as the hybrid dominant frequency features. Then, we apply a combination of the L₂ and L_q metric to classify the hybrid dominant frequency features to a person's class. The aim of the proposed system is to overcome the high memory space requirement, the high computational load, and the retraining problems of previous methods. The proposed system is tested using several face databases and the experimental results are compared to a well-known Eigenface method. The proposed method shows good performance, robustness, stability, and accuracy without requiring geometrical normalization. Furthermore, the purposed method has low computational cost, requires little memory space, and can overcome retraining problem.

In this paper, we propose a novel classifier-based object tracker. Our tracker is the combination of Rectangle Feature (RF) based detector [17],[18] and optical-flow based tracking method [1]. We show that the gradient of extended RFs can be calculated rapidly by using Integral Image method. The proposed tracker was tested on real video sequences. We applied our tracker for face tracking and car tracking experiments. Our tracker worked over 100 fps while maintaining comparable accuracy to RF based detector. Our tracking routine that does not contain image I/O processing can be performed about 500 to 2,500 fps with sufficient tracking accuracy.

Recently, many application systems have been developed by using a large number of cameras. If 3D points are observed from synchronized cameras, the multiple view geometry of these cameras can be computed and the 3D reconstruction of the scene is available. Thus, the synchronization of multiple cameras is essential. In this paper, we propose a method for synchronizing multiple cameras and for computing the epipolar geometry from uncalibrated and unsynchronized cameras. In particular we using affine invariance to match the frame numbers of camera images for finding the synchronization. The proposed method is tested by using real image sequences taken from uncalibrated and unsynchronized cameras.

In this paper, we introduce a method for recognizing a subject complex object in real world environment. We use a three dimensional model described by line segments of the object and the data provided by a three-axis orientation sensor attached to the video camera. We assume that existing methods for finding line features in the image allow at least one model line segment to be detected as a single continuous segment. The method consists of two main steps: generation of pose hypotheses and then evaluation of each pose in order to select the most appropriate one. The first stage is three-fold: model visibility, line matching and pose estimation; the second stage aims to rank the poses by evaluating the similarity between the projected model lines and the image lines. Furthermore, we propose an additional step that consists of refining the best candidate pose by using the Lie group formalism of spatial rigid motions. Such a formalism provides an efficient local parameterization of the set of rigid rotation via the exponential map. A set of experiments demonstrating the robustness of this approach is presented.

A simple and successful design of initial codebook of vector quantization (VQ) is presented. For existing initial codebook algorithms, such as random method, the initial codebook is strongly influenced by selection of initial codewords and difficult to match with the features of the training vectors. In the proposed method, training vectors are sorted according to the norm of training vectors. Then, the ordered vectors are partitioned into N groups where N is the size of codebook. The initial codewords are obtained from calculating the centroid of each group. This initializtion method has a robust performance and can be combined with the VQ algorithm to further improve the quality of codebook.

The shellcode use of the polymorphic form has become active as the de facto method for avoiding signature based network security system. We present a new static analysis method for detecting the decryption routine of the polymorphic shellcode. This method traces the processes by which the decryption routine stores the current program counter in a stack, moves the value between registers and uses the value in order to make the address of the encrypted code accessible. Most of decryption routines have the feature which they use the program counter stored on a stack as the address for accessing the memory that the encrypted code is positioned.

A neighbor-aided authentication watermarking based on a chaotic system with feedback is proposed in this paper. This algorithm can not only detect malicious manipulations but reveal block substitutions when the VQ attack occurs. An image is partitioned into non-overlapped blocks. The pixels in one block and its neighboring block are combined to produce an authentication watermark based on a chaotic system with feedback, which is sensitive to the initial value. The produced watermark is embedded into the current block. During detection, the detector extracts the watermark firstly, then generates a reference sequence and compares it with the extracted watermark to authenticate the integrity of the image and locate the tampered regions. Experimental results prove the effectiveness of our method.

In this letter, we propose a new histogram equalization method to compensate for acoustic mismatches mainly caused by corruption of additive noise and channel distortion in speech recognition. The proposed method employs an improved test cumulative distribution function (CDF) by more accurately smoothing the conventional order statistics-based test CDF with the use of window functions for robust feature compensation. Experiments on the AURORA 2 framework confirmed that the proposed method is effective in compensating speech recognition features by reducing the averaged relative error by 13.12% over the order statistics-based conventional histogram equalization method and by 58.02% over the mel-cepstral-based features for the three test sets.

The manifold-ranking algorithm has been successfully adopted in content-based image retrieval (CBIR) in recent years. However, while the global low-level features are widely utilized in current systems, region-based features have received little attention. In this paper, a novel attention-driven transductive framework based on a hierarchical graph representation is proposed for region-based image retrieval (RBIR). This approach can be characterized by two key properties: (1) Since the issue about region significance is the key problem in region-based retrieval, a visual attention model is chosen here to measure the regions' significance. (2) A hierarchical graph representation which combines region-level with image-level similarities is utilized for the manifold-ranking method. A novel propagation energy function is defined which takes both low-level visual features and regional significance into consideration. Experimental results demonstrate that the proposed approach shows the satisfactory retrieval performance compared to the global-based and the block-based manifold-ranking methods.

Three features for image classification into natural objects and artifacts are investigated. They are 'line length ratio', 'line direction distribution,' and 'edge coverage'. Among the three, the feature 'line length ratio' shows superior classification accuracy (above 90%) that exceeds the performance of conventional features, according to experimental results in application to digital camera images. As the development of this feature was motivated by the fact that the edge sharpening magnitude in image-quality improvement must be controlled based on the image content, this classification algorithm should be especially suitable for the image-quality improvement applications.

In this letter, a new concept of life log retrieval using human brain activities is presented. The non-invasive electroencephalogram (EEG) recording was applied to have P300 evoked potentials during the photo retrieving tasks. Three subjects tried to select the photo images that interest them among nine according to their mental states. It was found that with four times EEG averaging, the performances of target photo selections could reach 90% for two subjects. This concept would be applicable in future to achieve intuitive retrieval of life log with large quantities of data.