This letter proposes a Cell-based Hybrid Index (CHI) for energy conserving k Nearest Neighbor search on air. The proposed CHI provides global knowledge on data distribution for fast decision of the search space and local knowledge for efficient pruning of data items. Simulations show that CHI outperforms the existing indexing schemes in terms of tuning time and energy efficiency. With respect to access time, it outperforms them except the distributed indexing scheme optimized for access time.

It is observed, surprisingly, that existing nearest neighbor search methods in wireless data broadcast may not work effectively on mobile clients with very limited memory space. To resolve this problem, a novel method for nearest neighbor search is introduced in the context of a representative of indexes, the grid-partition index, in wireless data broadcast. In the proposed scheme, a mobile client performs the nearest neighbor search by making a sequential access to index packets according to their broadcast order over a wireless channel. The performance evaluation demonstrates that our approach substantially outperforms limited memory versions of existing methods in terms of access time, while retaining a good energy conservation.

Given a simple graph G with n vertices, m edges and k connected components. The spanning forest problem is to find a spanning tree for each connected component of G. This problem has applications to the electrical power demand problem, computer network design, circuit analysis, etc. An optimal parallel algorithm for finding a spanning tree on the trapezoid graph is given by Bera et al., it takes O(log n) time with O(n/log n) processors on the EREW (Exclusive-Read Exclusive-Write) PRAM. Bera et al.'s algorithm is very simple and elegant. Moreover, it can correctly construct a spanning tree when the graph is connected. However, their algorithm can not accept a disconnected graph as an input. Applying their algorithm to a disconnected graph, Concurrent-Write occurs once for each connected component, thus this can not be achieved on EREW PRAM. In this paper we present an O(log n) time parallel algorithm with O(n/log n) processors for constructing a spanning forest on trapezoid graph G on EREW PRAM even if G is a disconnected graph.

This letter presents a novel robust video watermarking scheme based on space-time interest points. These points correspond to inherent structures of the video so that they can be used as synchronization signals for watermark embedding and extraction. In the proposed scheme, local regions are generated using the space-time interest points, and the watermark is embedded into all the regions by quantization. It is a blind scheme and the watermark can be extracted from any position of the video. Experimental results show that the watermark is invisible and it can robustly survive traditional signal processing attacks and video-oriented attacks.

A blind image restoration for non-linear motion blurs with non-uniform point spread functions based on multiple blurred versions of a same scene is proposed. The restoration is separately considered as identification and deconvolution problems. In the proposed identification process, an identification difficulty is introduced to rank an order of blur identification. A blurred image with the lowest identification difficulty is initially identified by using a single-image-based scheme. Then, other images are identified based on a cross convolution relation between each pair of blurred images. In addition, an iterative feedback scheme is applied to improve the identification results. For the deconvolution process, a spatial adaptive scheme using regional optimal terminating points is modified from a conventional iterative deconvolution scheme. The images are decomposed into sub-regions based on smoothness. The regional optimal terminating points are independently assigned to suppress a noise in smooth regions and sharpen the image in edgy regions. The optimal terminating point for each region is decided by considering a discrepancy error. Restoration examples of simulated and real world blurred images are experimented to demonstrate the performance of the proposed method.

Conventional interest point based matching requires computationally expensive patch preprocessing and is not appropriate for recognition of plain objects with negligible detail. This paper presents a method for extracting distinctive interest regions from images that can be used to perform reliable matching between different views of plain objects or scene. We formulate the correspondence problem in a Naive Bayesian classification framework and a simple correlation based matching, which makes our system fast, simple, efficient, and robust. To facilitate the matching using a very small number of interest regions, we also propose a method to reduce the search area inside a test scene. Using this method, it is possible to robustly identify objects among clutter and occlusion while achieving near real-time performance. Our system performs remarkably well on plain objects where some state-of-the art methods fail. Since our system is particularly suitable for the recognition of plain object, we refer to it as Simple Plane Object Recognizer (SPOR).

A deterministic pushdown automaton (dpda) having just one stack symbol is called a deterministic restricted one-counter automaton (droca). When it accepts an input by empty stack, it is called strict. This paper is concerned with a subclass of real-time strict droca's, called Szilard strict droca's, and studies the problem of identifying the subclass in the limit from positive data. The class of languages accepted by Szilard strict droca's coincides with the class of Szilard languages (or, associated languages) of strict droca's and is incomparable to each of the class of regular languages and that of simple languages. After providing some properties of languages accepted by Szilard strict droca's, we show that the class of Szilard strict droca's is polynomial time identifiable in the limit from positive data in the sense of Yokomori. This identifiability is proved by giving an exact characteristic sample of polynomial size for a language accepted by a Szilard strict droca. The class of very simple languages, which is a proper subclass of simple languages, is also proved to be polynomial time identifiable in the limit from positive data by Yokomori, but it is yet unknown whether there exists a characteristic sample of polynomial size for any very simple language.

A motion blur identification scheme is proposed for non-linear uniform motion blurs approximated by piecewise linear models which consist of more than one linear motion component. The proposed scheme includes three modules that are a motion direction estimator, a motion length estimator and a motion combination selector. In order to identify the motion directions, the proposed scheme is based on a trial restoration by using directional forward ramp motion blurs along different directions and an analysis of directional information via frequency domain by using a Radon transform. Autocorrelation functions of image derivatives along several directions are employed for estimation of the motion lengths. A proper motion combination is identified by analyzing local autocorrelation functions of non-flat component of trial restored results. Experimental examples of simulated and real world blurred images are given to demonstrate a promising performance of the proposed scheme.

This paper presents discussion about channel fluctuation on channel estimation in digital terrestrial television broadcasting. This channel estimation uses a two-dimensional (2D) filter. In our previous work, only a structure of a lattice is considered for generation of nonrectangular 2D filter. We investigate generation of nonrectangular 2D filter with adaptive method, because we should refer to not only a lattice but also channel conditions. From the computer simulations, we show that bit error rate of the proposed filter is improved compared to that of the filter depending on only lattices.

Previous approaches for modeling Intrusion Detection System (IDS) have been on twofold: improving detection model(s) in terms of (i) feature selection of audit data through wrapper and filter methods and (ii) parameters optimization of detection model design, based on classification, clustering algorithms, etc. In this paper, we present three approaches to model IDS in the context of feature selection and parameters optimization: First, we present Fusion of Genetic Algorithm (GA) and Support Vector Machines (SVM) (FuGAS), which employs combinations of GA and SVM through genetic operation and it is capable of building an optimal detection model with only selected important features and optimal parameters value. Second, we present Correlation-based Hybrid Feature Selection (CoHyFS), which utilizes a filter method in conjunction of GA for feature selection in order to reduce long training time. Third, we present Simultaneous Intrinsic Model Identification (SIMI), which adopts Random Forest (RF) and shows better intrusion detection rates and feature selection results, along with no additional computational overheads. We show the experimental results and analysis of three approaches on KDD 1999 intrusion detection datasets.

Error concealment at a decoder is an efficient method to reduce the degradation of visual quality caused by channel errors. In this paper, we propose a novel spatio-temporal error concealment algorithm based on the spatial-temporal fading (STF) scheme which has been recently introduced. Although STF achieves good performance for the error concealment, several drawbacks including blurring still remain in the concealed blocks. To alleviate these drawbacks, in the proposed method, hybrid approaches with adaptive weights are proposed. First, the boundary matching algorithm and the decoder motion vector estimation which are well-known temporal error concealment methods are adaptively combined to compensate for the defect of each other. Then, an edge preserved method is utilized to reduce the blurring effects caused by the bilinear interpolation for spatial error concealment. Finally, two concealed results obtained by the hybrid spatial and temporal error concealment are pixel-wisely blended with adaptive weights. Experimental results exhibit that the proposed method outperforms conventional methods including STF in terms of the PSNR performance as well as subjective visual quality, and the computational complexity of the proposed method is similar to that of STF.

Though machine vision systems for automatically detecting visual defects, called mura, have been developed for thin flat transistor liquid crystal display (TFT-LCD) panels, they have not yet reached a level of reliability which can replace human inspectors. To establish an objective criterion for identifying real defects, some index functions for quantifying defect levels based on human perception have been recently researched. However, while these functions have been verified in the laboratory, further consideration is needed in order to apply them to real systems in the field. To begin with, we should correct the distortion occurring through the capturing of panels. Distortion can cause the defect level in the observed image to differ from that in the panel. There are several known methods to restore the observed image in general vision systems. However, TFT-LCD panel images have a unique background degradation composed of background non-uniformity and vignetting effect which cannot easily be restored through traditional methods. Therefore, in this paper we present a new method to correct background degradation of TFT-LCD panel images using principal component analysis (PCA). Experimental results show that our method properly restores the given observed images and the transformed shape of muras closely approaches the original undistorted shape.

We explain how network failures were caused by a natural disaster, describe the restoration steps that were taken, and present lessons learned from the recovery. At 21:26 on December 26th (UTC+9), 2006, there was a serious undersea earthquake off the coast of Taiwan, which measured 7.1 on the Richter scale. This earthquake caused significant damage to submarine cable systems. The resulting fiber cable failures shut down communications in several countries in the Asia Pacific networks. In the first post-earthquake recovery step, BGP routers detoured traffic along redundant backup paths, which provided poor quality connection. Subsequently, operators engineered traffic to improve the quality of recovered communication. To avoid filling narrow-bandwidth links with detoured traffic, the operators had to change the BGP routing policy. Despite the routing-level first aid, a few institutions could not be directly connected to the R&E network community because they had only a single link to the network. For these single-link networks, the commodity link was temporarily used for connectivity. Then, cable connection configurations at the switches were changed to provide high bandwidth and next-generation Internet service. From the whole restoration procedure, we learned that redundant BGP routing information is useful for recovering connectivity but not for providing available bandwidth for the re-routed traffic load and that collaboration between operators is valuable in solving traffic engineering issues such as poor-quality re-routing and lost connections of single-link networks.

We consider the blind recovery problem such that images embedded with side information are given, and we want to obtain the side information under some prescribed constraints. In this case, the system equation becomes y=Ax+b where in addition to the unknown A and x, b also is an unknown quantity and but clearly not a noise component. We assume that several images with the same embedding side information are given, and the image processing to b is described as the perturbation of A. We formulate the optimization function to obtain A, b and x, under the constraint of some finite brightness levels i.e. finite alphabets.

The objective of this paper is to survey the Generalized Multi-Protocol Label Switching (GMPLS) based recovery technology for optical transport networks. This paper introduces standardization activities of the GMPLS based recovery technology in the Internet Engineering Task Force (IETF), and recent progress of related experiments. In addition, this paper extracts requirements for the GMPLS based recovery technology through the evaluation of existing network elements, which can be client nodes of the optical transport networks. The results of field evaluations on the GMPLS based recovery technology are also introduced in this paper. Then, this paper addresses the issues for future deployment of the GMPLS based recovery technology for the optical transport networks.

Existing methods for inverse motion compensation (IMC) in the DCT domain have not considered the unrestricted motion vector (UMV). In the existing methods, IMC is performed to deal with the UMV in the spatial domain after the inverse DCT (IDCT). We propose an IMC method which can deal with the UMV directly in the DCT domain without the use of the IDCT/DCT required by the existing methods. The computational complexity of the proposed method can be reduced by about half of that of the brute-force method operating in the spatial domain. Experimental results show that the proposed method can efficiently reduce the processing time with similar visual quality.

We propose a recursive clustering and order restriction (R-CORE) method for learning large-scale Bayesian networks. The proposed method considers a reduced search space for directed acyclic graph (DAG) structures in scoring-based Bayesian network learning. The candidate DAG structures are restricted by clustering variables and determining the intercluster directionality. The proposed method considers cycles on only cmax(«n) variables rather than on all n variables for DAG structures. The R-CORE method could be a useful tool in very large problems where only a very small amount of training data is available.

The realization of k-nearest-matches search capability in fully-parallel mixed digital-analog associative memories by a sequential autonomous search mode is reported. The proposed concept and circuit implementation can be applied with all types of distance measures such as Hamming, Manhattan or Euclidean distance search, and the k value can be freely selected during operation. A test chip for concept verification has been designed in 0.35 µm CMOS technology with two-poly, three-metal layers, realizes k-nearest-matches Euclidean distance search and consumes 5.12 mm2 of the chip area for 64 reference patterns each with 16 units of 5-bit.

In this paper we propose a novel method to inpaint highlights and to remove the specularity in the image with specular objects by the color line projection. Color line projection is the method that a color with a surface reflection component is projected near the diffuse color line by following the direction of the specular color line. We use two captured images using different exposure time so that the clue of the original color in a highlight area is searched from two images since the color at the highlight region is distorted and saturated to the illumination color. In the first step of the proposed procedure, the region corresponding to the highlight is generated and the clue of the original highlight color is acquired. In the next step, the color line is generated by the restricted region growing method around the highlight region, and the color line is divided into the diffuse color line and the specular color line. In the final step, pixels near the specular color line are projected onto near the diffuse color line by the color line projection, in which the modified random function is applied to realistically inpaint the highlight. One of advantages in our method is to find the highlight region and the clue of the original color of the highlight with ease. It also efficiently estimates the surface reflection component which is utilized to remove specularity and to inpaint the highlight. The proposed method performs the highlight inpainting and the specular removal simultaneously once the color line is generated. In addition, color line projection with the modified random function can make the result more realistic. We show experimental results from the real images and make a synthesis of the real image and the image modified by the proposed method.

The estimation of the point-spread function (PSF) is one of very important and indispensable tasks for the practical image restoration. Especially, for the motion blur, various PSF estimation algorithms have been developed so far. However, a majority of them becomes useless in the low blurred signal-to-noise ratio (BSNR) environment. This paper describes a new robust PSF estimation algorithm based on Hough transform concerning gradient vectors, which can accurately and robustly estimate the motion blur PSF even in low BSNR case. The effectiveness and validity of the proposed algorithm are verified by applying it to the PSF estimation and the image restoration for noisy and motion blurred images.